{"as_of":"2026-08-16T19:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c7b4057a0f517e5ad41265036f2bcf51046606b72871007e2695ee94e353e501","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T10:33:01.207918Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.11080/citation-record","integrity":"/paper/1908.11080/integrity","json":"/paper/1908.11080/citation-record.json","paper":"/paper/1908.11080"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:33:01.704557Z","title":"Support vector machines in remote sensing: A review","venue":null,"work_id":"2764671d-65e5-4b2c-b6d2-3c0c6b8f9fe5","year":2011},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.053881Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:8bbd6945e864a83e64f5abdd91c9dd93ac2906b6bd34d9475e6b027c97cb6e84","observation_id":"35af4643-5714-45d1-b3d5-5e0be7af02cf","resolution":{"observed_at":"2026-08-14T10:33:01.709233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.690353Z","title":"Neural network classification of remote - sensing data","venue":null,"work_id":"12d615d6-40da-4d5b-9c93-d35179fd0bbb","year":1995},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.059308Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:ce8db4c0646f4d41eb62f2dcac0dfaf9d423867f3c9ccb8d7067ff25a91fcd4e","observation_id":"b0fab5e2-e17e-4a69-b252-6c7d6aeba9f3","resolution":{"observed_at":"2026-08-14T10:33:01.694932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.675504Z","title":"Random forest classifier for remote sensing classification","venue":null,"work_id":"5158059f-758f-4d5e-ae71-2ffa3a8bbdb5","year":2005},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.069997Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:e0b63f397e876f76c1436befd49e56f2c23b4fbe91ca95115e8514163fad690c","observation_id":"f2f8d4ec-e008-4ef1-b369-e430cb05a7e4","resolution":{"observed_at":"2026-08-14T10:33:01.680107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.661331Z","title":"Object - b ased image analysis for remote sensing","venue":null,"work_id":"2503f618-fd23-41b3-9744-1eb1da900199","year":2010},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.075387Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:7cac9e35a36914f689c626692a93874ced99855949ced854a73afb2d9c58e545","observation_id":"5acac08e-af34-48ee-94a2-a12c0d789ff6","resolution":{"observed_at":"2026-08-14T10:33:01.666184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.647369Z","title":"Object Recognition from Local gre Scale - Invariant Features","venue":null,"work_id":"fd289ea4-185f-4a37-80c6-5bf5cbb46626","year":1999},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.080360Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:4ef3b3ad4edf3b35619067a88447b38a4ffdb86212ddcb062faac9c7f6fda2fb","observation_id":"b43aaaf2-35bf-45f0-aa6a-556b427dff48","resolution":{"observed_at":"2026-08-14T10:33:01.652136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.633265Z","title":"Histog rams of Oriented Gradients for Human Detection","venue":null,"work_id":"37b283ab-a358-45e7-b5a6-0e03a0415f7d","year":2005},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.085168Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:a32311dba07d2c04da4225810e04a1bba9977be1d75ac319595392b4651c45d6","observation_id":"caf231fd-8756-4f27-9076-f981795ce681","resolution":{"observed_at":"2026-08-14T10:33:01.637869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02611","last_updated":"2018-08-22T20:41:10Z","snapshot_observed_at":"2026-08-15T22:03:00.407650Z","submitted_at":"2018-02-07T19:37:11Z","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02611","snapshot_observed_at":"2026-08-14T10:33:01.090701Z","title":"C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.090701Z"},"links":{"cited_paper":"/paper/1802.02611","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:64ebf6f915fb9c26ac1f118fbfb9ebed3fd980833c05d67d86d1c1cc6ff0cb45","observation_id":"0b209c93-3dcf-4d8d-8b62-010911efdfd1","resolution":{"observed_at":"2026-08-14T10:33:01.090701Z","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":"2026-08-14T10:33:01.617935Z","title":"Holistically - Nested Edge Detection[J]","venue":null,"work_id":"88bd034b-a6f3-4af8-8b6c-eaabe3d70239","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.095772Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:9b8153a3538f3029f1bd1831c101c4882cd1e8ff6c9ee3c0e714ce54cea9ab78","observation_id":"ee250ca5-05c5-4c3c-8b38-60b31808e01f","resolution":{"observed_at":"2026-08-14T10:33:01.622636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.604277Z","title":null,"venue":null,"work_id":"0d2118ea-d515-437e-a15d-a81cd333b121","year":2017},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.100335Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:6c0ae00a434d70177bd6a0ba608219b4e3019c1efc5e8109bca94086129f1082","observation_id":"17d3e570-273d-40f1-93d2-6e9a6eb09d41","resolution":{"observed_at":"2026-08-14T10:33:01.608621Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.590482Z","title":", Ferretti , A","venue":null,"work_id":"56718825-3e7f-494e-8bf3-dd4ffa81a0ac","year":2012},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.104890Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:f27e11e2aa36baf01a93dda2b4e6fad0391bc260ecd24b0f3d5088d78e3051a2","observation_id":"3e057174-cce6-44e1-9f92-40004c7bb9a4","resolution":{"observed_at":"2026-08-14T10:33:01.595014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.576122Z","title":"M onitoring abandoned dreg fields of high - speed railway construction with UAV remote sensing technology","venue":null,"work_id":"06038199-a255-4135-a6b6-09011397c7a0","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.109515Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:9c920eaf57c42a1218e8050ec6d26a3c4898d3464697dd79a12134adbd1606ec","observation_id":"80ebf89b-0ded-4596-ba91-221e850e1055","resolution":{"observed_at":"2026-08-14T10:33:01.580815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.561398Z","title":null,"venue":null,"work_id":"53a176de-d154-4048-bd6f-f1fbd614df56","year":2016},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.114579Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:ae4affbd1719182bcc1b49b0d30e0c242f8966fd4f01a71d6475d8f20df71c46","observation_id":"a08532ba-939d-4a97-a855-1b0575c854c5","resolution":{"observed_at":"2026-08-14T10:33:01.565931Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.546184Z","title":"Automated recognition of railroad infrastructure in r ural areas from lidar data","venue":null,"work_id":"ea87a0c3-becc-4b08-9a0a-7ab4f9bcfdec","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.119422Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:6e7ca3ef37a88e7767f3726b184cbd5c8314c7f4cf0487409c9020596b16d03f","observation_id":"a04182c9-9223-484f-bb6d-7d9a4b761557","resolution":{"observed_at":"2026-08-14T10:33:01.551860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.531844Z","title":"Fully convolutional networks for semantic segmentation","venue":null,"work_id":"27082f18-df1d-46b3-aa2d-5bde28b17b6f","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.123986Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:523b801e8ddf58247112f608997e8251f22bc3f4a530acfa7a07809b9a9ba08b","observation_id":"1cf649f8-b27c-403d-bfb3-1f4b97662bae","resolution":{"observed_at":"2026-08-14T10:33:01.536557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.517319Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"fe89c307-6318-43b0-a97d-29a240f17058","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.128439Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:a26c242a2ea7edd626ec8c4966661ea2692ae64541ca1b99c675888feabcd021","observation_id":"a3a40ac1-73c1-41c5-a28f-8811e5daad5e","resolution":{"observed_at":"2026-08-14T10:33:01.521951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.502834Z","title":"- Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollar, P.; Zitnick, C.L","venue":null,"work_id":"ebac1362-0f27-4728-835d-53cc101299cf","year":2014},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.132922Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:e05f1e26a6aeec76a3af573830ff15401cd4be3025b43b454b8c4150197d18c6","observation_id":"273a34d1-38f3-46e6-ac2e-5e99ff540f32","resolution":{"observed_at":"2026-08-14T10:33:01.507626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.00561","last_updated":"2016-10-10T21:11:59Z","snapshot_observed_at":"2026-08-14T22:25:13.259510Z","submitted_at":"2015-11-02T15:51:03Z","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.00561","snapshot_observed_at":"2026-08-14T10:33:01.137430Z","title":"SegNet: A Deep Convolutional Encoder - Decoder Architecture for Im age Segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.137430Z"},"links":{"cited_paper":"/paper/1511.00561","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:04d12fb466a22ef3adf8ca57aea1b930fcc8c9a68f613d5da6bd1816d52723f8","observation_id":"7000da12-d5d0-4d88-aafe-26560abf173c","resolution":{"observed_at":"2026-08-14T10:33:01.137430Z","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":"2026-08-14T10:33:01.488476Z","title":"U - Net: Convolutional Networks for Biomedical Image