{"as_of":"2026-08-19T10:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd4afb8652f8f4bf9da98c32e365ce29f42cf154ae198e7ebde6ba79416b72df","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:55:14.812626Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2504.14306/citation-record","integrity":"/paper/2504.14306/integrity","json":"/paper/2504.14306/citation-record.json","paper":"/paper/2504.14306"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.322117Z","title":"A review of change detection in multitemporal hyperspectral images: Current techniques, applications, and challenges,","venue":null,"work_id":"4e4b609b-e4aa-4dac-94af-da33f39f2235","year":2019},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.377302Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d0edb23ae41d5a700f7b59fd0165d33c675acd5fc3663cd6687d206f210c698d","observation_id":"badb3ccb-a33a-4981-af6d-fe148ae305c5","resolution":{"observed_at":"2026-08-16T11:55:16.328095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.302375Z","title":"An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images,","venue":null,"work_id":"eaa1e515-2e6f-499e-965b-948afeabf88e","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.383091Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:2c458b2ec68917129434729caf87cd1b557e4249a6878af657b4cef98887e9c0","observation_id":"67464175-1aa8-4af2-bbd4-cbcfc4ebaa4c","resolution":{"observed_at":"2026-08-16T11:55:16.309099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.283495Z","title":"Deep learning-based change detection in remote sensing images: A review,","venue":null,"work_id":"e20583c2-965f-4936-876a-facf2ab41069","year":2022},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.388088Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:b3cff07a713ef70cbf454088ec7fcd5e88e501533461cb8de4f03daad841779a","observation_id":"600f853d-0f16-492b-b2ad-f9a9a0ac24e0","resolution":{"observed_at":"2026-08-16T11:55:16.289143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.261256Z","title":"A review of data augmentation methods of remote sensing image target recognition,","venue":null,"work_id":"8e1b6cf9-668a-45b1-89c8-77a28d69894d","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.393958Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:8d844ec7136170b590a3235266e6904731c52d0b321eba4f277754c3051efbbf","observation_id":"e6d33c0d-43e2-4f75-ba69-ed91b6f2713c","resolution":{"observed_at":"2026-08-16T11:55:16.267431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.240255Z","title":"Fourier domain structural relationship analysis for unsupervised multimodal change de- tection,","venue":null,"work_id":"4d74f29d-c0c2-4120-bd50-6a7d6fe040e4","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.399482Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:f061e9d435f6ab571cf229509e3492b6918e51a28170a594a3c27708d6563479","observation_id":"d4a840ef-0586-4a95-bd8a-7fe033f2e9bb","resolution":{"observed_at":"2026-08-16T11:55:16.247638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.220265Z","title":"Self-supervised pre- training via multimodality images with transformer for change detection,","venue":null,"work_id":"aeb0853a-cbb4-46c0-9e8c-e28ce7a52664","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.405319Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:ce042fd35ea1a5697c47e17ee76de4c136fde01aad005c6ff275b4ca53f79dd6","observation_id":"e7df2b8e-4faa-4417-ac7a-4891bd3fd250","resolution":{"observed_at":"2026-08-16T11:55:16.225943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.198251Z","title":"Ssl4eo-s12: A large-scale multimodal, multi- temporal dataset for self-supervised learning in earth observa- tion [software and data sets],","venue":null,"work_id":"2063ff01-5a30-42bd-a5be-ce35325407eb","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.412724Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:4358473c00f9b99f70687fd03bcc656cf9dd1a7f62688dd4ff5761130f4b41f5","observation_id":"6e7f6aea-7f00-4587-a89f-81bb8977cc4e","resolution":{"observed_at":"2026-08-16T11:55:16.203676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.176488Z","title":"Exploring fine- grained image-text alignment for referring remote sensing image