{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UT6AFGCWXYYKVO5R37D4LEYFHC","short_pith_number":"pith:UT6AFGCW","schema_version":"1.0","canonical_sha256":"a4fc029856be30aabbb1dfc7c5930538a090b05820b93f1bbe484732c91a56c3","source":{"kind":"arxiv","id":"2601.14637","version":2},"attestation_state":"computed","paper":{"title":"Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.HC"],"primary_cat":"cs.CV","authors_text":"Ce Zhang, James Brock, Nantheera Anantrasirichai","submitted_at":"2026-01-21T04:23:33Z","abstract_excerpt":"The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central challenges in this domain are pixel-level change detection and semantic change interpretation, particularly for complex forest dynamics. While large language models (LLMs) are increasingly adopted for data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored, especially beyond urban environments. This paper introduces Forest-Chat,"},"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":"2601.14637","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-21T04:23:33Z","cross_cats_sorted":["cs.AI","cs.CL","cs.HC"],"title_canon_sha256":"5454d9450b25b15117911ecadbc1c69447ab99b1e084f7c0b723903a951331f2","abstract_canon_sha256":"27afacaf1cf9add6ceb4ea9a6ce79f5c79b16dcfed7c88d5d3c321dbf37b9e19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:05:06.246142Z","signature_b64":"OOxLdJuB9/odJNEOc819aAvkSccLviKhcpnpFm0sB9hVM1aUxumNsSeXWCKqzfbRjoT+BVGVi24bM2z/Gw8NCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4fc029856be30aabbb1dfc7c5930538a090b05820b93f1bbe484732c91a56c3","last_reissued_at":"2026-06-08T01:05:06.245202Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:05:06.245202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.HC"],"primary_cat":"cs.CV","authors_text":"Ce Zhang, James Brock, Nantheera Anantrasirichai","submitted_at":"2026-01-21T04:23:33Z","abstract_excerpt":"The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central challenges in this domain are pixel-level change detection and semantic change interpretation, particularly for complex forest dynamics. While large language models (LLMs) are increasingly adopted for data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored, especially beyond urban environments. This paper introduces Forest-Chat,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.14637","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/2601.14637/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":"2601.14637","created_at":"2026-06-08T01:05:06.245337+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.14637v2","created_at":"2026-06-08T01:05:06.245337+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.14637","created_at":"2026-06-08T01:05:06.245337+00:00"},{"alias_kind":"pith_short_12","alias_value":"UT6AFGCWXYYK","created_at":"2026-06-08T01:05:06.245337+00:00"},{"alias_kind":"pith_short_16","alias_value":"UT6AFGCWXYYKVO5R","created_at":"2026-06-08T01:05:06.245337+00:00"},{"alias_kind":"pith_short_8","alias_value":"UT6AFGCW","created_at":"2026-06-08T01:05:06.245337+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/UT6AFGCWXYYKVO5R37D4LEYFHC","json":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC.json","graph_json":"https://pith.science/api/pith-number/UT6AFGCWXYYKVO5R37D4LEYFHC/graph.json","events_json":"https://pith.science/api/pith-number/UT6AFGCWXYYKVO5R37D4LEYFHC/events.json","paper":"https://pith.science/paper/UT6AFGCW"},"agent_actions":{"view_html":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC","download_json":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC.json","view_paper":"https://pith.science/paper/UT6AFGCW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.14637&json=true","fetch_graph":"https://pith.science/api/pith-number/UT6AFGCWXYYKVO5R37D4LEYFHC/graph.json","fetch_events":"https://pith.science/api/pith-number/UT6AFGCWXYYKVO5R37D4LEYFHC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC/action/storage_attestation","attest_author":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC/action/author_attestation","sign_citation":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC/action/citation_signature","submit_replication":"https://pith.science/pith/UT6AFGCWXYYKVO5R37D4LEYFHC/action/replication_record"}},"created_at":"2026-06-08T01:05:06.245337+00:00","updated_at":"2026-06-08T01:05:06.245337+00:00"}