{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HPE7WSHVDX5D7ZUJGMSM4WO7OT","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":"23a089a997797e27fe915cecf9631820b1b7bdd0b26fdc7240fcade191ea022b","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-03T03:40:24Z","title_canon_sha256":"96f49d8f9688636a4ec39d9442db5c9e63acb4b25255a70cf4e753aeb5bbf913"},"schema_version":"1.0","source":{"id":"2103.02155","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.02155","created_at":"2026-07-05T02:20:01Z"},{"alias_kind":"arxiv_version","alias_value":"2103.02155v1","created_at":"2026-07-05T02:20:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.02155","created_at":"2026-07-05T02:20:01Z"},{"alias_kind":"pith_short_12","alias_value":"HPE7WSHVDX5D","created_at":"2026-07-05T02:20:01Z"},{"alias_kind":"pith_short_16","alias_value":"HPE7WSHVDX5D7ZUJ","created_at":"2026-07-05T02:20:01Z"},{"alias_kind":"pith_short_8","alias_value":"HPE7WSHV","created_at":"2026-07-05T02:20:01Z"}],"graph_snapshots":[{"event_id":"sha256:bada7cfa188734d844c3da01ac21bf3b0310d91f17e6406f5846f422aeff6f40","target":"graph","created_at":"2026-07-05T02:20:01Z","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/2103.02155/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This study marks the first attempt to cross-compare performances of popular state-of-the-art deep learning models in estimating population distribution from remote sensing images, investigate the contribution of neighboring effect, and explore the potential systematic population estimation biases. We conduct an end-to-end trainin","authors_text":"Di Zhu, Fan Zhang, Lei Zou, Tao Liu, Xiao Huang, Xiao Li","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-03T03:40:24Z","title":"Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.02155","kind":"arxiv","version":1},"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":"sha256:6caefb7d7bfb6c55a954e8f23233bba69a1f7345ecfaab54a654baa6b8f2d104","target":"record","created_at":"2026-07-05T02:20:01Z","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":"23a089a997797e27fe915cecf9631820b1b7bdd0b26fdc7240fcade191ea022b","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-03T03:40:24Z","title_canon_sha256":"96f49d8f9688636a4ec39d9442db5c9e63acb4b25255a70cf4e753aeb5bbf913"},"schema_version":"1.0","source":{"id":"2103.02155","kind":"arxiv","version":1}},"canonical_sha256":"3bc9fb48f51dfa3fe6893324ce59df74cd84ccbc15445150da14c975694b1a9c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3bc9fb48f51dfa3fe6893324ce59df74cd84ccbc15445150da14c975694b1a9c","first_computed_at":"2026-07-05T02:20:01.146837Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:20:01.146837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jNd7BF8tM6rVpyeZGdvmPdcDhg3+J7iVyUykQ7spqlHTabY9sfL0BAT0FFzkgTDq7d2QzjqJXwRC0Ap3xh8bCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:20:01.147350Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.02155","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6caefb7d7bfb6c55a954e8f23233bba69a1f7345ecfaab54a654baa6b8f2d104","sha256:bada7cfa188734d844c3da01ac21bf3b0310d91f17e6406f5846f422aeff6f40"],"state_sha256":"7522f238eb68e289c0688ec58bef65656f152afd029352d8983cf4029cb7b554"}