{"as_of":"2026-08-10T12:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32cebe5f5b62d790ef2c1fde7a0e03e42bcd93a4e6c8ac7cf8646a3c2c3df41d","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:44:15.779107Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.07527/citation-record","integrity":"/paper/2507.07527/integrity","json":"/paper/2507.07527/citation-record.json","paper":"/paper/2507.07527"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.306005Z","title":"Flood Detection with SAR: A re- view of Techniques and Datasets","venue":null,"work_id":"48cd1207-7ab5-46bd-bf5a-b17c6904aaec","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.615175Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:8f9dcc834547e364d22fe949eda1cf52ad3d6f28a8f479a3896ea2fed2bdb148","observation_id":"5f69ffe4-f176-4f33-8f95-04c38bc41823","resolution":{"observed_at":"2026-08-06T18:44:16.309006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.296455Z","title":"Multimodal Machine Learning: A Survey and Tax- onomy","venue":null,"work_id":"e5da9900-6a36-41f1-8431-abf2cfcdd73b","year":2018},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.619140Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:2053a50de87939c1079de8840f1db7c31af0dc7ee705229aede5d0053cca0007","observation_id":"86fa649d-2ca8-4ad8-81dc-415e95bea55a","resolution":{"observed_at":"2026-08-06T18:44:16.299690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-06T18:44:15.622616Z","title":"On the Opportunities and Risks of Foundation Models.arXiv preprint arXiv:2108.07258, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.622616Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:827501003f9d6007bd48451e34f393df06cbd38cdff880dc66f4ceb5f309558a","observation_id":"94d94513-e687-4ce4-a576-628bf7dbe76c","resolution":{"observed_at":"2026-08-06T18:44:15.622616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-06T18:44:15.626200Z","title":"Language Models are Few-shot Learners","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.626200Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:4ad281cd36db51318ab98f91d7be7424dbefc6cf9ce8e62f50da5cd601e56326","observation_id":"9bcebdad-e93c-457b-8f21-e7b351bcbb88","resolution":{"observed_at":"2026-08-06T18:44:15.626200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.286540Z","title":"CAL FIRE Incidents","venue":null,"work_id":"656c4fae-ef2b-49cc-8a91-0a2f69ffeedb","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.629683Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:f09aee5f4507baf5c03d38e85c66880ad64e49d1aec870b5ac04ce269d4f500b","observation_id":"c3a1af33-37a3-4b2b-be1f-5bf5453e9d8f","resolution":{"observed_at":"2026-08-06T18:44:16.289645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.277254Z","title":"Cali- fornia Fire Perimeters (all)","venue":null,"work_id":"46375235-b266-477f-9680-f6a452413ff7","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.632867Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:9c22452d20d6e8175469dc01137042d772f232b1a0b4a62a7198560755e056a1","observation_id":"d3ba832c-78c0-4ebd-9eef-6bace2cbf933","resolution":{"observed_at":"2026-08-06T18:44:16.280259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.267600Z","title":"MV-MOE: A Visual Mixture-of-Experts Model for Optical-SAR Image Match- ing","venue":null,"work_id":"92f4f869-fbbf-4ccc-a414-5c1034d25f7a","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.636259Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:40b83881bbf18555884fe9256963cb8efb6a0007eb1bc4086a3736c488b16d01","observation_id":"12531476-27c5-4625-86ff-b04114163c8c","resolution":{"observed_at":"2026-08-06T18:44:16.270608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.257544Z","title":"Big Data for Remote Sensing: Challenges and Opportunities","venue":null,"work_id":"d618ddc7-0887-4cd5-b69c-c10bdd3475c7","year":2016},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.640357Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:6f8af4296a171b0302cf4ca2bfbee1d11d0acd24075f282c423ab706d0ed928a","observation_id":"f1146e84-1fad-4347-868b-059aa97b1fdd","resolution":{"observed_at":"2026-08-06T18:44:16.260835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.247979Z","title":"Com- parison of Burn Severity Assessments using Differenced Normalized Burn Ratio and Ground