{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PGBVIA332C6SWYR3JDAEVPX6VG","short_pith_number":"pith:PGBVIA33","schema_version":"1.0","canonical_sha256":"798354037bd0bd2b623b48c04abefea99df5f8761651eb881107bb5ec3560059","source":{"kind":"arxiv","id":"2508.07020","version":1},"attestation_state":"computed","paper":{"title":"TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdul Matin, Sangmi Lee Pallickara, Shrideep Pallickara, Tanjim Bin Faruk","submitted_at":"2025-08-09T15:32:22Z","abstract_excerpt":"Hyperspectral satellite imagery offers sub-30 m views of Earth in hundreds of contiguous spectral bands, enabling fine-grained mapping of soils, crops, and land cover. While self-supervised Masked Autoencoders excel on RGB and low-band multispectral data, they struggle to exploit the intricate spatial-spectral correlations in 200+ band hyperspectral images. We introduce TerraMAE, a novel HSI encoding framework specifically designed to learn highly representative spatial-spectral embeddings for diverse geospatial analyses. TerraMAE features an adaptive channel grouping strategy, based on statis"},"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":"2508.07020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-09T15:32:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0fcc4bbf0b3c2042729b02af4fcf963f5ad2bde1f43c8422b84730563d8add1d","abstract_canon_sha256":"8a4cd6a439f0b86d4054cb0b9bb20caba09d279dfc0900c3ada83d987304594d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:39.389674Z","signature_b64":"XcpB8OPfJB/OeeUQXj4TI8YgY22/9n+jbcOwevBG/om6uuLwfLjn+Q8HftlTjPmsjDZ6RCh4FgtI/UtIfOp/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"798354037bd0bd2b623b48c04abefea99df5f8761651eb881107bb5ec3560059","last_reissued_at":"2026-07-05T11:51:39.389253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:39.389253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdul Matin, Sangmi Lee Pallickara, Shrideep Pallickara, Tanjim Bin Faruk","submitted_at":"2025-08-09T15:32:22Z","abstract_excerpt":"Hyperspectral satellite imagery offers sub-30 m views of Earth in hundreds of contiguous spectral bands, enabling fine-grained mapping of soils, crops, and land cover. While self-supervised Masked Autoencoders excel on RGB and low-band multispectral data, they struggle to exploit the intricate spatial-spectral correlations in 200+ band hyperspectral images. We introduce TerraMAE, a novel HSI encoding framework specifically designed to learn highly representative spatial-spectral embeddings for diverse geospatial analyses. TerraMAE features an adaptive channel grouping strategy, based on statis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.07020","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/2508.07020/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":"2508.07020","created_at":"2026-07-05T11:51:39.389311+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.07020v1","created_at":"2026-07-05T11:51:39.389311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.07020","created_at":"2026-07-05T11:51:39.389311+00:00"},{"alias_kind":"pith_short_12","alias_value":"PGBVIA332C6S","created_at":"2026-07-05T11:51:39.389311+00:00"},{"alias_kind":"pith_short_16","alias_value":"PGBVIA332C6SWYR3","created_at":"2026-07-05T11:51:39.389311+00:00"},{"alias_kind":"pith_short_8","alias_value":"PGBVIA33","created_at":"2026-07-05T11:51:39.389311+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/PGBVIA332C6SWYR3JDAEVPX6VG","json":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG.json","graph_json":"https://pith.science/api/pith-number/PGBVIA332C6SWYR3JDAEVPX6VG/graph.json","events_json":"https://pith.science/api/pith-number/PGBVIA332C6SWYR3JDAEVPX6VG/events.json","paper":"https://pith.science/paper/PGBVIA33"},"agent_actions":{"view_html":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG","download_json":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG.json","view_paper":"https://pith.science/paper/PGBVIA33","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.07020&json=true","fetch_graph":"https://pith.science/api/pith-number/PGBVIA332C6SWYR3JDAEVPX6VG/graph.json","fetch_events":"https://pith.science/api/pith-number/PGBVIA332C6SWYR3JDAEVPX6VG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG/action/storage_attestation","attest_author":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG/action/author_attestation","sign_citation":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG/action/citation_signature","submit_replication":"https://pith.science/pith/PGBVIA332C6SWYR3JDAEVPX6VG/action/replication_record"}},"created_at":"2026-07-05T11:51:39.389311+00:00","updated_at":"2026-07-05T11:51:39.389311+00:00"}