{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VDHIM64MOVVMNBFDYMTYTTKYPW","short_pith_number":"pith:VDHIM64M","schema_version":"1.0","canonical_sha256":"a8ce867b8c756ac684a3c32789cd587da72f53e143fea7d4465259d382ecb488","source":{"kind":"arxiv","id":"2412.19492","version":1},"attestation_state":"computed","paper":{"title":"Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Chengyang Ye, Pingping Zhang, Yunzhi Zhuge","submitted_at":"2024-12-27T07:20:30Z","abstract_excerpt":"Recently, deep learning based methods have revolutionized remote sensing image segmentation. However, these methods usually rely on a pre-defined semantic class set, thus needing additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic classes. In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic classes in remote sensing images. To address the lack of OVRSISS datasets, we develop LandDiscover50K, a comprehensive dataset of 51,"},"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":"2412.19492","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-27T07:20:30Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"a761663ef6c731ee27640915351928ea365e2e78c94106befd15af58781b052a","abstract_canon_sha256":"3beb7b0edaa97695d445d195fc73b599cddf403a70a4eeed15dd1460a4b2ce2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:36.666255Z","signature_b64":"D8ZxLylO8XD2O0sY2oCgt3J6ZVpBUEPZzcuwZGT7vbnGa+Qrt4cSiNZil1WzcJ5tbwUtfLijPl3J2napKYIrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8ce867b8c756ac684a3c32789cd587da72f53e143fea7d4465259d382ecb488","last_reissued_at":"2026-07-05T09:54:36.665759Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:36.665759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Chengyang Ye, Pingping Zhang, Yunzhi Zhuge","submitted_at":"2024-12-27T07:20:30Z","abstract_excerpt":"Recently, deep learning based methods have revolutionized remote sensing image segmentation. However, these methods usually rely on a pre-defined semantic class set, thus needing additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic classes. In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic classes in remote sensing images. To address the lack of OVRSISS datasets, we develop LandDiscover50K, a comprehensive dataset of 51,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19492","kind":"arxiv","version":1},"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/2412.19492/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":"2412.19492","created_at":"2026-07-05T09:54:36.665838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19492v1","created_at":"2026-07-05T09:54:36.665838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19492","created_at":"2026-07-05T09:54:36.665838+00:00"},{"alias_kind":"pith_short_12","alias_value":"VDHIM64MOVVM","created_at":"2026-07-05T09:54:36.665838+00:00"},{"alias_kind":"pith_short_16","alias_value":"VDHIM64MOVVMNBFD","created_at":"2026-07-05T09:54:36.665838+00:00"},{"alias_kind":"pith_short_8","alias_value":"VDHIM64M","created_at":"2026-07-05T09:54:36.665838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12857","citing_title":"SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation","ref_index":60,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW","json":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW.json","graph_json":"https://pith.science/api/pith-number/VDHIM64MOVVMNBFDYMTYTTKYPW/graph.json","events_json":"https://pith.science/api/pith-number/VDHIM64MOVVMNBFDYMTYTTKYPW/events.json","paper":"https://pith.science/paper/VDHIM64M"},"agent_actions":{"view_html":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW","download_json":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW.json","view_paper":"https://pith.science/paper/VDHIM64M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19492&json=true","fetch_graph":"https://pith.science/api/pith-number/VDHIM64MOVVMNBFDYMTYTTKYPW/graph.json","fetch_events":"https://pith.science/api/pith-number/VDHIM64MOVVMNBFDYMTYTTKYPW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW/action/storage_attestation","attest_author":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW/action/author_attestation","sign_citation":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW/action/citation_signature","submit_replication":"https://pith.science/pith/VDHIM64MOVVMNBFDYMTYTTKYPW/action/replication_record"}},"created_at":"2026-07-05T09:54:36.665838+00:00","updated_at":"2026-07-05T09:54:36.665838+00:00"}