DDDMNet
A DSM Difference Normalization Module Network for Urban Building Change Detection
Bibliographic Data
| ID | 22032806 |
|---|---|
| Authors | Yihang Fu (0009-0009-9942-9838, Shenzhen University), Yuejin Li (0000-0003-0993-6545, Shenzhen University, corresponding author), Shijie Zhang (0000-0001-5394-3991, Shenzhen University) |
| Year | 2025 |
| Volume | 14 |
| Issue | 11 |
| Pages | 451 |
| Publication date | 2025-11-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14110451 |
| OpenAlex | W4416334864 |
| Language | EN |
| References cited | 34 |
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—including DSM, dnDSM, DOM, and NDVI—to 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—for 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
Change detection · Data integrity · Deep learning · Inference · Scalability · Advanced Neural Network Applications · Remote Sensing and LiDAR Applications · Remote-Sensing Image Classification
| Highly cited | No |
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