segmentation,","venue":null,"work_id":"1a96525c-c7e4-470e-add0-e51e23b3e655","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.421808Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:67547a4e5edf5708fc07f211b21a6521db33e04ea3626052fec60eb59d15642f","observation_id":"46227682-796c-45e6-a67f-136095c07865","resolution":{"observed_at":"2026-08-16T11:55:16.182814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.155484Z","title":"Ss-mae: Spa- tial–spectral masked autoencoder for multisource remote sens- ing image classification,","venue":null,"work_id":"a90ff195-afc4-4527-92f9-5b014142f7eb","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.427710Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d9c4505a5c2b3ca22faf9081f66d8022ec129840627c3b89e4e5371d8082cd26","observation_id":"06d924c6-18d6-46ff-97ec-a833ef1d797f","resolution":{"observed_at":"2026-08-16T11:55:16.161787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.133362Z","title":"Snunet-cd: A densely connected siamese network for change detection of vhr images,","venue":null,"work_id":"7db2a25f-bc2e-46d1-88d2-410874b53683","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.433215Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:606641770d22220ce1fe11491bc10ac30c0e827c66ef24df058aaf671002dc40","observation_id":"17603593-6421-45d7-985b-28105f1013be","resolution":{"observed_at":"2026-08-16T11:55:16.139030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.116421Z","title":"A feature difference convolutional neural network-based change detection method,","venue":null,"work_id":"6e246f12-9e1b-41dd-b9d8-27125904683f","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.440665Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:e8ae54adfa4f20146be1a940ce44d301d639177d4f1520302959de04981493fd","observation_id":"413ddbae-21c8-4957-9990-62f087da75c9","resolution":{"observed_at":"2026-08-16T11:55:16.121995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.096266Z","title":"An attention- based multiscale transformer network for remote sensing image change detection,","venue":null,"work_id":"fd58ed17-8389-4437-a10a-957ad386451b","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.446328Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:c9696b8e820d09b61244170f3f7c763f4170eb009aa6ac0d205c84da29e4d330","observation_id":"abdf57f3-c259-4b1a-bad5-09474cc51979","resolution":{"observed_at":"2026-08-16T11:55:16.102951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.075995Z","title":"A full-level fused cross-task transfer learning method for building change detection using noise- robust pretrained networks on crowdsourced labels,","venue":null,"work_id":"02b178ff-acd2-4087-a8ea-4cf62a05f15f","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.452065Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:1805b318eb8e448a12dc30593cc642b79015f7d4656cee189137f6582cbede9d","observation_id":"34294095-8f81-4526-bcb6-18d7191e1b45","resolution":{"observed_at":"2026-08-16T11:55:16.082621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.056034Z","title":"Sea ice change detection in sar images based on convolutional-wavelet neural networks,","venue":null,"work_id":"464c0bd7-5c2d-4567-a78c-33d9dac8cb12","year":2019},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.459271Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:9c487f08d7e32831dfdcc82a897494bef627bd28146d2c866742cdd2675f41e3","observation_id":"fb00f7de-9af8-4bef-84e6-86e3d8178219","resolution":{"observed_at":"2026-08-16T11:55:16.062880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.034329Z","title":"Remote sensing image change detection with transformers,","venue":null,"work_id":"4d251db8-e903-4a66-8efb-dfe0db52f504","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.465674Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:c92900856445e404574cec234e6e0274576f9c0c3c8f73d330d4f7ba62392207","observation_id":"0774c434-585e-4079-add4-3a530fe559be","resolution":{"observed_at":"2026-08-16T11:55:16.040827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:16.013671Z","title":"Dam- net: Flood detection from sar imagery using differential atten- tion metric-based vision