{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UCBRLVRNUACFDSXDCG4Z2YXZRQ","short_pith_number":"pith:UCBRLVRN","canonical_record":{"source":{"id":"2406.17458","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T10:53:57Z","cross_cats_sorted":[],"title_canon_sha256":"310662961ff08dd24665336c0e77e25dfde9165a595ef91b7e60a29046f5eabc","abstract_canon_sha256":"25391fad1feceafd39dcf09c741758e6544b286d893e552176ab7e5a3f74434c"},"schema_version":"1.0"},"canonical_sha256":"a08315d62da00451cae311b99d62f98c1cbcb35b52e47b54add19c60fd0b5d30","source":{"kind":"arxiv","id":"2406.17458","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.17458","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"arxiv_version","alias_value":"2406.17458v3","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17458","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_12","alias_value":"UCBRLVRNUACF","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_16","alias_value":"UCBRLVRNUACFDSXD","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_8","alias_value":"UCBRLVRN","created_at":"2026-07-05T11:18:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UCBRLVRNUACFDSXDCG4Z2YXZRQ","target":"record","payload":{"canonical_record":{"source":{"id":"2406.17458","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T10:53:57Z","cross_cats_sorted":[],"title_canon_sha256":"310662961ff08dd24665336c0e77e25dfde9165a595ef91b7e60a29046f5eabc","abstract_canon_sha256":"25391fad1feceafd39dcf09c741758e6544b286d893e552176ab7e5a3f74434c"},"schema_version":"1.0"},"canonical_sha256":"a08315d62da00451cae311b99d62f98c1cbcb35b52e47b54add19c60fd0b5d30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:12.931678Z","signature_b64":"WzLLChgTayqgJ8kT6lNnHYxmsCwxpmwwSRNq/T+hFpwDejs/YgY+wP/CkKxdTDJhvZs0nwnYJUHKQUMsasszAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a08315d62da00451cae311b99d62f98c1cbcb35b52e47b54add19c60fd0b5d30","last_reissued_at":"2026-07-05T11:18:12.931192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:12.931192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.17458","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:18:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lQvQB/aWOuDrm1XA3JjKUu9RgIsH5jR8zbwzE9Ys6f7vMnHiAouk4bi6vTE/TR5r6HVem728vsxX8yvgUePICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T16:43:11.023474Z"},"content_sha256":"8ce91cdc40aed172016a88715219ccf7946d45670d88d0820c136ce0f6b9e00b","schema_version":"1.0","event_id":"sha256:8ce91cdc40aed172016a88715219ccf7946d45670d88d0820c136ce0f6b9e00b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UCBRLVRNUACFDSXDCG4Z2YXZRQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Heng Fang, Hossein Azizpour, Sebastian Hafner, Yifang Ban","submitted_at":"2024-06-25T10:53:57Z","abstract_excerpt":"Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable development strategies with accurate information on urban growth. Deep learning-based methods have achieved promising urban change detection results from optical satellite image pairs using convolutional neural networks (ConvNets), transformers, and a multi-task learning setup. However, bi-temporal methods are limited for continuous urban change detection, i.e., the detection of changes in consecutive image pairs of satel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17458","kind":"arxiv","version":3},"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/2406.17458/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:18:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z2i+fRv/EkjmXft4riu1DWhjObKtMtomOxK4oMQERE1dozC9uqsk71flWxSTb3pRjK00awm7jSbllMULtSU6Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T16:43:11.023859Z"},"content_sha256":"9c273ba479149b017a1e1e7363fe3360c5162576fffe9fc5641cef0fdfb47204","schema_version":"1.0","event_id":"sha256:9c273ba479149b017a1e1e7363fe3360c5162576fffe9fc5641cef0fdfb47204"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/bundle.json","state_url":"https://pith.science/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-12T16:43:11Z","links":{"resolver":"https://pith.science/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ","bundle":"https://pith.science/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/bundle.json","state":"https://pith.science/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UCBRLVRNUACFDSXDCG4Z2YXZRQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UCBRLVRNUACFDSXDCG4Z2YXZRQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"25391fad1feceafd39dcf09c741758e6544b286d893e552176ab7e5a3f74434c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T10:53:57Z","title_canon_sha256":"310662961ff08dd24665336c0e77e25dfde9165a595ef91b7e60a29046f5eabc"},"schema_version":"1.0","source":{"id":"2406.17458","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.17458","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"arxiv_version","alias_value":"2406.17458v3","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17458","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_12","alias_value":"UCBRLVRNUACF","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_16","alias_value":"UCBRLVRNUACFDSXD","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_8","alias_value":"UCBRLVRN","created_at":"2026-07-05T11:18:12Z"}],"graph_snapshots":[{"event_id":"sha256:9c273ba479149b017a1e1e7363fe3360c5162576fffe9fc5641cef0fdfb47204","target":"graph","created_at":"2026-07-05T11:18:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.17458/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable development strategies with accurate information on urban growth. Deep learning-based methods have achieved promising urban change detection results from optical satellite image pairs using convolutional neural networks (ConvNets), transformers, and a multi-task learning setup. However, bi-temporal methods are limited for continuous urban change detection, i.e., the detection of changes in consecutive image pairs of satel","authors_text":"Heng Fang, Hossein Azizpour, Sebastian Hafner, Yifang Ban","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T10:53:57Z","title":"Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17458","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8ce91cdc40aed172016a88715219ccf7946d45670d88d0820c136ce0f6b9e00b","target":"record","created_at":"2026-07-05T11:18:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"25391fad1feceafd39dcf09c741758e6544b286d893e552176ab7e5a3f74434c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T10:53:57Z","title_canon_sha256":"310662961ff08dd24665336c0e77e25dfde9165a595ef91b7e60a29046f5eabc"},"schema_version":"1.0","source":{"id":"2406.17458","kind":"arxiv","version":3}},"canonical_sha256":"a08315d62da00451cae311b99d62f98c1cbcb35b52e47b54add19c60fd0b5d30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a08315d62da00451cae311b99d62f98c1cbcb35b52e47b54add19c60fd0b5d30","first_computed_at":"2026-07-05T11:18:12.931192Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:12.931192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WzLLChgTayqgJ8kT6lNnHYxmsCwxpmwwSRNq/T+hFpwDejs/YgY+wP/CkKxdTDJhvZs0nwnYJUHKQUMsasszAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:12.931678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.17458","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8ce91cdc40aed172016a88715219ccf7946d45670d88d0820c136ce0f6b9e00b","sha256:9c273ba479149b017a1e1e7363fe3360c5162576fffe9fc5641cef0fdfb47204"],"state_sha256":"6eb652666ef5e76d8d74f945e98c95a6a51a6314c8b632ae328fa6b81a42e493"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bJ/1jYFQ4rMypLcsRPOHNK2uVtcEUSrf0NdYnnUO1AXgqbrK2bhesFJWWCkaewkWuCTYxe0ObpbIr7j3uYw9AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T16:43:11.026414Z","bundle_sha256":"7feff957ad0470927ae02e4b024d3466104388f66816625c53e305bfa8f404ca"}}