[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83418-en":3,"doc-seo-83418-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83418,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","LTM: Large-scale Terrain Model for Landscapes","Accurate 3D terrain mapping is vital for emergency response in wildfire-prone regions, yet coverage demands exceed conventional reconstruction methods. Airborne LiDAR delivers high-resolution data but remains costly and rarely updated. Image-based approaches are cheaper but fail under sparse visual features and limited overlap. This work proposes a multimodal reconstruction framework that uses legacy digital elevation models (DEMs) as geometric priors, with physics-based pixel-pixel alignment to avoid expensive feature matching, enabling real-time high-fidelity depth maps.","LTM: Large-scale Terrain Model for Landscapes  \nXiao Fu, Yue Hu, Meida Chen, Peter Anthony Beerel, Barath Raghavan  \nUniversity of Southern California  \narXiv :2607 .087 1 1v 1 [ cs .CV] 9 Jul 2026  \nAbstract  \nAccurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfireprone regions often span vast areas where conventional reconstruction methods underperform. Airborne LiDAR systems provide high-resolution terrain data, but they are expensive and infrequently updated. Image-based methods offer a lower-cost alternative, but struggle due to sparse visual features and limited image overlap. We propose a multimodal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for imagebased 3D reconstruction. Our key innovation is physics-based pixel-pixel alignment between images and DEM data, dramatically reducing computational complexity by eliminating expensive feature matching procedures. To validate our approach, we developed a large-terrain simulator based on areal wildfire-prone area, generating realistic images enabling a comprehensive evaluation. Given posed images and legacy DEMs, our method produces high-fidelity depth maps while maintaining real-time performance. We find significant improvements in reconstruction accuracy and computational efficiency over existing techniques, offering a scalable solution for wildfire response.  \nIntroduction  \nEnvironmental monitoring has yet to fully leverage the vast network of cameras already deployed in wildfireprone and other environmentally vulnerable regions. These camera systems capture rich spatio-temporal imagery that offers valuable insights into dynamic natural landscapes.  \nSuch imagery can help identify both gradual ecological changes, such as vegetation growth, and sudden environmental events, including avalanches (Barbolini et al. 2011), floods (Mudashiru et al. 2021), and wildfires (Fu et al. 2024) . Accurate mapping of such landscapes not only enhances disaster preparedness but also aids automated semantic analysis, including assessments of disaster intensity. In particular, fuel maps, which provide vegetation-related spatial semantics, are widely used in conjunction with terrain models to support wildfire propagation simulators such as FARSITE (Finney 1998) and FlamMap (Finney 2006) . In addition, 3D reconstruction techniques generate detailed repre-  \nCopyright © 2026, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \nFigure 1: A novel framework for large-scale dynamic terrain updates in end-to-end 3D semantic mapping.  \nsentations of natural terrain, which serve as the spatial foundation on which fuel maps are overlaid, enabling the extraction of timely and accurate insights that improve early warning systems and support more informed and responsive disaster management.  \nLandscape, terrain, and vegetation are traditionally captured using remote sensing methods using spacecraft (Hirano, Welch, and Lang 2003) or aircraft-based platforms (Dobrowski et al. 2008; Rodriguez and Aggarwal 2002) . However, the resulting 3D models are typically updated only on an annual basis, if that, due to the vast areas that must be scanned (Gorelick et al. 2017; Krishnanet al. 2011; Kervyn et al. 2007) . This infrequent update cycle is insufficient for effective disaster mitigation, as significant changes in vegetation and surface conditions can occur over seasonal or even monthly timescales (Yang, Meng, and Zhang 2011) . In contrast, ground-based camera networks offer higher temporal resolution, but lack the top-down, widearea coverage that is critical for comprehensive landscape monitoring.  \nWhile prior ground image-based 3D reconstruction methods have shown strong performance in urban environments, their effectiveness in wildfire-prone natural landscapes remains limited (Iglhaut et al. 2019) . These approaches typically focus on est","cbCaiiWIlVpvQMYl","https://ap.wps.com/l/cbCaiiWIlVpvQMYl","pdf",3679948,2,1,10,"English","en",105,"# Abstract\n# Introduction\n## Need for dynamic terrain updates in wildfire monitoring\n## Limits of remote sensing update frequency\n## Challenges for ground image-based 3D reconstruction in vegetated scenes\n## Related 3D representations and motivation for DEM-based modeling","[{\"question\":\"Why are accurate 3D terrain maps important for wildfire response?\",\"answer\":\"They support disaster preparedness and help automated semantic analysis, including fuel maps and terrain foundations for wildfire propagation simulators and early warning systems.\"},{\"question\":\"What are the main drawbacks of existing reconstruction approaches in wildfire-prone landscapes?\",\"answer\":\"Conventional methods underperform over vast areas; LiDAR is expensive and infrequently updated; image-based techniques struggle with sparse visual features, limited overlap, and vegetation-driven instability.\"},{\"question\":\"How does the proposed LTM framework improve reconstruction efficiency and accuracy?\",\"answer\":\"It leverages outdated DEMs as geometric priors and uses physics-based pixel-pixel alignment between images and DEM data, removing costly feature matching while producing high-fidelity depth maps with real-time performance.\"}]",1784187462,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ltm-large-scale-terrain-model-for-landscapes","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/ltm-large-scale-terrain-model-for-landscapes/83418/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are accurate 3D terrain maps important for wildfire response?","Question",{"text":75,"@type":76},"They support disaster preparedness and help automated semantic analysis, including fuel maps and terrain foundations for wildfire propagation simulators and early warning systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main drawbacks of existing reconstruction approaches in wildfire-prone landscapes?",{"text":80,"@type":76},"Conventional methods underperform over vast areas; LiDAR is expensive and infrequently updated; image-based techniques struggle with sparse visual features, limited overlap, and vegetation-driven instability.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed LTM framework improve reconstruction efficiency and accuracy?",{"text":84,"@type":76},"It leverages outdated DEMs as geometric priors and uses physics-based pixel-pixel alignment between images and DEM data, removing costly feature matching while producing high-fidelity depth maps with real-time performance.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":22,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]