{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:08:33Z","timestamp":1763471313930,"version":"3.45.0"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Engineering Technology Research Center for Ordinary Universities in Guangdong Province","award":["2024GCZX005"],"award-info":[{"award-number":["2024GCZX005"]}]},{"name":"Shenzhen Medical Research Fund","award":["C2501033"],"award-info":[{"award-number":["C2501033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Urban building change detection (UBCD) is essential for urban planning, land-use monitoring, and smart city analytics, yet bi-temporal optical methods remain limited by spectral confusion, occlusions, and weak sensitivity to structural change. To overcome these challenges, we propose DDDMNet, a lightweight deep learning framework that fuses multi-source inputs\u2014including DSM, dnDSM, DOM, and NDVI\u2014to jointly model geometric, spectral, and environmental cues. A core component of the network is the DSM Difference Normalization Module (DDDM), which explicitly normalizes elevation differences and directs the model to focus on height-related structural variations such as rooftop additions and demolition. Embedded into a TinyCD backbone, DDDMNet achieves efficient inference with low memory cost while preserving detail-level change fidelity. Across LEVIR-CD, WHU-CD, and DSIFN, DDDMNet achieves up to 93.32% F1-score, 89.05% Intersection over Union (IoU), and 99.61% Overall Accuracy (OA), demonstrating consistently strong performance across diverse benchmarks. Ablation analysis further shows that removing multi-source fusion, DDDM, dnDSM, or morphological refinement causes notable drops in performance\u2014for example, removing DDDM reduces IoU from 88.12% to 74.62%, underscoring its critical role in geometric normalization. These results demonstrate that DDDMNet is not only accurate but also practically deployable, offering strong potential for scalable 3D city updates and long-term urban monitoring under diverse data conditions.<\/jats:p>","DOI":"10.3390\/ijgi14110451","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:33:04Z","timestamp":1763469184000},"page":"451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DDDMNet: A DSM Difference Normalization Module Network for Urban Building Change Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Yihang","family":"Fu","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518172, China"},{"name":"Guangdong Engineering Center for Social Computing and Mental Health, Shenzhen 518172, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-4668","authenticated-orcid":false,"given":"Yuejin","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518172, China"},{"name":"Guangdong Engineering Center for Social Computing and Mental Health, Shenzhen 518172, China"}]},{"given":"Shijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518172, China"},{"name":"Guangdong Engineering Center for Social Computing and Mental Health, Shenzhen 518172, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JSTARS.2011.2168195","article-title":"Morphological building\/shadow index for building extraction from high-resolution imagery over urban areas","volume":"5","author":"Huang","year":"2011","journal-title":"IEEE J. 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