transformers,","venue":null,"work_id":"8de7019f-b8d3-41e6-bc97-5bd6ef337513","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.477802Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:6b98e1b83e09b9f4d7c2db5cbe51899eafe6661d5227058407974a290152d18a","observation_id":"8a720e61-4297-4149-ab78-42a50aa5a591","resolution":{"observed_at":"2026-08-16T11:55:16.020523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.982725Z","title":"Global and local attention-based transformer for hyperspectral image change detection,","venue":null,"work_id":"68084058-203e-42e7-9831-a686c2b03f10","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.485575Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:ed39b54c0b298609f0cf36f2cfa3b27da7ee4efe6b81c5dcc3dd006a3a6a00dd","observation_id":"8831ff54-5bb5-4f91-bddd-740ea5889951","resolution":{"observed_at":"2026-08-16T11:55:15.988960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.959260Z","title":"Ida-siamnet: Interactive-and dynamic-aware siamese network for building change detection,","venue":null,"work_id":"52f64bb2-4778-441a-a95b-562f86effcab","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.492097Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:a761cf7a9f27bd2da2ea7ddb0810f8c525f0747e7e7c5c4d1fdca50c68c679d2","observation_id":"0a523906-3341-4f6b-9d43-8fbfed84213e","resolution":{"observed_at":"2026-08-16T11:55:15.966364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.932672Z","title":"A novel remote sensing image change detection approach based on multi-level state space model,","venue":null,"work_id":"76595b0d-0c65-4816-b978-a4d5b67a5b5d","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.503763Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:b59b5dd62833ac14324fe70879a1eb34e98c7047add18330009f9cfcb9873fd9","observation_id":"ebb2857e-5d14-4fe9-bd70-74c80150de9e","resolution":{"observed_at":"2026-08-16T11:55:15.940203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.903562Z","title":"Change- mamba: Remote sensing change detection with spatio-temporal state space model,","venue":null,"work_id":"12437a99-9190-4551-8e37-4c14c0ef7dba","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.512112Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:6a925f9245525b2aa633d6787853b75e0181d5acee16eb9e82a040bcb45e87de","observation_id":"9197c346-a141-408f-ba58-ba2c5ca0e730","resolution":{"observed_at":"2026-08-16T11:55:15.909298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04207","last_updated":"2025-05-19T07:03:10Z","snapshot_observed_at":"2026-08-16T13:45:27.898215Z","submitted_at":"2024-06-06T16:04:30Z","title":"CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04207","snapshot_observed_at":"2026-08-16T11:55:14.517492Z","title":"Cd- mamba: Remote sensing image change detection with mamba,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.517492Z"},"links":{"cited_paper":"/paper/2406.04207","citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:48aa5b759a40f1240c37d1fead272bf9af256356d411c242e34a32dafb35e0a7","observation_id":"106f02fb-c9f0-440c-a9f7-562c3a10166d","resolution":{"observed_at":"2026-08-16T11:55:14.517492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.879937Z","title":"Unifying remote sensing change detection via deep probabilistic change models: From principles, models to applications,","venue":null,"work_id":"f6481b44-92e3-4801-a5d2-987e34b06a69","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.523197Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d82e904d3d5f76c2b018a578819c511151999322b3c1adca471ff3015a64aac5","observation_id":"a5ac0235-6c9d-4e58-b231-ff5dcf497110","resolution":{"observed_at":"2026-08-16T11:55:15.888049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06839","last_updated":"2024-07-08T17:05:48Z","snapshot_observed_at":"2026-08-16T13:36:04.634121Z","submitted_at":"2024-07-08T17:05:48Z","title":"A Mamba-based Siamese Network for Remote Sensing Change Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06839","snapshot_observed_at":"2026-08-16T11:55:14.530054Z","title":"A mamba-based siamese network for remote sensing change detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.530054Z"},"links":{"cited_paper":"/paper/2407.06839","citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:5fda2ecb6027b6ffa76797e17e6fc187ddff89dc524e81fadc175b89e678724e","observation_id":"48c0a13b-e447-4850-841e-9faec10fd129","resolution":{"observed_at":"2026-08-16T11:55:14.530054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.857466Z","title":"Dc- mamba: A novel network for enhanced remote sensing change detection in difficult cases,","venue":null,"work_id":"6b4769a0-3e52-42e8-a6c1-3ced4b2c2adc","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.536147Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:0dcd22c1ee3ca59cd8ce376850ca7fed67c523c64654f1886448d3718cee9841","observation_id":"f43eab4e-3c7b-4d73-a202-708b480f610c","resolution":{"observed_at":"2026-08-16T11:55:15.864771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.834512Z","title":"A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,","venue":null,"work_id":"f0e6a5bd-ddde-450f-a192-894c871fa336","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.541975Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:23673fa7eef954843f0d617d6a37a3fe94c232b2a2682dace2e8f5b142a19a70","observation_id":"95686c85-fb67-4f47-a0a7-07995d993859","resolution":{"observed_at":"2026-08-16T11:55:15.844385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.812942Z","title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,","venue":null,"work_id":"368d419f-2c34-45e3-86a2-7e663514c550","year":2019},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.547355Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:24aa4c530bc37253a4bc47d4406caaa141a64eab5ac3811bff2fb3f28917ce66","observation_id":"4b42afe1-8936-4c51-8ce9-763b7800765d","resolution":{"observed_at":"2026-08-16T11:55:15.818308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.791413Z","title":"Change detection in remote sensing images using conditional adversarial networks,","venue":null,"work_id":"cce74348-ca73-4c14-9d53-359a43d9c7e2","year":2018},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.552644Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:84a9136093787c62d8571415da7bf889408c9833853fbfbf3f1cc653a0ae9a6f","observation_id":"3942eb57-1d5d-448a-83fa-37eb76e8b4be","resolution":{"observed_at":"2026-08-16T11:55:15.797235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.772781Z","title":"Loftr: Detector-free local feature matching with transformers,","venue":null,"work_id":"23765a79-f72a-4d45-b53e-ba36d831f643","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.558630Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:ec4c3d3dc67e617fe74287f467ff570a769caed600507ec328848c326ff092cd","observation_id":"23e6637d-1c60-4f7a-937b-980a72a06d3c","resolution":{"observed_at":"2026-08-16T11:55:15.778878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.746432Z","title":"Superglue: Learning feature matching with graph neural net- works,","venue":null,"work_id":"c4a1522c-7d1e-45ad-b59b-80e68986efdc","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.565957Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:a39a2174e93f53b61ac00cd7630a68afa7b7e72692784e4570fea36e66f5af12","observation_id":"b820c09b-8853-48d2-8eef-f49817915cec","resolution":{"observed_at":"2026-08-16T11:55:15.753803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.724264Z","title":"Lightglue: Local feature matching at light speed,","venue":null,"work_id":"254a5a7f-7887-486b-93b2-de026c3061d9","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.571516Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:178d991b6d2e1ea1fd4ea4a0d8e2b015fdd42506e9ca6767dcae1af2d11c6948","observation_id":"2775d8c8-ef15-4f56-b851-a8be1472d1e2","resolution":{"observed_at":"2026-08-16T11:55:15.730921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.698931Z","title":"Dkm: Dense kernelized feature matching for geometry esti- mation,","venue":null,"work_id":"bdaaac52-a24a-4e4a-bb7d-a2c69a5e55ab","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.576856Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d8e9ae3974a944f17c27573f98fcb3a3d174e733dfae24b3a51d5b691a01f299","observation_id":"5f81e6cf-cc5f-4b58-924f-3016ef4f1ba4","resolution":{"observed_at":"2026-08-16T11:55:15.709053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.677404Z","title":"Toward distortion- aware change detection in realistic scenarios,","venue":null,"work_id":"67147bbe-2cab-4759-8f3c-4222b5cbc3cc","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.582266Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:8160a18f5d7dec9d70d3d86024628a5aaf92e62f424bbf08a44d07e18ecd82cc","observation_id":"9c87d180-5904-4c67-944f-f65ee063c1bb","resolution":{"observed_at":"2026-08-16T11:55:15.684189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.655700Z","title":"Segment