{"as_of":"2026-08-22T18:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21269ba973f07189a6173934bb789e8ebd38869a63b4903f276f329bd34dd3c1","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T19:36:42.560507Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:18:29.282142Z","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":"2026-08-11T23:07:00.904347Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"cited_work":{"arxiv_id":"1906.08716","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.08716","snapshot_observed_at":"2026-08-11T23:07:00.904347Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","venue":"cs.CV","work_id":"c9659096-d78c-459c-8d0d-afa9b0be9561","year":2019},"citing_paper":{"arxiv_id":"2412.02831","last_updated":"2026-06-28T17:16:26Z","snapshot_observed_at":"2026-08-19T09:54:12.365999Z","submitted_at":"2024-12-03T20:53:42Z","title":"FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:07:00.630322Z"},"links":{"cited_paper":"/paper/1906.08716","citing_paper":"/paper/2412.02831"},"observation_digest":"sha256:06893010d1ce01d45cc63fd44daec8a36781d05eb1185bf065079e87e001d295","observation_id":"0e133ec9-d14b-4999-9d24-dcc5b557126c","resolution":{"observed_at":"2026-08-11T23:07:00.907602Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.08716","snapshot_observed_at":"2026-08-16T04:18:29.282142Z","title":"Deep-learning- based aerial image classification for emergency response applications using unmanned aerial vehicles,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.01638","last_updated":"2025-05-03T00:23:11Z","snapshot_observed_at":"2026-08-17T11:42:11.229213Z","submitted_at":"2025-05-03T00:23:11Z","title":"Seeing Heat with Color -- RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:18:29.282142Z"},"links":{"cited_paper":"/paper/1906.08716","citing_paper":"/paper/2505.01638"},"observation_digest":"sha256:fbd4bee4de3227a85e734f461d055fcbd6fd34d6cd488990346023137b78e451","observation_id":"1623848d-b00b-4f46-8a2a-7e0bf943a901","resolution":{"observed_at":"2026-08-16T04:18:29.282142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1906.08716/citation-record","integrity":"/paper/1906.08716/integrity","json":"/paper/1906.08716/citation-record.json","paper":"/paper/1906.08716"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng","venue":null,"work_id":"671d4a25-1222-4c65-b32c-1c8539459c61","year":2016},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:d836cb20bf118ab535fffbced9c055620df98e4280838b662a0bbf499e710b6b","observation_id":"5bce00b9-d07d-48db-bdbc-5797272b94ba","resolution":{"observed_at":"2026-05-25T19:37:07.569271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Survey of computer vision algorithms and applications for unmanned aerial vehicles","venue":null,"work_id":"bf8a2551-1352-49d7-acb8-bb4a33b23471","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:fb5f901d8e42b3cd87015eb17ea20b89ad9fbb768259355e902b8a2528265df1","observation_id":"6931eb75-2fec-4ad9-bd90-1a1f4a4bf6d0","resolution":{"observed_at":"2026-05-25T19:37:07.577621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"A convolutional neural network approach for assisting avalanche search and rescue operations with uav imagery","venue":null,"work_id":"15ae7b63-ce48-4859-a01b-6143797f190a","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:eda63eefc9d2741481bacfe0d0a634b014c82c7acd78ef6b0977991ec994d876","observation_id":"b9cef2ba-0c14-4dec-ba5e-25887c2bed1b","resolution":{"observed_at":"2026-05-25T19:37:07.630386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Cheng, C","venue":null,"work_id":"14774b76-e10a-427c-be72-bf569135b93c","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:9cc44daf2daccd21373b2173278922c0369457232721ba4d5a79ec0371070b97","observation_id":"d8642040-1e27-41a3-b1a2-32cfc15bb784","resolution":{"observed_at":"2026-05-25T19:37:07.592325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a08e8d5f-7199-4338-ae81-709b083e6a8f","year":2015},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:9ee1c182a920b58bdb496fd2120e22907f0e34e752a465a82513717c366a0ca0","observation_id":"29d2591a-163a-4122-ae5f-12d548769248","resolution":{"observed_at":"2026-05-25T19:37:07.573099Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":"1512.03385","doi":"10.48550/arxiv.1512.03385","metadata_source":"pith","pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep