{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GBMQMVYQGH4FYWKFWG4A3JPSGR","short_pith_number":"pith:GBMQMVYQ","schema_version":"1.0","canonical_sha256":"305906571031f85c5945b1b80da5f23474a1d26328314c51e7e9e08075219dfe","source":{"kind":"arxiv","id":"2402.13791","version":2},"attestation_state":"computed","paper":{"title":"Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adrian H\\\"ohl, Andreas Dengel, Dario Oliveira, Hiba Najjar, Ivica Obadic, Miguel \\'Angel Fern\\'andez Torres, Xiao Xiang Zhu, Zeynep Akata","submitted_at":"2024-02-21T13:19:58Z","abstract_excerpt":"In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches and emerging directions that tackle"},"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":"2402.13791","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T13:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"b071e6f2421bd4bdbbbebdbb30a663cd7251082f97936c8e020176bdf6cdd9d5","abstract_canon_sha256":"1b8609584abbdbd62866c214876cfe9612f901cd253dd1b6086e89eb82afed8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:58.410639Z","signature_b64":"qcVTBYPly5fVZoez89FkbKxEq35WhLJiE6I90C42dMRuDl98x8+EFgradx8sdILai0nFgqZWfIIXamiT03dxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"305906571031f85c5945b1b80da5f23474a1d26328314c51e7e9e08075219dfe","last_reissued_at":"2026-05-26T02:04:58.409823Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:58.409823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adrian H\\\"ohl, Andreas Dengel, Dario Oliveira, Hiba Najjar, Ivica Obadic, Miguel \\'Angel Fern\\'andez Torres, Xiao Xiang Zhu, Zeynep Akata","submitted_at":"2024-02-21T13:19:58Z","abstract_excerpt":"In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches and emerging directions that tackle"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13791","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/2402.13791/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":"2402.13791","created_at":"2026-05-26T02:04:58.409922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.13791v2","created_at":"2026-05-26T02:04:58.409922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13791","created_at":"2026-05-26T02:04:58.409922+00:00"},{"alias_kind":"pith_short_12","alias_value":"GBMQMVYQGH4F","created_at":"2026-05-26T02:04:58.409922+00:00"},{"alias_kind":"pith_short_16","alias_value":"GBMQMVYQGH4FYWKF","created_at":"2026-05-26T02:04:58.409922+00:00"},{"alias_kind":"pith_short_8","alias_value":"GBMQMVYQ","created_at":"2026-05-26T02:04:58.409922+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/GBMQMVYQGH4FYWKFWG4A3JPSGR","json":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR.json","graph_json":"https://pith.science/api/pith-number/GBMQMVYQGH4FYWKFWG4A3JPSGR/graph.json","events_json":"https://pith.science/api/pith-number/GBMQMVYQGH4FYWKFWG4A3JPSGR/events.json","paper":"https://pith.science/paper/GBMQMVYQ"},"agent_actions":{"view_html":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR","download_json":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR.json","view_paper":"https://pith.science/paper/GBMQMVYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.13791&json=true","fetch_graph":"https://pith.science/api/pith-number/GBMQMVYQGH4FYWKFWG4A3JPSGR/graph.json","fetch_events":"https://pith.science/api/pith-number/GBMQMVYQGH4FYWKFWG4A3JPSGR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR/action/storage_attestation","attest_author":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR/action/author_attestation","sign_citation":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR/action/citation_signature","submit_replication":"https://pith.science/pith/GBMQMVYQGH4FYWKFWG4A3JPSGR/action/replication_record"}},"created_at":"2026-05-26T02:04:58.409922+00:00","updated_at":"2026-05-26T02:04:58.409922+00:00"}