Data","venue":null,"work_id":"02bfd882-1f14-42a7-83a6-4a1b11a3a59e","year":2005},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.643455Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:d21f6175e9e68cc5323426e44f92a809b6a93f83dd6593a4b9bc96ea662d9b67","observation_id":"58edb016-299a-4aa6-b508-0891f7279584","resolution":{"observed_at":"2026-08-06T18:44:16.251231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08051","last_updated":"2023-01-15T19:27:57Z","snapshot_observed_at":"2026-08-05T01:16:21.229241Z","submitted_at":"2022-07-17T01:35:29Z","title":"SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.08051","snapshot_observed_at":"2026-08-06T18:44:15.646501Z","title":"Burke, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.646501Z"},"links":{"cited_paper":"/paper/2207.08051","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:9f09e2f615e54663645ee7cd2bc48fb53a6c6802138a2f2cb58a9a0e75352860","observation_id":"2fd1fde2-0488-44d2-ad68-88bb8865b496","resolution":{"observed_at":"2026-08-06T18:44:15.646501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T18:44:15.649820Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.arXiv preprint arXiv:2010.11929, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.649820Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:1646d06e3a9bcee7738e600f8cff1245652d511896b035f9d8580503d1ebcd18","observation_id":"15d58791-0396-4ea5-82d1-40558943fcb8","resolution":{"observed_at":"2026-08-06T18:44:15.649820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.239288Z","title":"Sentinel-2: ESA’s optical high-resolution mission for GMES operational services","venue":null,"work_id":"e02ce6ff-8639-4c67-b75a-05738e58f3c1","year":2012},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.653355Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:aa3a4f5b28b0ca113cd7e5a1a868f352d2441b35dfd97b1158b745c96cbbaf32","observation_id":"ac4436c4-e07d-46ea-aaac-f55f028d5443","resolution":{"observed_at":"2026-08-06T18:44:16.242213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.230184Z","title":"Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity","venue":null,"work_id":"8c9b3073-26b4-44c4-bb62-5de6956c986d","year":2022},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.657210Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:ccbb5b041b47a79c9c1338b319c3a8b79166fe1e53919069de672cd5c80391be","observation_id":"5958ab31-3b25-4707-a182-527e576e1bc0","resolution":{"observed_at":"2026-08-06T18:44:16.233292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.220521Z","title":"Normalized burn ratio (NBR)","venue":null,"work_id":"c42c1c46-a918-453e-92ed-d0ba06ef8e30","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.659970Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:09ddfa6781962b4e0dcb0015ccaf1e35ee978e8678c9e4b3fa65fe9197615ea6","observation_id":"36669fc4-129c-4a4b-86c5-80f8af1966b1","resolution":{"observed_at":"2026-08-06T18:44:16.224001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18765","last_updated":"2024-06-26T21:30:41Z","snapshot_observed_at":"2026-07-06T18:37:39.282367Z","submitted_at":"2024-06-26T21:30:41Z","title":"WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images","version":1},"cited_work":{"arxiv_id":"2406.18765","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.18765","snapshot_observed_at":"2026-08-06T18:44:15.933233Z","title":"WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images","venue":"cs.LG","work_id":"48f854e6-805d-4404-83bf-90853f588090","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.662726Z"},"links":{"cited_paper":"/paper/2406.18765","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:37a4db62e7888b2e816aca48477926cca5d08df795d7ec6e0e1650671b8c54e4","observation_id":"2d3d644a-3ff7-4907-b278-c3aaf8bdff2c","resolution":{"observed_at":"2026-08-06T18:44:15.936863Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.211127Z","title":"Skysense: A Multi-modal Remote Sensing Foundation Model Towards Universal Interpreta- tion for Earth Observation Imagery","venue":null,"work_id":"0c946228-aba6-4b39-9a5e-727f23973a1f","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.665741Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:67df0b1db04aba58980c68e8e066d39752b29097fc013d3b8daf20a28cc212ae","observation_id":"81c773e0-c104-4218-acad-66117bba1861","resolution":{"observed_at":"2026-08-06T18:44:16.214590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.201655Z","title":"Multisensory Geospatial Models via Cross-Sensor Pre- training","venue":null,"work_id":"76074d80-8c47-43fa-b663-055a8d4f1016","year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.668665Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:007faec15abcde4e1095374ce894fffb9e8cef5802bb156928cfbdcdea75f99f","observation_id":"d9090562-2f97-4f72-bfcd-59316c513a66","resolution":{"observed_at":"2026-08-06T18:44:16.205076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.192172Z","title":"Sparse multimodal vision transformer for weakly supervised seman- tic segmentation","venue":null,"work_id":"308a577c-f3fd-4309-a408-ab3d9a9ececa","year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.671771Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:29d6fbc47b1d8dc24e2185100ae2203a98d701cba694ae459fabb0fac534e2aa","observation_id":"d869e519-7b58-40d1-a277-765d2a3e5769","resolution":{"observed_at":"2026-08-06T18:44:16.195566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.182255Z","title":"Masked