{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:25:41Z","timestamp":1779906341315,"version":"3.53.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>The multi-modality remote sensing foundation model (MM-RSFM) has made notable progress recently. However, most existing approaches remain limited to medium-resolution, single-modality, restricting their performance in fine-grained downstream applications such as disaster response and urban planning. In this work, MaRS is proposed, a multi-modality very-high-resolution (VHR) remote sensing foundation model designed for cross-modality granularity interpretation of complex scenes. To achieve this, a multi-modality VHR SAR-optical dataset, MaRS-16M, is constructed through large-scale collection and semi-automated processing, comprising over 16 million paired samples. Unlike previous work, MaRS tackles two fundamental challenges in VHR SAR-optical self-supervised learning (SSL) techniques. Cross-granularity contrastive learning (CGCL) is introduced to alleviate alignment inconsistencies caused by imaging differences, and meta-modality attention (MMA) is designed to unify heterogeneous physical characteristics across modalities. Compared to existing remote sensing foundation models (RSFMs) and general vision foundation models (VFMs), MaRS performs better as a pre-trained backbone across nine multi-modality VHR downstream tasks.<\/jats:p>","DOI":"10.1609\/aaai.v40i14.38153","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:14:52Z","timestamp":1773792892000},"page":"11685-11693","source":"Crossref","is-referenced-by-count":2,"title":["MaRS: A Multi-modality Very-high-resolution Remote Sensing Foundation Model with Cross-Granularity Meta-Modality Learning"],"prefix":"10.1609","volume":"40","author":[{"given":"Ruoyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38153\/42115","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38153\/42115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:14:53Z","timestamp":1773792893000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i14.38153","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}