{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5CBHDZ6FQK5TJR32WLEBAIEGZJ","short_pith_number":"pith:5CBHDZ6F","schema_version":"1.0","canonical_sha256":"e88271e7c582bb34c77ab2c8102086ca5243838c507d52ad0994f4c2d004803f","source":{"kind":"arxiv","id":"1905.12886","version":2},"attestation_state":"computed","paper":{"title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Aditya Arora, Akshita Gupta, Fahad Shahbaz Khan, Fan Zhu, Gui-Song Xia, Guolei Sun, Ling Shao, Salman Khan, Syed Waqas Zamir, Xiang Bai","submitted_at":"2019-05-30T07:18:28Z","abstract_excerpt":"Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object ins"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1905.12886","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-30T07:18:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ed47383cadf86e3f3d3fb77df7d4969f7215da906576a54d6c8c800b9493443b","abstract_canon_sha256":"41029e294b79264dc66002150cfb16276129560be191dd3385377c1400a9afc5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:00:15.539381Z","signature_b64":"xjM4u9Tdt7EAzOmn1GBDCivMOc5vFPg4XWmBTXLFKPdkn9SUt0j41Ba6Ql/rbpsirlB0iFUlojNheH4voU81Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e88271e7c582bb34c77ab2c8102086ca5243838c507d52ad0994f4c2d004803f","last_reissued_at":"2026-07-05T00:00:15.538993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:00:15.538993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Aditya Arora, Akshita Gupta, Fahad Shahbaz Khan, Fan Zhu, Gui-Song Xia, Guolei Sun, Ling Shao, Salman Khan, Syed Waqas Zamir, Xiang Bai","submitted_at":"2019-05-30T07:18:28Z","abstract_excerpt":"Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object ins"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.12886","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1905.12886/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1905.12886","created_at":"2026-07-05T00:00:15.539040+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.12886v2","created_at":"2026-07-05T00:00:15.539040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.12886","created_at":"2026-07-05T00:00:15.539040+00:00"},{"alias_kind":"pith_short_12","alias_value":"5CBHDZ6FQK5T","created_at":"2026-07-05T00:00:15.539040+00:00"},{"alias_kind":"pith_short_16","alias_value":"5CBHDZ6FQK5TJR32","created_at":"2026-07-05T00:00:15.539040+00:00"},{"alias_kind":"pith_short_8","alias_value":"5CBHDZ6F","created_at":"2026-07-05T00:00:15.539040+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ","json":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ.json","graph_json":"https://pith.science/api/pith-number/5CBHDZ6FQK5TJR32WLEBAIEGZJ/graph.json","events_json":"https://pith.science/api/pith-number/5CBHDZ6FQK5TJR32WLEBAIEGZJ/events.json","paper":"https://pith.science/paper/5CBHDZ6F"},"agent_actions":{"view_html":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ","download_json":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ.json","view_paper":"https://pith.science/paper/5CBHDZ6F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.12886&json=true","fetch_graph":"https://pith.science/api/pith-number/5CBHDZ6FQK5TJR32WLEBAIEGZJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5CBHDZ6FQK5TJR32WLEBAIEGZJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ/action/storage_attestation","attest_author":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ/action/author_attestation","sign_citation":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ/action/citation_signature","submit_replication":"https://pith.science/pith/5CBHDZ6FQK5TJR32WLEBAIEGZJ/action/replication_record"}},"created_at":"2026-07-05T00:00:15.539040+00:00","updated_at":"2026-07-05T00:00:15.539040+00:00"}