Autoencoders are Scal- able Vision Learners","venue":null,"work_id":"0d1f0d93-d32d-4fba-8fb3-f8114f159fe1","year":2022},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.674471Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:27ba8bf55fa72bed7ef4acc05fe25e3076311111aef56ac60ab892b01c5690b1","observation_id":"742d8ee9-9afa-4dc2-aa75-f225e61d4b83","resolution":{"observed_at":"2026-08-06T18:44:16.186260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T18:44:15.677507Z","title":"Distilling the Knowledge in a Neural Net- work","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.677507Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:02b358a980375fd18207e80faee58a45eea43cc9bf424c2236a871eaad6b5b97","observation_id":"ff982127-84c5-4a94-bb21-49368d836327","resolution":{"observed_at":"2026-08-06T18:44:15.677507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.173202Z","title":"SpectralGPT: Spectral Remote Sensing Foun- dation Model","venue":null,"work_id":"085b3829-3b02-482c-869e-532364cd8239","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.680237Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:daad6d262da785b4c1be2c94b6cf05742eff33116ad676379cd2513f7af86b4c","observation_id":"2d72b1e8-70fd-440e-9838-37e74dd75207","resolution":{"observed_at":"2026-08-06T18:44:16.176175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-07T07:43:16.294957Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-06T18:44:15.683174Z","title":"LoRA: Low-rank Adaptation of Large Language Models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.683174Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:996f8827ed00099cbe236941176e221d1fd01f86cca113ff5788024783050daf","observation_id":"4da39e5c-a0be-4b95-b063-3cc4206fb5c7","resolution":{"observed_at":"2026-08-06T18:44:15.683174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04894","last_updated":"2023-11-08T18:55:24Z","snapshot_observed_at":"2026-07-06T16:44:51.557703Z","submitted_at":"2023-11-08T18:55:24Z","title":"DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04894","snapshot_observed_at":"2026-08-06T18:44:15.686003Z","title":"Damex: Dataset-aware mixture-of-experts for visual understanding of mixture-of-datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.686003Z"},"links":{"cited_paper":"/paper/2311.04894","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:a4f72e1487c3273a537eaa0599586fbe9b4932cc3752d1daf97b7234016482c0","observation_id":"24f25c98-67c6-44d4-8fd6-baaa7f60b5cf","resolution":{"observed_at":"2026-08-06T18:44:15.686003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-06T18:44:15.689377Z","title":"Scaling Laws for Neural Language Models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.689377Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:0ef70c59c7bb1a7fedce346b0bd866e9b8089474fcc5470fa647e0697be7f17d","observation_id":"dc3ede29-af26-4f57-8d9e-30906a22e6ce","resolution":{"observed_at":"2026-08-06T18:44:15.689377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.163935Z","title":"Segment Any- thing","venue":null,"work_id":"a8fd37f8-6f59-44e7-af34-430411b9cb9a","year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.693273Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:aba52a1b50a05d157f397f5ffd8e5275490d30c7898222e763f7ee395046f6da","observation_id":"fd7a8ef5-e5b8-4c43-89f1-59ef25d39026","resolution":{"observed_at":"2026-08-06T18:44:16.166988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.154355Z","title":"Multimodal Foundation Models: From Specialists to General-purpose Assistants","venue":null,"work_id":"160c3bfd-c5cd-47b0-ab19-115ec104dc7f","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.696240Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:2d681ddf027cd9f73c8d74ff9d717423b8316e4e842c8e517fde4678b482c67d","observation_id":"888a99e1-e8b7-480a-8a86-ef806922015b","resolution":{"observed_at":"2026-08-06T18:44:16.157415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.144146Z","title":"Remote Sensing and Image