Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones Article Swipe
Zheng Kai
,
Wu Zhenkai
,
Wei Fupeng
,
Zhou, Miaolan
,
Lie Kai
,
Guo Hai-tao
,
Ding, Lei
,
Zhang Wei
,
Dong, Hang-Cheng
,
Zhenga, Kai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.19035
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.19035
Accurately and swiftly assessing damage from conflicts is crucial for humanitarian aid and regional stability. In conflict zones, damaged zones often share similar architectural styles, with damage typically covering small areas and exhibiting blurred boundaries. These characteristics lead to limited data, annotation difficulties, and significant recognition challenges, including high intra-class similarity and ambiguous semantic changes. To address these issues, we introduce a pre-trained DINOv3 model and propose a multi-scale cross-attention difference siamese network (MC-DiSNet). The powerful visual representation capability of the DINOv3 backbone enables robust and rich feature extraction from bi-temporal remote sensing images. The multi-scale cross-attention mechanism allows for precise localization of subtle semantic changes, while the difference siamese structure enhances inter-class feature discrimination, enabling fine-grained semantic change detection. Furthermore, a simple yet powerful lightweight decoder is designed to generate clear detection maps while maintaining high efficiency. We also release a new Gaza-change dataset containing high-resolution satellite image pairs from 2023-2024 with pixel-level semantic change annotations. It is worth emphasizing that our annotations only include semantic pixels of changed areas. We evaluated our method on the Gaza-Change and two classical datasets: the SECOND and Landsat-SCD datasets. Experimental results demonstrate that our proposed approach effectively addresses the MCD task, and its outstanding performance paves the way for practical applications in rapid damage assessment across conflict zones.
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- Type
- preprint
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- https://doi.org/10.48550/arxiv.2511.19035
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All OpenAlex metadata
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https://doi.org/10.48550/arxiv.2511.19035Digital Object Identifier
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Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict ZonesWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-11-24Full publication date if available
- Authors
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Zheng Kai, Wu Zhenkai, Wei Fupeng, Zhou, Miaolan, Lie Kai, Guo Hai-tao, Ding, Lei, Zhang Wei, Dong, Hang-Cheng, Zhenga, KaiList of authors in order
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https://doi.org/10.48550/arxiv.2511.19035Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.48550/arxiv.2511.19035Direct OA link when available
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0Total citation count in OpenAlex
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