Residual Learning for Image Recognition","venue":"cs.CV","work_id":"ae9e5671-23e8-4853-82a4-699b5b8dd639","year":2015},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:b065e8437c82de7f938d6d49d22b657b9012e908775d84d1017e5ff2b7777d12","observation_id":"17281fb7-dbd3-4d3a-a8a9-5ef8e34fc339","resolution":{"observed_at":"2026-05-25T19:37:07.317832Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-02T01:38:25.811068+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T01:38:25.811068+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Visual analytics in deep learning: An interrogative survey for the next frontiers","venue":null,"work_id":"b8449232-aeef-411c-b9aa-0d59ea64b386","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:816e26349306a25a4397a40384c485ed272997aad3af9e7d410f0f13a60c6d02","observation_id":"7b9d299c-fa16-4fde-b2c5-ec7ddba26339","resolution":{"observed_at":"2026-05-25T19:37:07.634748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"A review on evaluation metrics for data classiﬁcation evaluations","venue":null,"work_id":"188e20a2-8d06-4871-989c-958d2627021c","year":2015},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:c5f90bfc8a95028feaa61cf841c5ae60653425a5be2db06e4a07deddf9507e41","observation_id":"611defb2-7842-43b1-b1d6-7371df70a5fc","resolution":{"observed_at":"2026-05-25T19:37:07.599118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-08-20T09:32:02.065929Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":"1704.04861","doi":"10.48550/arxiv.1704.04861","metadata_source":"pith","pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","venue":"cs.CV","work_id":"3870239a-c950-4625-bf33-c4f902d14175","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:f9b87e697b6a77aa6a21f18ece35c7c09295fcd694c3aefc9525738cb92474c8","observation_id":"ffbfa876-98d4-4798-89ba-519b953a9f10","resolution":{"observed_at":"2026-05-25T19:37:07.345185Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Prenafeta-BoldÃo","venue":null,"work_id":"c632e4f5-6482-40d2-940c-fbaa88e4b95f","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:371abd230311c6ac69ed6fe339365d88792dabd1789779f3a304d2fcb84c4454","observation_id":"824a341b-034d-4cb9-bda8-cf9807e915bc","resolution":{"observed_at":"2026-05-25T19:37:07.581622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5b9b1e35-a9fb-492f-a547-288c6981d7c4","year":2016},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:eab56d20ba495089910b9ebafa2d77e5fa4e7f25a2bb87722bf619fddc838ddd","observation_id":"4369716d-ca92-4953-89fc-4d62000bcec9","resolution":{"observed_at":"2026-05-25T19:37:07.585140Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Kyrkou, S","venue":null,"work_id":"be15b4f1-36ee-4ed3-afe6-800464e86174","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:7da3bab1c9c11b1cedc8bf3f2dfe0c47c01d2ee9b9b17991dc1238ce051bbe09","observation_id":"d2cbe7aa-eba5-4d6f-9a9c-ddb5e332378e","resolution":{"observed_at":"2026-05-25T19:37:07.588811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.4400","last_updated":"2014-03-04T05:15:42Z","snapshot_observed_at":"2026-08-14T23:51:30.531758Z","submitted_at":"2013-12-16T15:34:13Z","title":"Network In Network","version":3},"cited_work":{"arxiv_id":"1312.4400","doi":null,"metadata_source":"pith","pith_arxiv_id":"1312.4400","snapshot_observed_at":"2026-07-02T08:06:47.826842Z","title":"Network In