Interpretation","venue":null,"work_id":"20367a0d-b29c-4c23-a0c0-f46a665edd14","year":2015},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.699140Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:37df1838fcafcb3e823b768c05265de245789d9ea0e2ddc57dad794f9b9089b2","observation_id":"b2226105-b63b-4668-8340-d2cf25b8bdf0","resolution":{"observed_at":"2026-08-06T18:44:16.147755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01595","last_updated":"2025-02-10T19:14:09Z","snapshot_observed_at":"2026-07-06T19:44:16.540551Z","submitted_at":"2024-11-03T15:05:49Z","title":"RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering","version":2},"cited_work":{"arxiv_id":"2411.01595","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.01595","snapshot_observed_at":"2026-08-06T18:44:15.877252Z","title":"RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering","venue":"cs.CV","work_id":"3d7e3f1c-e34c-4e74-bafa-4a2ca6889879","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.702187Z"},"links":{"cited_paper":"/paper/2411.01595","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:eb67bd942b3d4d71cfcde6d7457b9de80dc6adc1862c4545196ebbeec50ce96e","observation_id":"65c5ec45-8f17-4e75-acde-e2ec27ec8918","resolution":{"observed_at":"2026-08-06T18:44:15.884127Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21770","last_updated":"2024-08-12T16:20:37Z","snapshot_observed_at":"2026-08-09T08:07:42.316346Z","submitted_at":"2024-07-31T17:46:51Z","title":"MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21770","snapshot_observed_at":"2026-08-06T18:44:15.705658Z","title":"MoMa: Efficient Early-fusion Pre-training with Mixture of Modality-aware Experts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.705658Z"},"links":{"cited_paper":"/paper/2407.21770","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:043762048c59dd4f1d015a1f290baaa9c8c15fdad3ba114f12cee0384c55a3f4","observation_id":"24150172-6649-4036-bbcf-77b5b22c47d7","resolution":{"observed_at":"2026-08-06T18:44:15.705658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.135174Z","title":"Decoupled Weight De- cay Regularization","venue":null,"work_id":"151c397f-a7da-4b0f-950d-ae977c37fa3a","year":2017},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.708801Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:227cebd42342192dd498f2fd945c1cc21e01fa7353154bfccb0442f87d7afcc1","observation_id":"c3fc4361-aa2b-4515-b652-e06b8b4694f5","resolution":{"observed_at":"2026-08-06T18:44:16.138266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03464","last_updated":"2025-02-11T22:29:52Z","snapshot_observed_at":"2026-07-06T18:57:39.564455Z","submitted_at":"2024-08-06T22:39:34Z","title":"Vision Foundation Models in Remote Sensing: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03464","snapshot_observed_at":"2026-08-06T18:44:15.711701Z","title":"AI Foundation Models in Re- mote Sensing: A Survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.711701Z"},"links":{"cited_paper":"/paper/2408.03464","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:347d01d1fd4d66da7fc9c5592ecbe8e317a50b9fc7f35f6f9b4a4235acacb2d6","observation_id":"f1672302-2f68-405d-b802-b840027be999","resolution":{"observed_at":"2026-08-06T18:44:15.711701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.125744Z","title":"Ben-ge: Extending BigEarthNet with geographical and environmen- tal data","venue":null,"work_id":"545c4a92-f831-48c7-b550-b42131245822","year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.715688Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:b60799d92d4aa87bcab554e63e653e2b901c3b9f823af31dfc25ff46a870ff7b","observation_id":"3d9cb11a-0937-453d-a547-7e128c45ac28","resolution":{"observed_at":"2026-08-06T18:44:16.129030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02771","last_updated":"2024-07-29T10:35:50Z","snapshot_observed_at":"2026-08-06T10:55:27.379554Z","submitted_at":"2024-05-04T23:16:48Z","title":"MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.02771","snapshot_observed_at":"2026-08-06T18:44:15.721344Z","title":"Belongie, Christian Igel, and Nico Lang","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.721344Z"},"links":{"cited_paper":"/paper/2405.02771","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:112e731e9eeb52cb10163e3074a6a7458922c1e04cd76cdfe1cb57159187e033","observation_id":"e93899d6-2853-41c5-ae8f-f3841083efcb","resolution":{"observed_at":"2026-08-06T18:44:15.721344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.115845Z","title":"Learning Transferable Visual Models from Natural Language Super- vision","venue":null,"work_id":"6c1feb9c-d840-4ce3-a361-3f11bb58698d","year":2021},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.724141Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:fa7e46af3470994f3d45eba92037776e5e7f4656f4d6a7a8712bbd2501388f9f","observation_id":"15cf0d9b-1271-428f-8636-6f8b520aa00e","resolution":{"observed_at":"2026-08-06T18:44:16.118857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.105874Z","title":"Sen12-flood: a SAR and Multispectral Dataset for Flood Detection","venue":null,"work_id":"80ba27ed-5bb4-4826-b390-db1e5f783085","year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.727522Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:2c994bde52e963dce9dc4b513610b757a7b770e1a9edbb7d6a06f65b939dea26","observation_id":"3164c0d8-809e-4ce4-91fe-1a2887ed6974","resolution":{"observed_at":"2026-08-06T18:44:16.110024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.096023Z","title":"Scale-MAE: A Scale- Aware Masked Autoencoder for Multiscale Geospatial Rep- resentation Learning","venue":null,"work_id":"77e7dd24-0099-4ec4-a084-c175d3e1aa96","year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.730563Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:a4d45e34c396817e25f700302d499da31485db3f17970c38f9b882255ad1c779","observation_id":"aea04bbd-11e9-4bd7-9c19-4a6b4b9944f1","resolution":{"observed_at":"2026-08-06T18:44:16.099300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.087216Z","title":"Scaling Vision with Sparse Mix- ture of Experts","venue":null,"work_id":"8b1026ea-4099-47e7-9f41-9e418741c88b","year":2021},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.733693Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:1912e91d420695085fc313f83d81fbb452dc633232326c192634740713892208","observation_id":"388d5fb9-54d2-4514-a1a6-6997192a1bef","resolution":{"observed_at":"2026-08-06T18:44:16.090060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04597","last_updated":"2015-05-18T11:28:37Z","snapshot_observed_at":"2026-08-06T11:05:16.105361Z","submitted_at":"2015-05-18T11:28:37Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04597","snapshot_observed_at":"2026-08-06T18:44:15.736796Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.736796Z"},"links":{"cited_paper":"/paper/1505.04597","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:f63842686f3ea9a5f8b47fa65fade74d69f39c4bc496f0ab72bb2b55376938d3","observation_id":"d3ee1c23-2075-44cd-82eb-04947f6ad775","resolution":{"observed_at":"2026-08-06T18:44:15.736796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.077975Z","title":"Self-supervised Vision Transformers for Land-cover Segmentation and Classification","venue":null,"work_id":"94ad52c6-2dd4-4fca-a790-2e907f5ec494","year":2022},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.739533Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:ec3121e043197e8917d1fdf2dc344b584d502c2b3eb315853bd3fa029f3e5461","observation_id":"b4df59ac-8787-423c-8d55-b1d94abf56af","resolution":{"observed_at":"2026-08-06T18:44:16.081100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-07-06T05:27:13.416519Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-06T18:44:15.742690Z","title":"Outrageously Large Neural Networks: The Sparsely-gated Mixture-of-Experts Layer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.742690Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:44e4adba9b68de9d13e2f9e4be32d1d52ac361e025c114a57ad37f11c3e28877","observation_id":"fcb11f83-624e-41ef-8a9f-2e6ba52ded8e","resolution":{"observed_at":"2026-08-06T18:44:15.742690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.068623Z","title":"Neural Net Pruning-Why and How","venue":null,"work_id":"d198aed3-3b54-4963-b0c5-fbbb369c0fa7","year":1988},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.746057Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:ef3592b08bec8a649f802100a46ad914052e0ca050a00b9d2124149501902fe4","observation_id":"f9111178-fda7-4a0b-bfae-deb9e38aa7e8","resolution":{"observed_at":"2026-08-06T18:44:16.071545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.059053Z","title":"Ap- plications of Remote Sensing in Precision Agriculture: A Review","venue":null,"work_id":"4e91a670-c377-4118-a84e-03c3a3d50463","year":2020},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.748807Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:51268802a3ebf88868d72b781de7367d2f44d47b83ef92a9080c1a7c6c86ce9e","observation_id":"02697eee-a0e3-4e37-a8ff-92ece962a9c5","resolution":{"observed_at":"2026-08-06T18:44:16.061876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.049821Z","title":null,"venue":null,"work_id":"ecc8b5f5-d59b-4d46-9ae9-a0d5ff8ef5ec","year":2021},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.752202Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:955c7c7893653fdf14db6ff8648e6836d923e127a907a32e4d8bd6edc6295486","observation_id":"9dd143b2-0f94-402d-a478-d3b389b4c4c7","resolution":{"observed_at":"2026-08-06T18:44:16.053043Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.040950Z","title":"Remote Sensing Plat- forms and Sensors: A Survey","venue":null,"work_id":"0db065d3-ccee-4a29-9440-93ccf2eadf9f","year":2016},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.755195Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:1c947e701920306138c54c5fe0a7e94fe8a849d9f9775f9c8af0a684200e246c","observation_id":"e528898e-445b-488e-9e2e-29b99d85bf56","resolution":{"observed_at":"2026-08-06T18:44:16.044084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.031602Z","title":"Attention is all you