{"as_of":"2026-08-20T08:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:544e8c33ad3977203db2402dcb5c5c22049559d803f7b8b2656b9552e85d4ae7","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T20:43:56.798461Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T20:43:56.723686Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-09T20:43:56.867065Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"cited_work":{"arxiv_id":"2501.19283","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.19283","snapshot_observed_at":"2026-08-09T20:43:56.867065Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","venue":"cs.CV","work_id":"6bc35a9f-47ba-44a3-9d83-ba9479bc2684","year":2025},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.723686Z"},"links":{"cited_paper":"/paper/2501.19283","citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:1368b54dbccc1526403fa1727f80c999780a35bd1a9ea85e878d75ebaee77b96","observation_id":"1f9c76d4-c34d-4d3e-b904-53197386f844","resolution":{"observed_at":"2026-08-09T20:43:56.875036Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.19283/citation-record","integrity":"/paper/2501.19283/integrity","json":"/paper/2501.19283/citation-record.json","paper":"/paper/2501.19283"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T20:43:57.105044Z","title":"The fundamental problems that these models try to solve are, (i) to estimate the underlying distribution of the observed data and (ii) to generate sample data from the same","venue":null,"work_id":"a238df5d-79f5-49fd-bfa7-9fdee2a37aa0","year":null},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.718324Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:bb4a6b2f57258241e1d4da609e8491a9dd5fab679e24233bb37caa4fefc5c071","observation_id":"3cb655a3-29c7-4d30-8627-c3deb0b2afec","resolution":{"observed_at":"2026-08-09T20:43:57.110800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"cited_work":{"arxiv_id":"2501.19283","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.19283","snapshot_observed_at":"2026-08-09T20:43:56.867065Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","venue":"cs.CV","work_id":"6bc35a9f-47ba-44a3-9d83-ba9479bc2684","year":2025},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.723686Z"},"links":{"cited_paper":"/paper/2501.19283","citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:1368b54dbccc1526403fa1727f80c999780a35bd1a9ea85e878d75ebaee77b96","observation_id":"1f9c76d4-c34d-4d3e-b904-53197386f844","resolution":{"observed_at":"2026-08-09T20:43:56.875036Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:57.088262Z","title":null,"venue":null,"work_id":"6b1574c7-2ca8-4f86-85e6-77fafe0cf728","year":2025},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.728927Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:00836a2d8da83bcd5d8dc7b9cf6e022b44e95caf80c64de81ccbb89b8dc62139","observation_id":"8890b368-96e9-4daf-a1fd-65212acb76ff","resolution":{"observed_at":"2026-08-09T20:43:57.093656Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:57.071429Z","title":null,"venue":null,"work_id":"cbb4718b-7dfb-4b26-927d-a34aa456425d","year":2025},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.734260Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:f21e47eabaeee5203a47587aef04fa1a845aa4c4041541262455657052b05318","observation_id":"7116f21a-c052-4204-b160-c88949ce91f7","resolution":{"observed_at":"2026-08-09T20:43:57.076471Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:57.055726Z","title":"Deep gen- erative models: Survey,","venue":null,"work_id":"f671dd28-e8d1-4755-80b1-28dfe77e9a70","year":2018},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.739444Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:49e0a5ef4e9c1740b543b506539bd82c52b6862ebc372e58ca2a1f4916e07d2c","observation_id":"b5756d1f-fb5a-4012-8a0d-438ae345bec5","resolution":{"observed_at":"2026-08-09T20:43:57.060713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:57.039630Z","title":"An introduction to deep generative modeling,","venue":null,"work_id":"64ee083d-4973-4888-8839-b2aab197f601","year":2021},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.744271Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:edb43a86e6d085bcbde93f8a0b3e28b86d0a0594aed72cf2dbc4e2092666e0f4","observation_id":"190f2ea5-930f-4a7e-92bf-c804bc1c0ed2","resolution":{"observed_at":"2026-08-09T20:43:57.044739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:57.024365Z","title":"Deep gaus- sian mixture models,","venue":null,"work_id":"9bad6822-240f-4a22-9065-83fd19b63039","year":2019},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.748909Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:230eb2ba1318040aae6353d8ffd6e6eb3d025a900efc5ceb176d1955b441cd3f","observation_id":"a9d4a532-51d8-4b55-ac01-9c823e1939cb","resolution":{"observed_at":"2026-08-09T20:43:57.029055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-09T20:43:56.753610Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.753610Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:1ef26c18e2b72bc609a0a3f11b3d800c23fcde6e32859cca88bf385ae4db7360","observation_id":"0224c0ae-d2a2-4ada-ae3c-02fdd97fc588","resolution":{"observed_at":"2026-08-09T20:43:56.753610Z","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":"2026-08-09T20:43:57.009214Z","title":"Generative