Network","venue":"cs.NE","work_id":"d0d875f6-6330-48b0-8f92-300cbfb5d81a","year":2013},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1312.4400","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:8c60589e033d9890d7ba8a32fe035752a00bcff24017254c042dd3f807cbc73b","observation_id":"db2d2cc1-520e-458b-8802-04ff19316c6d","resolution":{"observed_at":"2026-05-25T19:37:07.310056Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Maggiori, Y","venue":null,"work_id":"00e7b06f-8181-4067-882b-885b7d89e0d2","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:87bb6b014fd54ba0cccf519d82cbe51e1792e3203bd425786e60aee922e55cd5","observation_id":"433376fc-4f46-420e-b787-3b8e6e27d4d8","resolution":{"observed_at":"2026-05-25T19:37:07.595178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.01030","last_updated":"2016-10-05T09:50:15Z","snapshot_observed_at":"2026-08-14T21:36:47.743494Z","submitted_at":"2016-10-04T14:53:51Z","title":"Applications of Online Deep Learning for Crisis Response Using Social Media Information","version":2},"cited_work":{"arxiv_id":"1610.01030","doi":null,"metadata_source":"pith","pith_arxiv_id":"1610.01030","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Applications of Online Deep Learning for Crisis Response Using Social Media Information","venue":"cs.CL","work_id":"11f7640b-b793-465e-9690-247e97c93027","year":2016},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1610.01030","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:8cc0d666196d4263cdd71ad212f13a23016869a1dffdc314cf51d0395c477259","observation_id":"94635af6-1324-4e03-8ecd-66c93fb82d2f","resolution":{"observed_at":"2026-05-25T19:37:07.351313Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Disaster prevention and emergency response using unmanned aerial systems","venue":null,"work_id":"73ec972c-5cd6-4a32-bee8-6083d6eb63a7","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:27daab2dc1a8eeecd091be312c110dcf66c1ca0ace02a96aeb8d2494ffb58afe","observation_id":"33dd3b2a-3971-429b-9d01-cd1ed9d28125","resolution":{"observed_at":"2026-05-25T19:37:07.639044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Petrides, C","venue":null,"work_id":"38f5dba3-e518-449f-833c-667e274d7d55","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:5c74db95b17de6f3d1c19b70c44ec4dd48d05dee8bb5fd11525ecc46effe0334","observation_id":"32e57b67-6f0a-4160-9b9a-5fc5ebaf3d8a","resolution":{"observed_at":"2026-05-25T19:37:07.646821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Cnn features off-the-shelf: An astounding baseline for recognition","venue":null,"work_id":"d12bb442-5cd8-4b0b-b602-bd03f33066de","year":2014},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:89a25f81edb574b3dc8e6d825b544f645efa167db08aacf104884299989f3749","observation_id":"84ca4681-254c-4e92-aca9-34b130845918","resolution":{"observed_at":"2026-05-25T19:37:07.610289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"aead1d8c-6b98-47ab-b6a8-c7dc5c654e87","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:f98f517c777dbab69d0f34eb7e16ea2d1d54864e5fd0257e14e1f9ce010bf2a6","observation_id":"764f3936-7d88-4db9-b695-4308b1ba6219","resolution":{"observed_at":"2026-05-25T19:37:07.623931Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the iot era","venue":null,"work_id":"b7924440-4ddf-4b83-a0f1-418974efbde5","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:ff88c41d9eb2d27e5c229af2d0fa13b62ee128879ec716b9304057e702798e10","observation_id":"035b178a-9004-4aec-a2c2-623840475dc4","resolution":{"observed_at":"2026-05-25T19:37:07.614437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Deep convolutional neural networks for ﬁre detection in