{"as_of":"2026-08-14T01:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a4aca7e03cbf3c2cfc82f04d3cdbb192306f718d81d677ff7a9a771dc4b33c27","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:16:53.949426Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2501.05265/citation-record","integrity":"/paper/2501.05265/integrity","json":"/paper/2501.05265/citation-record.json","paper":"/paper/2501.05265"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:16:54.265419Z","title":"Thick cloud removal under land cover changes using multisource satellite imagery and a spatiotemporal attention network,","venue":null,"work_id":"59bb5480-b9ff-413d-bc6f-8722db3a4207","year":2023},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.859385Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:4ae8bb58ae45bfa48bcdf8eb7653cde7ff94c5d8cb59a69aefcae0c96377cb77","observation_id":"d92c58f5-0306-4044-880f-4fdb8e6755a4","resolution":{"observed_at":"2026-08-10T21:16:54.270066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.250708Z","title":"A spatiotemporal fusion based cloud removal method for remote sensing images with land cover changes,","venue":null,"work_id":"a64a1bf2-e254-48b2-9afd-9f21322b1432","year":2019},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.864845Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:eabf8a8726e00268ce281e93681fbc18216907cff2fb93feee74abbd0f136253","observation_id":"af921d00-009c-48c5-83f8-5ad109a21cbb","resolution":{"observed_at":"2026-08-10T21:16:54.256072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.234810Z","title":"Joint cloud removal and classification of Sentinel-2 image time series for agricultural land cover mapping in northern Benin,","venue":null,"work_id":"04b7a0fa-4e7a-464e-a62b-d0c377d3adcc","year":2024},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.869641Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:105662382d80784675f08cf55c1a8e62f74de5affa1882f50f7496f7fa025d6d","observation_id":"48cbe8a1-0af9-464a-96c0-68686835fd4b","resolution":{"observed_at":"2026-08-10T21:16:54.240243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.219444Z","title":"An effective thin cloud removal procedure for visible remote sensing im- ages,","venue":null,"work_id":"68e58385-505a-400c-a79b-2a27fb8d4e01","year":2014},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.874229Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:fc5eb11c5b488ba5951a1e6466e1c7a6a293595d5df1ce059398fd57ac8b6afb","observation_id":"22dbe70b-a5bb-4db7-9cf0-f823969c746e","resolution":{"observed_at":"2026-08-10T21:16:54.224746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.205159Z","title":"Masked autoencoders are scalable vision learners,","venue":null,"work_id":"98ce4278-a456-4826-a698-5f0e8971d3e3","year":2022},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.879299Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:79ee3d110b77d008b10c91909e4cb8456f3803319067f91f90e631ee1be541c9","observation_id":"a0d234a9-65a5-40b2-8ef9-0ba448184bf9","resolution":{"observed_at":"2026-08-10T21:16:54.210053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.190395Z","title":"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion,","venue":null,"work_id":"d671bb1a-3abc-4df6-8742-55fea0660dcc","year":2020},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.884207Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:923fd33e72511691da15fa50ba7c95c8eb71977e4df3d6c6155df32f185009ac","observation_id":"bb887c15-5ffa-4459-82b5-8054d6206b6e","resolution":{"observed_at":"2026-08-10T21:16:54.195228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.175686Z","title":"Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition,","venue":null,"work_id":"feca754f-e2aa-4ef0-be33-e54ea4a51566","year":2019},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.889387Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:46d4b11ed5b8ebd6714eb11bf1d6b85f4b8c4509d1866abb75ee86d0e57b1c9d","observation_id":"ee3a8aca-41ed-4755-af12-f9c0c4c5d0fb","resolution":{"observed_at":"2026-08-10T21:16:54.181242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.161417Z","title":"Thick clouds removing from multitemporal Landsat images using spatiotemporal neural networks,","venue":null,"work_id":"a49bb10d-c2cb-437a-9729-cf2bee7e914f","year":2020},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.893785Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:67c9e1dd1e615bd182debe18eecf96bbfdae5fa9cadf85efda95e6157b0c301a","observation_id":"7cc6706a-2051-4609-8d00-3d23ce9c25c9","resolution":{"observed_at":"2026-08-10T21:16:54.166062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.146353Z","title":"Thin cloud removal from single satellite images,","venue":null,"work_id":"be55c3db-25fa-437b-b5b5-a0b85537342a","year":2014},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.898149Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:7c601f1988a0bc15c9e6b669c34a3b6e86e88246e1e89aeef3b9edb2aae1a31b","observation_id":"83863600-165d-4dfa-a98a-33687fbd3c7e","resolution":{"observed_at":"2026-08-10T21:16:54.151685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.131045Z","title":"Cloud removal methodology from modis snow cover product,","venue":null,"work_id":"845b100a-24b2-437f-982c-ea4d0e37d6cb","year":2009},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.902612Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:9614e75576927f733a11d79cfaa701d07db18cbb0333d9f7f66e9253e7e22081","observation_id":"200b4131-d3ce-4305-8470-2cfccdef0bbc","resolution":{"observed_at":"2026-08-10T21:16:54.136031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.116542Z","title":"A modified homomorphism filtering algorithm for cloud