anything,","venue":null,"work_id":"07693f51-6246-40f4-96be-e1c0b4d33bed","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.587477Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:0379cd14b20542de890e5bb24739a97ef72876360d2533beeeb055312b60d071","observation_id":"a7434597-019c-4e9b-8fe5-54c2c1e48bf2","resolution":{"observed_at":"2026-08-16T11:55:15.660912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:14.593311Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.593311Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:8f2ac7a498ef187c5341ef077b34552e29a65164465396e204de526b5bc82411","observation_id":"bf634c7d-fd45-4083-8ae1-e1cc216913ad","resolution":{"observed_at":"2026-08-16T11:55:14.593311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.617850Z","title":"Transformer-based multistage enhancement for remote sensing image super-resolution,","venue":null,"work_id":"93e5a59d-5b22-403e-8431-808e055dfcbc","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.599893Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d9cf4663a5d6972cf21d390a01b9950203ae33d2deebf6d31c5c84d100a1cc55","observation_id":"476f5841-2770-49cb-b839-f2cc098f4a2a","resolution":{"observed_at":"2026-08-16T11:55:15.625770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.595883Z","title":"Spectralgpt: Spectral remote sensing foundation model,","venue":null,"work_id":"7e88e3f8-5d75-436c-bfb3-f43ac25f32a6","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.606506Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:fba4d5ceba3f89c84ae2a0a7a91cfe14dc3a7764592ecada06b671238b3a6a57","observation_id":"2d436fa6-38b4-4cd9-9657-0fd36878c919","resolution":{"observed_at":"2026-08-16T11:55:15.602500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13401","last_updated":"2025-01-12T15:10:26Z","snapshot_observed_at":"2026-08-16T13:16:55.110936Z","submitted_at":"2024-09-20T11:02:18Z","title":"PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13401","snapshot_observed_at":"2026-08-16T11:55:14.612309Z","title":"Pointsam: Pointly-supervised segment anything model for remote sensing images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.612309Z"},"links":{"cited_paper":"/paper/2409.13401","citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:7eca23531d73dbad5629d78f515f659eaf42366bef948ac60f0923c70d6c83f1","observation_id":"7798bc3f-06e7-42e1-8135-3128617abd5c","resolution":{"observed_at":"2026-08-16T11:55:14.612309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.575172Z","title":"Sam-assisted remote sensing imagery semantic segmentation with object and boundary constraints,","venue":null,"work_id":"3ca7e430-3483-48b0-be2d-21a3db482599","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.619265Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:0343000a95374bbffa4c6c49a8a143191265540c01026fa28bb3c00a447e33c4","observation_id":"f32f15d1-8372-49b0-81a8-4fc68b8b3993","resolution":{"observed_at":"2026-08-16T11:55:15.580718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.555613Z","title":"Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model,","venue":null,"work_id":"d69fdd5d-4f98-4e46-84b3-a207640a1a39","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.625288Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:592c4443a8826f3db999c7953a23d40f56d2c89ca36206a98c22734535c5125c","observation_id":"06e5c026-ac64-4739-a3a6-95e46e129f45","resolution":{"observed_at":"2026-08-16T11:55:15.562347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.534948Z","title":"Ringmo: A remote sensing foundation model with masked image modeling,","venue":null,"work_id":"cbb0ad54-8178-4efa-9b04-a3cf24eab796","year":2022},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.631353Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:92be98642eeae88fef2499d12dc095519331da705382314e5e997829c7a7348b","observation_id":"323c7ee0-c260-4557-9e7a-837bfe612af7","resolution":{"observed_at":"2026-08-16T11:55:15.541847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.510807Z","title":"Changeclip: Remote sensing change detection with multimodal vision-language