images","venue":null,"work_id":"cb4b6760-26b2-4894-8f22-e2de8000773f","year":2017},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:93424391b855b6603baefdfdb2669d977eaf1e5536f3bfcb68cb1407c5f0c090","observation_id":"6eb71dd8-aa98-401c-808b-62710e844fee","resolution":{"observed_at":"2026-05-25T19:37:07.643685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-17T19:17:06.411141Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":"1409.1556","doi":"10.48550/arxiv.1409.1556","metadata_source":"pith","pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","venue":"cs.CV","work_id":"1c4b4409-c14b-488b-a086-c57a5aab8a29","year":2014},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:8f8a2a2a064d6502a06142d0d786bd501fe8c1a792ce09aff33738e9268dce9c","observation_id":"8a1100cf-ffe3-4290-a8f4-89bd2036ce96","resolution":{"observed_at":"2026-05-25T19:37:07.324955Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-07-12T23:51:07.915518+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T23:51:07.915518+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.07625","last_updated":"2017-10-17T02:50:38Z","snapshot_observed_at":"2026-08-19T19:16:27.178931Z","submitted_at":"2016-12-22T14:50:40Z","title":"Hardware for Machine Learning: Challenges and Opportunities","version":5},"cited_work":{"arxiv_id":"1612.07625","doi":null,"metadata_source":"pith","pith_arxiv_id":"1612.07625","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hardware for Machine Learning: Challenges and Opportunities","venue":"cs.CV","work_id":"a166331e-40aa-4283-8e6b-74a3d62aadd3","year":2016},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1612.07625","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:de6fb9404612abbac8ac5a5491f01233346b6e8d8dc0416c2238e95d02a1d102","observation_id":"b9380046-d353-426d-94ac-0a9f9146becd","resolution":{"observed_at":"2026-05-25T19:37:07.338906Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.4842","last_updated":"2014-09-17T01:03:11Z","snapshot_observed_at":"2026-08-18T03:16:57.233236Z","submitted_at":"2014-09-17T01:03:11Z","title":"Going Deeper with Convolutions","version":1},"cited_work":{"arxiv_id":"1409.4842","doi":"10.48550/arxiv.1409.4842","metadata_source":"pith","pith_arxiv_id":"1409.4842","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Going Deeper with Convolutions","venue":"cs.CV","work_id":"bfbafb27-ff94-46ae-98e9-35389fe858b6","year":2014},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"cited_paper":"/paper/1409.4842","citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:e88c9e4602853518b6ebf0d91e530b73908e167f42576f6b4c303b1219cda129","observation_id":"0f7967c9-c348-4c40-9d51-d0087e12fc6f","resolution":{"observed_at":"2026-05-25T19:37:07.330508Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1eeffdef-b088-4f05-8594-fe69b4698581","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:fa245e957699f1133f3e2f7117acb79cb852927f2cfd02cd5eae82c58e41a7a7","observation_id":"7a97a827-3f19-41b7-9f38-dc4d58cde269","resolution":{"observed_at":"2026-05-25T19:37:07.603111Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"65a384cb-9ee0-4807-85da-d432f4c42bfa","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:255f6fb5a3ddafb81899aa5ee6911930e4408bb41e0c038e14f032146da287ef","observation_id":"469ae58c-0dd5-4841-96be-3c5c5324d615","resolution":{"observed_at":"2026-05-25T19:37:07.606698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-05T21:23:00.469572Z","title":"Saliency detection and deep learning-based wildﬁre identiﬁcation in uav imagery","venue":null,"work_id":"3f1e96ef-9459-4158-a851-9f8942418f97","year":2018},"citing_paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T19:36:42.560507Z"},"links":{"citing_paper":"/paper/1906.08716"},"observation_digest":"sha256:c256555859bb4df99c7ea115357d7c4c95d1838dc8765e6c909f475b349cea8b","observation_id":"a971df2b-6f8e-4088-ba5d-8b5dad6db16a","resolution":{"observed_at":"2026-05-25T19:37:07.620024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1906.08716","last_updated":"2019-06-20T16:03:32Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T09:41:04.847908Z","submitted_at":"2019-06-20T16:03:32Z","title":"Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":5,"verified_exact":3,"verified_fuzzy":15},"total_outbound_references":27},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:1906.08716."}