need","venue":null,"work_id":"40cae8be-738c-4a21-ab35-99e917eeed3f","year":2017},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.757790Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:ce9498441f09a4f84ba014c567c47c17bc1cd7409f32c59c0d3df42793036af2","observation_id":"2bc68cd1-6b52-4097-a875-df7cde3e0ff6","resolution":{"observed_at":"2026-08-06T18:44:16.034796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11519","last_updated":"2025-04-01T15:14:22Z","snapshot_observed_at":"2026-08-10T03:58:29.121390Z","submitted_at":"2024-06-17T13:22:58Z","title":"HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11519","snapshot_observed_at":"2026-08-06T18:44:15.760450Z","title":"HyperSIGMA: Hyperspectral Intelligence Comprehen- sion Foundation Model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.760450Z"},"links":{"cited_paper":"/paper/2406.11519","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:d0adcaa8dc4231ce6eaf297b292b26d9676fbc701ee0f1848839b249573e4f84","observation_id":"a47c140c-a306-49b5-ad2d-cc42dcf3fab8","resolution":{"observed_at":"2026-08-06T18:44:15.760450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16602","last_updated":"2025-06-03T02:58:06Z","snapshot_observed_at":"2026-08-09T20:01:08.885349Z","submitted_at":"2024-10-22T01:08:21Z","title":"Foundation Models for Remote Sensing and Earth Observation: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16602","snapshot_observed_at":"2026-08-06T18:44:15.763997Z","title":"Foundation Models for Remote Sensing and Earth Observation: A Sur- vey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.763997Z"},"links":{"cited_paper":"/paper/2410.16602","citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:65f282a2a47e8370448ef1da1106ed4ea195b2c1cb60e65ee409c3d45be89b31","observation_id":"65677ff6-b954-45ec-b3b0-c5fcfe0008c4","resolution":{"observed_at":"2026-08-06T18:44:15.763997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.022074Z","title":"Stewart, Joelle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu","venue":null,"work_id":"fca723b4-606a-46b2-a048-d47ca1ce22ff","year":2024},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.767498Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:c8fb17a78e7fedaec6badef3885e5c95e907a07273ec380fd375993634175c54","observation_id":"65ac1106-c2b3-42e0-9b2f-8a282542be4d","resolution":{"observed_at":"2026-08-06T18:44:16.025242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.011937Z","title":"RingMo-SAM: A Foundation Model for Segment Any- thing in Multimodal Remote-sensing Images","venue":null,"work_id":"5a36db6d-9670-4459-bfc6-1187bcd0dfed","year":2023},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.770367Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:a900ab90e5a2932aa603a6b1cfed0b828865c92065f261634d0b7fe98c3ee5f5","observation_id":"61807f48-f92c-4fb8-8a8c-6eb013cfba6f","resolution":{"observed_at":"2026-08-06T18:44:16.015012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:16.001863Z","title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","venue":null,"work_id":"3225abd4-5a79-4228-a03e-2d91067b0baa","year":2017},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.773343Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:6615ec05233462df07793513e5b453bab35fa1d7e272862d8643ebabdcf15fb9","observation_id":"f335e623-ee61-4bb9-afee-088041212ef8","resolution":{"observed_at":"2026-08-06T18:44:16.005243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:15.992362Z","title":"In each k-shot experiment, we randomly select k samples for every class from the training set","venue":null,"work_id":"5a37b99c-03f9-495e-99d8-c69b2b298bcb","year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.776344Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:e5065634883384fba0d0a844e30e4c1bb1e5aac190df16c4623cad932e570dea","observation_id":"9316fa16-2fa6-465a-80d2-6426a9de878b","resolution":{"observed_at":"2026-08-06T18:44:15.995520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:44:15.982319Z","title":null,"venue":null,"work_id":"394df8f5-8e0d-4fe9-a85d-5d34b300ad4e","year":null},"citing_paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T18:44:15.779107Z"},"links":{"citing_paper":"/paper/2507.07527"},"observation_digest":"sha256:484634619d510597dcd76708cc43927932d1832e8b1e60a459eddbd121f3e76a","observation_id":"e8275768-f1a3-4dc1-b379-19dab1e100d9","resolution":{"observed_at":"2026-08-06T18:44:15.985705Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.07527","last_updated":"2025-07-10T08:19:34Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T22:48:30.017559Z","submitted_at":"2025-07-10T08:19:34Z","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":33},"total_outbound_references":52},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.07527."}