rep- resentation learning,","venue":null,"work_id":"365d0d64-2a51-4a3d-a7bd-f212af0907e9","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.636281Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:4ff7c9768892ad5057ad55a6904c32fb89315c767afcf99504dea1834f792702","observation_id":"631814a3-0d6d-410d-a9a1-abeb80cdcfb5","resolution":{"observed_at":"2026-08-16T11:55:15.518454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.487829Z","title":"Geochat: Grounded large vision-language model for remote sensing,","venue":null,"work_id":"d1d24116-847e-49d0-9d0b-092a770a6ab8","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.643041Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:9a3eb8ac6e477dd182cc4b7b24e1353cb4c91ca69b5961dcd2189e017b014e40","observation_id":"0f6c5d7b-ed59-48c0-8953-38fa0268327b","resolution":{"observed_at":"2026-08-16T11:55:15.495630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.465073Z","title":"Distinctive image features from scale-invariant keypoints,","venue":null,"work_id":"7c3f2dc5-7132-47db-a3cb-f6a55222f944","year":2004},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.648689Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:78e51de0286473390357978ccf91ddb174de84695b721d5d5f3db7a5237f79d3","observation_id":"396b94a1-84eb-47e8-9b6d-936cf010602a","resolution":{"observed_at":"2026-08-16T11:55:15.471509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.443470Z","title":"Superpoint: Self-supervised interest point detection and description,","venue":null,"work_id":"91ee2163-d8ad-4b84-842a-498d3c1ad195","year":2018},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.656740Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:5b8ecd1a293cfe25e113c05c2b8f23e3978570ebb433b80077f48940ffe7661b","observation_id":"747dd732-9af0-4b52-88e9-c7788ecf1c41","resolution":{"observed_at":"2026-08-16T11:55:15.451596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.416143Z","title":"Gim: Learning generalizable image matcher from internet videos,","venue":null,"work_id":"13fefe12-900e-492a-b071-6ed2fc46ae3f","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.663892Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:24b88967219e6061e2e5763b97de31a5a52824a9809d2cfbdb1d5f5b914045f6","observation_id":"20acace2-b3a9-4be7-856c-cb7f9d023282","resolution":{"observed_at":"2026-08-16T11:55:15.425086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.386351Z","title":"Geo- metric and non-linear radiometric distortion robust multimodal image matching via exploiting deep feature maps,","venue":null,"work_id":"93b30b03-a867-4092-bf90-b9bd32ea6adc","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.676902Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:2578fe629f74ef1177cd1a185bba2275afc09611cf632fe2e6420dea85430b76","observation_id":"4d6fdc83-8121-40a9-9025-3886238ee7d3","resolution":{"observed_at":"2026-08-16T11:55:15.397178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.363775Z","title":"Robust matching for sar and optical images using multiscale convolutional gra- dient features,","venue":null,"work_id":"e60a9c60-33af-425e-91ef-5cb3385b26f4","year":2022},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.686692Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:a48b58f66fa947e7cb61a083caf70c373a30054ee5f1f5d0e457db407805cb85","observation_id":"1a285692-81d5-4ae5-8686-671b1d8024a9","resolution":{"observed_at":"2026-08-16T11:55:15.372034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.345111Z","title":"R&D-Net: Integration of registration-net and detection-net for identifying building changes in high spatial- resolution remote sensing images,","venue":null,"work_id":"f0bb1f56-4168-4498-8097-67ea787acdba","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.692776Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:d5bdf7bd3571c54cb95b4c431bfccd5774916c2deb3ebf45d2190a62a307e9bc","observation_id":"c83511e7-ec76-421b-8e83-e8b4308500e8","resolution":{"observed_at":"2026-08-16T11:55:15.351687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.326582Z","title":"A unified deep learning network for remote sensing image registration and change