removal,","venue":null,"work_id":"49f85941-eea9-420e-9020-2af11d462555","year":2010},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.907531Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:6b801721e04135646d4c99c5daa011548ed30deda87da531ce7c38b9c79d6bd4","observation_id":"6329b80d-2511-4fee-837a-ca61bce9f371","resolution":{"observed_at":"2026-08-10T21:16:54.121157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.101101Z","title":"Cloud-GAN: Cloud removal for Sentinel-2 imagery using a cyclic consistent generative ad- versarial networks,","venue":null,"work_id":"d7ebda5c-6eb3-4d15-b539-e293668efe64","year":2018},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.912076Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:4eb86c0da210d368b96b6543d1081d87ca2d67e740b1d0e0cc6653791cdf4d96","observation_id":"9db89aeb-92fb-4d30-a4a1-3a12916ecc3b","resolution":{"observed_at":"2026-08-10T21:16:54.106353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.085142Z","title":"Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,","venue":null,"work_id":"c9b13483-30e1-46ef-8474-351788fd1228","year":2020},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.916997Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:076042d8a4ca5bb8f1678f0faf9dc01f695d9d6a0477a1ca95b26324484b3368","observation_id":"7e1c8735-2b72-4747-ac45-b32cc6c46199","resolution":{"observed_at":"2026-08-10T21:16:54.090053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-10T21:16:53.921378Z","title":"An image is worth 16 ×16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.921378Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:15b7be8afa142db2bc8e28a030fbd1095cabf7bdc01e8f8a229ceb3684198294","observation_id":"5624bb9c-4bde-43a6-bfbc-d4e9093d1ca5","resolution":{"observed_at":"2026-08-10T21:16:53.921378Z","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":"2026-08-10T21:16:54.070423Z","title":"A survey on deep transfer learning,","venue":null,"work_id":"0968ab9f-d5e4-4502-8eed-014a3a177feb","year":2018},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.926730Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:4dfbcab9ff8d56f3acc79ea7effd680684d2291fba17f62a96f01ca6ddf2a65e","observation_id":"4d4a5950-adce-4a21-a3c8-fcbde850f9c0","resolution":{"observed_at":"2026-08-10T21:16:54.075119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.055786Z","title":"Convolutional neural network-driven improvements in global cloud detection for Landsat 8 and transfer learning on Sentinel-2 imagery,","venue":null,"work_id":"3e21e661-9e38-4a52-888c-c37389d67c39","year":2023},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.931302Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:6af97220856688d16d97f6c981c55a57460f4b81ddac3a246c150978a820d5da","observation_id":"83bfac0a-9f5f-4c40-ba37-2c470ed9c5f1","resolution":{"observed_at":"2026-08-10T21:16:54.060618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-10T21:16:54.040313Z","title":"Cloud removal on satellite image using transfer learning based generative adversar- ial network,","venue":null,"work_id":"faa3a8fb-7277-4c3d-a954-87ed075ef184","year":2020},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.935692Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:dbed69a59bf739e67e93bbbd0090fefec8c1bfb2c0428cc29c48a1ed4938d2cf","observation_id":"084dca49-b6f6-456b-82ad-8ef79b54fa66","resolution":{"observed_at":"2026-08-10T21:16:54.045767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.00600","last_updated":"2019-01-03T03:43:38Z","snapshot_observed_at":"2026-07-06T07:24:39.425472Z","submitted_at":"2019-01-03T03:43:38Z","title":"A Remote Sensing Image Dataset for Cloud Removal","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.00600","snapshot_observed_at":"2026-08-10T21:16:53.940063Z","title":"A remote sensing image dataset for cloud removal,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.940063Z"},"links":{"cited_paper":"/paper/1901.00600","citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:dd61740d1952aef534e74c75f8cabf5ed07c225d50931ced3240920efefdc788","observation_id":"021f2e7b-0fe0-415c-a985-db08bcae0906","resolution":{"observed_at":"2026-08-10T21:16:53.940063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.13015","last_updated":"2020-11-14T08:17:05Z","snapshot_observed_at":"2026-08-13T19:38:23.903244Z","submitted_at":"2020-09-28T02:13:23Z","title":"Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.13015","snapshot_observed_at":"2026-08-10T21:16:53.944857Z","title":"Cloud removal for remote sensing imagery via spa- tial attention generative adversarial network,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.944857Z"},"links":{"cited_paper":"/paper/2009.13015","citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:3d171a7860457e8cfad19067e5d53258bc3e917673746cf3b970407e21d716d1","observation_id":"2b5884b7-cef9-4816-8ece-8747ebc56106","resolution":{"observed_at":"2026-08-10T21:16:53.944857Z","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":"2026-08-10T21:16:54.022235Z","title":"Cascaded memory network for opti- cal remote sensing imagery cloud removal,","venue":null,"work_id":"eb9fbfc8-85ad-4f5a-8a2c-39f09d25246b","year":2024},"citing_paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:16:53.949426Z"},"links":{"citing_paper":"/paper/2501.05265"},"observation_digest":"sha256:2276f83c6727db8ae551609831d7624f71d079cbeb8056f4904c2e619f934f08","observation_id":"cfb726c9-a494-4836-8450-4cf5b997cd0c","resolution":{"observed_at":"2026-08-10T21:16:54.029869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.05265","last_updated":"2025-01-09T14:19:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T21:12:26.650111Z","submitted_at":"2025-01-09T14:19:46Z","title":"Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":20},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2501.05265."}