Segmentation","venue":null,"work_id":"4a367ffb-443f-4b5d-b225-75a440bbd1af","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.142337Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:295cd4aa43b62183e5a8b627b5f65571ee7e046b501916ff6ff20189f256d712","observation_id":"19b3473c-283a-45f9-ba94-98a3aa5f2aaa","resolution":{"observed_at":"2026-08-14T10:33:01.493107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.06612","last_updated":"2016-11-25T02:01:05Z","snapshot_observed_at":"2026-08-14T21:29:31.399030Z","submitted_at":"2016-11-20T23:39:52Z","title":"RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06612","snapshot_observed_at":"2026-08-14T10:33:01.146944Z","title":"RefineNet: Multi - Path Refinement Networks for High - Resolution Semantic Segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.146944Z"},"links":{"cited_paper":"/paper/1611.06612","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:78d58791ae0535c1a738711e706020bef6e1ccf44a76ea55c5bfe8b3f898ee84","observation_id":"bcce281e-bec0-4f02-acc6-1d93a0caeb49","resolution":{"observed_at":"2026-08-14T10:33:01.146944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03718","last_updated":"2017-06-14T20:37:17Z","snapshot_observed_at":"2026-08-14T20:53:59.817669Z","submitted_at":"2017-06-14T20:37:17Z","title":"LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03718","snapshot_observed_at":"2026-08-14T10:33:01.152245Z","title":"Linknet: exploiting encoder representations for efficient semantic segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.152245Z"},"links":{"cited_paper":"/paper/1707.03718","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:fe580eb71f6cb1f44f841370fd1d51f9677e586aa55de0fdc00686acdfab708a","observation_id":"ce8588fa-de8e-4e5b-b187-8b15ffb82bdc","resolution":{"observed_at":"2026-08-14T10:33:01.152245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.01105","last_updated":"2017-04-27T12:15:17Z","snapshot_observed_at":"2026-08-14T21:27:13.583882Z","submitted_at":"2016-12-04T11:46:22Z","title":"Pyramid Scene Parsing Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.01105","snapshot_observed_at":"2026-08-14T10:33:01.157253Z","title":"Pyramid Scene Parsing Network","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.157253Z"},"links":{"cited_paper":"/paper/1612.01105","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:4de751f80e4cc1f27f62dc438195efee6549e4d91f2e207538d3b66102d9d027","observation_id":"ad494d6d-3115-4814-9704-c308ebfd0776","resolution":{"observed_at":"2026-08-14T10:33:01.157253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.7062","last_updated":"2016-06-07T04:00:08Z","snapshot_observed_at":"2026-08-14T23:06:27.726818Z","submitted_at":"2014-12-22T17:18:33Z","title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.7062","snapshot_observed_at":"2026-08-14T10:33:01.162215Z","title":"- C.; Papandreou, G.; K okkinos, I.; Murphy, K.; Yuille, A.L","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.162215Z"},"links":{"cited_paper":"/paper/1412.7062","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:fa61d43ec2299ed01a7e221d5269c738a055729c6c824c94e3d857b2ace8efb5","observation_id":"66585673-5957-49ce-bb2a-aa545e5af8f1","resolution":{"observed_at":"2026-08-14T10:33:01.162215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.00915","last_updated":"2017-05-12T03:25:47Z","snapshot_observed_at":"2026-08-14T21:54:25.974440Z","submitted_at":"2016-06-02T21:52:21Z","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.00915","snapshot_observed_at":"2026-08-14T10:33:01.167152Z","title":"- C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.167152Z"},"links":{"cited_paper":"/paper/1606.00915","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:67d92e0988f1bc9a573b55f895f03070defc420bc0983a398cc2180ee1b8a4ec","observation_id":"69f695d3-de6a-4dba-bbff-230c697714ed","resolution":{"observed_at":"2026-08-14T10:33:01.167152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-16T09:04:18.586776Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-14T10:33:01.171810Z","title":"C., Papandreou, G., Schroff, F., A dam, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.171810Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:7d985735fa57908a6a9c343158224c6bd262512461a2267afd51cb5a3c84cb85","observation_id":"b3549869-5971-475f-aa22-718bd02b4db7","resolution":{"observed_at":"2026-08-14T10:33:01.171810Z","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":"2026-08-14T10:33:01.473850Z","title":"Dense semantic labeling of sub - decimeter resolution images with convolutional neu ral networks","venue":null,"work_id":"0866feb7-eb40-4833-b143-3be08a90e2d2","year":2017},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.176721Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:36721ab782c99db567a56685625181cc244568c6a031be56c141a2f9138bf3f3","observation_id":"732f7b98-f7e3-45b0-a40d-d3565286b04f","resolution":{"observed_at":"2026-08-14T10:33:01.478788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.457527Z","title":"Hourglass - ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery","venue":null,"work_id":"72a861d1-c220-4aee-bc6d-7eec52eed013","year":2017},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.181063Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:cf2962c173c58841616720848bf62f11b79c5110bdb95064a4518a1446223136","observation_id":"57ead6b2-55dc-4952-b13b-06703b83aa10","resolution":{"observed_at":"2026-08-14T10:33:01.463130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01962","last_updated":"2016-11-07T10:02:49Z","snapshot_observed_at":"2026-08-14T21:31:41.665962Z","submitted_at":"2016-11-07T10:02:49Z","title":"High-Resolution Semantic Labeling with Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":"1611.01962","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.01962","snapshot_observed_at":"2026-08-14T10:33:01.242769Z","title":"High-Resolution Semantic Labeling with Convolutional Neural Networks","venue":"cs.CV","work_id":"61a78365-aeb9-4197-8c2d-3620d1641f1d","year":2016},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.185708Z"},"links":{"cited_paper":"/paper/1611.01962","citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:1e348a763f78596c153cb7a67c29f4f7d5f3a9d9e029928ed14530d4401017a7","observation_id":"0f6fbf53-2c6e-43ec-9235-a91cd47d9660","resolution":{"observed_at":"2026-08-14T10:33:01.250002Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.442358Z","title":"Building Footprint Extraction from Hig h - Resolution Images via Spatial Residual Inception Convolutional Neural Network","venue":null,"work_id":"39a8f765-f14b-4844-bccf-9b1d6eb8c30a","year":2019},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.190317Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:a1df7e0845a75e10a26aa739b22cda72c5c5db6a253f30acb648bdae71d9665f","observation_id":"66c8b034-445c-4ca9-be18-6747f01fc734","resolution":{"observed_at":"2026-08-14T10:33:01.447053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.426917Z","title":"Classification with an edge: Improving semantic image segmentation w ith boundary detection","venue":null,"work_id":"72ca62e6-c05c-4097-b34c-a79c056c87bc","year":2018},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.194584Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:98813b724257c328693a75e3f5c4b9ec0a1ad0374da209adcdbda72a0fc58ac3","observation_id":"f723fc93-0339-43ae-a811-4019c706042b","resolution":{"observed_at":"2026-08-14T10:33:01.432232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.412449Z","title":"Pixel - Wise Classification Method for High Resolution Remote Sensing Imagery Usi ng Deep Neural Networks","venue":null,"work_id":"cdceb355-4ed1-4149-880f-e3f46db3803d","year":2018},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.199015Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:3b35a696012c6d7fcfb8aa0ab1e6ab4f177d71bb6befd4b7424dec235197c0a9","observation_id":"173734bc-0405-4c81-9f4e-0d5c6215def9","resolution":{"observed_at":"2026-08-14T10:33:01.417047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.397084Z","title":"A Y - Net deep learning method for road seg mentation using high - resolution visible remote sensing images","venue":null,"work_id":"33c6ab4b-03ed-4ffe-9f7f-6c832c827042","year":2019},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.203279Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:f5bc2ce95cfd7ccd775d54b672cf71c695657f1e2621d34c82030d2dd01a1abf","observation_id":"d5dc806e-12ae-432a-9cff-dfc92f7226fe","resolution":{"observed_at":"2026-08-14T10:33:01.402109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T10:33:01.382100Z","title":"Very Deep Convoluti onal Networks for Large - Scale Image Recognition","venue":null,"work_id":"5d479232-b524-4571-b25c-34c392be28c0","year":2015},"citing_paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T10:33:01.207918Z"},"links":{"citing_paper":"/paper/1908.11080"},"observation_digest":"sha256:955d94052e389a621ed847ebce2266042bdf7989d8c7b210552829283604f9f9","observation_id":"5bb5fd02-5e00-416c-bd2b-6bd13b1e8020","resolution":{"observed_at":"2026-08-14T10:33:01.386796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.11080","last_updated":"2019-08-29T07:46:32Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T10:12:20.787951Z","submitted_at":"2019-08-29T07:46:32Z","title":"DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":21},"total_outbound_references":32},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:1908.11080."}