detection,","venue":null,"work_id":"981aaf15-30eb-4c6e-9865-95843390cfaf","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.699321Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:8171e8a7bc034ac188493730902c281f207921731b38a61c39504ad622eb10d2","observation_id":"2df960ef-18ba-4c52-a763-242f9ee6bb7d","resolution":{"observed_at":"2026-08-16T11:55:15.333203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.304338Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":"7682c58a-2b93-4e41-91c1-fc61dd02dd5d","year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.704880Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:5de5218c7aa775ee1232104c40c1d4d98cc28c072a294c8d7066a888d07cfb0f","observation_id":"0d4ca3af-e694-4e76-8c54-c56146ff5240","resolution":{"observed_at":"2026-08-16T11:55:15.312953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:14.714242Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.714242Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:50033e1b4510dd5a6c253331f542e9a63cd3e9740e884c5937a7849ad1db60ac","observation_id":"304a6600-8185-4b32-962f-f9a8c2904b91","resolution":{"observed_at":"2026-08-16T11:55:14.714242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.268679Z","title":"Exploring simple siamese representation learning,","venue":null,"work_id":"e7765ffa-8e49-4d81-afec-f1c4196a6272","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.722119Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:e1f0812c5017b1daad8d8a2c1bfaf2e1545588710757aec4c0c4c60a1bdf84dd","observation_id":"3e4bef30-9265-4687-98f4-afdede14b534","resolution":{"observed_at":"2026-08-16T11:55:15.275472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.247682Z","title":"Masked autoencoders are scalable vision learners,","venue":null,"work_id":"16925978-5538-4520-a4d2-bb69e9df9ae6","year":2022},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.728352Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:05171a0e17824e9bb7bca7196b384e4faed6b6d30960e7ac2ffb512dabb445ca","observation_id":"75a35db1-3f0d-4b67-97f3-6f27859a36bd","resolution":{"observed_at":"2026-08-16T11:55:15.254416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.218605Z","title":"Self-supervised global–local contrastive learning for fine- grained change detection in vhr images,","venue":null,"work_id":"753ee310-ff7f-4356-a1e2-32f7924ebec1","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.736796Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:7d644a08fc6eae8bdead9485bb0dc4b6ae8edcafa55fcc7f2c2f6a94377d7c71","observation_id":"438bfd6b-ac0f-4d38-ba34-44227154c18d","resolution":{"observed_at":"2026-08-16T11:55:15.226949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.195679Z","title":"Sslchange: A self-supervised change detection framework based on domain adaptation,","venue":null,"work_id":"241a9a08-fe8c-4780-b62e-350825025adf","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.742908Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:b9bf5f70ed9fedf3477348dd45592206c3a9212f0702dedc919e595049f7345c","observation_id":"9e66ef87-ddcf-4aad-b079-35c76ea1ccf2","resolution":{"observed_at":"2026-08-16T11:55:15.201371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.173041Z","title":"Coud: Continual urbanization detector for time series building change detection,","venue":null,"work_id":"40524552-41cb-46f8-b8ce-b138ca2f80d9","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.749086Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:6075c3c8279a83f1d14cd0d530715a755210b183cc0d4982e8f80d01bc32c3fc","observation_id":"94270261-8fec-4fcd-bdf1-55dee4176f2e","resolution":{"observed_at":"2026-08-16T11:55:15.180812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12156","last_updated":"2023-06-21T10:08:29Z","snapshot_observed_at":"2026-08-16T15:22:17.250833Z","submitted_at":"2023-06-21T10:08:29Z","title":"Fast Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12156","snapshot_observed_at":"2026-08-16T11:55:14.755191Z","title":"Fast segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.755191Z"},"links":{"cited_paper":"/paper/2306.12156","citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:7a42716fc87ce32a936fbe711e9eae58bb45eb0e9642709935b09f6df0e8cbaf","observation_id":"7eb9ab62-4756-461b-9b18-d5a10a0bf060","resolution":{"observed_at":"2026-08-16T11:55:14.755191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.145140Z","title":"Emerging properties in