adversar- ial networks,","venue":null,"work_id":"dfb94e0b-345d-4433-8e54-2bf667e830e0","year":2020},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.758015Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:1ee501725dbecd92303b5237d5432bbcdc0b769aeae0766c0e8be88fab0a4c12","observation_id":"0ba2025e-f3e2-4926-896b-3f34c41f9cb7","resolution":{"observed_at":"2026-08-09T20:43:57.013972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.762058Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.762058Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:bc3e15cad65c4d9cb8c6c4a3bf1a279235c75b32ee668ff46cb645905d548cb0","observation_id":"863e5b55-e6ff-4f92-9892-7841e0767fb0","resolution":{"observed_at":"2026-08-09T20:43:56.762058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.11970","last_updated":"2022-08-25T09:55:25Z","snapshot_observed_at":"2026-08-19T23:51:12.297696Z","submitted_at":"2022-08-25T09:55:25Z","title":"Understanding Diffusion Models: A Unified Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.11970","snapshot_observed_at":"2026-08-09T20:43:56.766080Z","title":"Understanding diffusion models: A uni- fied perspective,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.766080Z"},"links":{"cited_paper":"/paper/2208.11970","citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:d086f75a8c75af8a508ea9915b42904021a883ebe9dc6c81e5ed25901dfdb646","observation_id":"48579103-f1e4-4519-b250-26763b519927","resolution":{"observed_at":"2026-08-09T20:43:56.766080Z","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":"2026-08-09T20:43:56.982341Z","title":"Rose, Jacob J","venue":null,"work_id":"bc9fc4bf-25ae-49e7-8a2e-ff9a943b6ced","year":2017},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.770408Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:9fcf28f8bccde990d498c65c69d3af2ec9f9b1ea48b196e3cb7c8eb84e0c844c","observation_id":"539bca9b-8429-4491-9375-472c7ff1ecb3","resolution":{"observed_at":"2026-08-09T20:43:56.987015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.966726Z","title":"The kolmogorov-smirnov test for goodness of fit,","venue":null,"work_id":"d9a34efa-eab1-4da8-af35-c72686b196ca","year":1951},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.775645Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:0de075bf9e79cdc9be69b3fc225c77c50e3d19aebca3e274fd9cff32a8400f9f","observation_id":"10ab5a76-c9de-4604-90b1-678857045d30","resolution":{"observed_at":"2026-08-09T20:43:56.971754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.949553Z","title":"Ball divergence: Nonparametric two sample test,","venue":null,"work_id":"d5331a89-209f-4594-b066-6947e7f7bada","year":2018},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.780257Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:2435160c6472b1d9dab274f6c394f36ffe830cde939a760a5222b421e3832fc2","observation_id":"9472a381-8774-49ba-8d35-c5338373af6f","resolution":{"observed_at":"2026-08-09T20:43:56.955234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.933606Z","title":"Multilayer feedforward networks are universal approx- imators,","venue":null,"work_id":"9d4ab52d-0124-40a8-b914-911d2c4acca9","year":1989},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.784772Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:d610155818baff0ebc73b6a901814aa49f6fd8d748748a9003da7edfea3a3ed1","observation_id":"35f5aa80-67d6-4d90-936d-2a2916874175","resolution":{"observed_at":"2026-08-09T20:43:56.938991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.917215Z","title":"Deep learning,","venue":null,"work_id":"0866ef96-4b46-44eb-9bd1-a20147745e7f","year":2016},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.789445Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:f67c687cac5dc556aaaf279069170907844d24109859ebd1861811b6fb770348","observation_id":"e65f5411-b48d-46e0-8cf6-62b64c93d70b","resolution":{"observed_at":"2026-08-09T20:43:56.923037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.902109Z","title":"260–260, Springer US, Boston, MA, 2017","venue":null,"work_id":"f3d56df5-ca06-489b-b488-2f8428198e0c","year":2017},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.793942Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:a3bc2cfd5521f30be1b48821c1003a8614e1bbaecdee5d6ec71fcdbec54014f3","observation_id":"17f05f3b-92e2-4939-b47a-29a697d2401d","resolution":{"observed_at":"2026-08-09T20:43:56.906478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-09T20:43:56.886903Z","title":"A coefficient of agreement for nominal scales,","venue":null,"work_id":"580c1e30-1dd3-49f7-856d-b407dcd4c102","year":1960},"citing_paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T20:43:56.798461Z"},"links":{"citing_paper":"/paper/2501.19283"},"observation_digest":"sha256:05f5e09961a2b37b9967b6d3bbc6d5e21f5d69598fd21db6b5842e5df74624a9","observation_id":"82bbc8a9-da13-4a76-8cd3-208741d6ecc2","resolution":{"observed_at":"2026-08-09T20:43:56.891944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.19283","last_updated":"2025-01-31T16:47:22Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T00:29:04.680780Z","submitted_at":"2025-01-31T16:47:22Z","title":"Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":12},"total_outbound_references":18},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2501.19283."}