用雷达和光学图像实现全天候建筑损毁实例级识别，提升灾后响应效率。
Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge
- 融合灾前光学与灾后雷达图像，分步定位建筑并判断损毁等级。
- 最佳模型在未知灾害上达到mAP 0.182，远超基线但低于理想水平。
- 适合灾害应急、遥感分析及多模态融合研究者参考。
快速灾后响应需要及时获取每栋建筑是否完好、受损或倒塌的详细信息。然而，灾后光学影像可能因云层、烟雾或黑暗而不可用。本研究通过‘明亮挑战’（Bright Challenge）评估了基于亚米级灾前光学图像与灾后合成孔径雷达（SAR）图像的全天候建筑损毁映射能力。参赛者需检测并勾勒每栋建筑，并为其分配三类互斥的损毁标签之一。挑战扩展了全球分布的 extsc{Bright}数据集，新增16个灾害事件中约29.1万栋建筑的实例级标注，涵盖七种灾害类型。最终阶段仅在两个未参与训练的2025年事件上评估：加利福尼亚野火与牙买加飓风。共有157名参与者提交1,289次结果，46支团队进入决赛。两支优胜方案测试mAP分别为0.182和0.181，约为公开基线0.021的8.7倍，但仍显著低于最佳域内保留分数0.513。跨阶段排名大幅波动，性能普遍下降。领先方案均采用模态专用编码、分阶段或后期融合策略，且以光学图像主导分离建筑定位与损毁识别。优胜方法还引入场景感知阈值调整与伪标签自适应机制。结果表明，跨事件泛化与稳定损伤程度判别仍是主要挑战。所有数据、标注、基线代码及优胜方案均已公开于https://github.com/ChenHongruixuan/BRIGHT。
原文摘要 · Abstract (English)
Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event optical image and a post-event SAR image. Participants were required to detect and delineate each building and assign exactly one of three mutually exclusive damage labels. The challenge extended the globally distributed \textsc{Bright} dataset with instance-level annotations for about 291,000 buildings across 16 disaster events spanning seven disaster types. The final phase was evaluated exclusively on two 2025 events absent from training: a wildfire event in California and a hurricane in Jamaica. A total of 157 participants made 1,289 submissions, and 46 teams entered the final phase. The two winning solutions achieved test mAPs of 0.182 and 0.181, approximately 8.7 times the public baseline of 0.021, but remained far below the best in-domain holdout score of 0.513. Across teams ranked in both phases, performance declined sharply and the rank order changed substantially. The two leading solutions independently favored modality-specific encoding, staged or late optical--SAR fusion, and an optical-dominant separation of building localization from damage recognition. The winning method additionally used scene-aware threshold adjustment and pseudo-label adaptation. These results identify cross-event generalization and stable severity discrimination as the principal remaining challenges. All data, annotations, baseline code, and winning solutions are publicly available at https://github.com/ChenHongruixuan/BRIGHT.
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