self-supervised vision transformers,","venue":null,"work_id":"7bc6dd9e-f79d-4e9b-943e-761f9e02dc4b","year":2021},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.761683Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:82736031b068b7fb19e1072bed4887b35e503287c22aea46f3b3498d2f88422b","observation_id":"22d344a4-4eb6-4077-8e64-43f90d4396bc","resolution":{"observed_at":"2026-08-16T11:55:15.153381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.114336Z","title":"A computer algorithm for reconstruct- ing a scene from two projections,","venue":null,"work_id":"8b57f591-0152-406f-a3df-0deec8b35ba6","year":1981},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.767642Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:4f9675b5bb7c7085ce9d43773e57f0728e5ed72d534c32a83a90173eeb22971c","observation_id":"1e477ae9-66e6-4634-bf04-22755a3b297b","resolution":{"observed_at":"2026-08-16T11:55:15.125884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:14.773510Z","title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.773510Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:df8c5d2833ade80fae150d259d2036110afe9be67d30f179532ff5568a945857","observation_id":"b2bf909a-e298-49b0-922f-46e58b6f1b9a","resolution":{"observed_at":"2026-08-16T11:55:14.773510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.072738Z","title":"Aid: A benchmark data set for performance evaluation of aerial scene classification,","venue":null,"work_id":"4b33232c-cb7f-473f-8d13-f9aef278e613","year":2017},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.778415Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:72aa1b4f36b21f7d24daf31158b9bfd6c8ce0a7b2e553290605e0e9d331022a4","observation_id":"e92894e8-bf6c-4f45-a7e4-652de33cfc27","resolution":{"observed_at":"2026-08-16T11:55:15.079694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.042030Z","title":"Bag-of-visual-words and spatial extensions for land-use classification,","venue":null,"work_id":"dc1d8970-128a-48df-a7e2-71830d08fa41","year":2010},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.783304Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:bfaed275104c770a54fcb94594fdd5faabdab96f18df3ad78473dd424baff1e2","observation_id":"7d95a0b9-61b2-492c-bb13-6aaf478c4450","resolution":{"observed_at":"2026-08-16T11:55:15.055381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:15.017724Z","title":"Fully convolutional siamese networks for change detection,","venue":null,"work_id":"848878b8-27fd-4735-bac2-1d0961261e10","year":2018},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.794110Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:b84326e41c70957784ae347de1ea165dc8b8f09a3acc1fc01370f0545d05c4c4","observation_id":"56832cdc-9292-4769-9d44-c54ca6065907","resolution":{"observed_at":"2026-08-16T11:55:15.025511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:14.988914Z","title":"Ultralightweight spatial–spectral feature cooperation network for change detection in remote sensing images,","venue":null,"work_id":"50780d3d-4703-4248-989d-37139eb22ef8","year":2023},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.804380Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:ebe34cd5adeb8c517bd15aac33d77ef599d896a11e36b1abe4a25d69f867187c","observation_id":"92e28585-5222-4c50-be3b-dba4b26298e8","resolution":{"observed_at":"2026-08-16T11:55:14.996468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:55:14.963890Z","title":"Spatiotemporal enhance- ment and interlevel fusion network for remote sensing images change detection,","venue":null,"work_id":"84afc9f7-63c1-40ee-ad2b-70783e7169bc","year":2024},"citing_paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:14.812626Z"},"links":{"citing_paper":"/paper/2504.14306"},"observation_digest":"sha256:f584f017e96b446a6b040f0e479198d279018055d8f830c45a07007fdc4e0cf7","observation_id":"7d5ed54b-eaa1-4461-869c-ebcd7ef0aeb3","resolution":{"observed_at":"2026-08-16T11:55:14.972205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.14306","last_updated":"2025-04-19T14:05:39Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T10:32:14.590514Z","submitted_at":"2025-04-19T14:05:39Z","title":"Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":58},"total_outbound_references":65},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2504.14306."}