{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:08:52Z","timestamp":1779253732172,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of  China","award":["2025YFE0212800"],"award-info":[{"award-number":["2025YFE0212800"]}]},{"name":"Geological Survey Program of China","award":["DD20251300201"],"award-info":[{"award-number":["DD20251300201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gully type debris flows are sudden, highly destructive geological hazards requiring accurate, real-time monitoring for effective early warning. However, single-modal visual monitoring is sensitive to complex environments, while existing multi-modal fusion approaches rely on static strategies, limiting adaptability and modal complementarity. Blurred boundary segmentation, class imbalance, and real-time deployment challenges also remain unaddressed. To overcome these issues, this study proposes a cross-modal dynamic feature fusion framework integrating visible and infrared imagery, consisting of a shared backbone for multi-scale feature extraction, a dynamic feature aggregation module for adaptive heterogeneous fusion, a lightweight context-aware semantic segmentation network, and a composite loss function to enhance boundary delineation and mitigate class imbalance. Validated on a self-constructed dual-modal debris flow dataset and public benchmarks, the method achieves an mIoU of 75.6%, outperforming state-of-the-art methods by 3.1%. It meets real-time monitoring requirements and exhibits strong generalization, providing a practical solution for debris flow monitoring with great potential for disaster early warning deployment.<\/jats:p>","DOI":"10.3390\/ijgi15050209","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:39:32Z","timestamp":1778513972000},"page":"209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Modal Dynamic Feature Fusion for Visible-Infrared Debris Flow Segmentation"],"prefix":"10.3390","volume":"15","author":[{"given":"Mingzhi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Safety Science, Institute of Public Safety Research, Tsinghua University, Beijing 100084, China"},{"name":"China Institute of Geo-Environment Monitoring, Beijing 100081, China"},{"name":"China-Uganda Belt and Road Joint Laboratory on Natural Disaster Monitoring and Early Warning, Institute of Public Safety Research, Tsinghua University (Hefei), Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyong","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Safety Science, Institute of Public Safety Research, Tsinghua University, Beijing 100084, China"},{"name":"China-Uganda Belt and Road Joint Laboratory on Natural Disaster Monitoring and Early Warning, Institute of Public Safety Research, Tsinghua University (Hefei), Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongri","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610213, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5177-6808","authenticated-orcid":false,"given":"Chun","family":"Bao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610213, China"},{"name":"Zhiyang Innovation Technology Co., Ltd., Zibo 255086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"Zhiyang Innovation Technology Co., Ltd., Zibo 255086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhikun","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhiyang Innovation Technology Co., Ltd., Zibo 255086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2020GL090874","DOI":"10.1029\/2020GL090874","article-title":"Machine Learning Improves Debris Flow Warning","volume":"48","author":"Chmiel","year":"2021","journal-title":"Geophys. 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