将全天候建筑损伤映射推进到实例级别：2026年光明挑战赛的成果与见解

中文摘要 AI 辅助

快速的灾后响应需要有关建筑物是否完好、受损或被毁的及时的建筑级信息。然而，由于云层、烟雾或黑暗，震后光学图像可能无法获取。光明挑战赛评估了从亚米级分辨率的震前光学图像和震后合成孔径雷达（SAR）图像进行全天候建筑损伤映射。参与者需要检测和描绘每栋建筑，并准确分配三个相互排斥的损伤标签之一。该挑战赛扩展了全球分布的Bright数据集，为跨越七种灾害类型的16个灾害事件中的约291,000栋建筑提供了实例级注释。最后阶段仅在训练中未出现的2025年的两个事件上进行评估：加利福尼亚的野火事件和牙买加的飓风事件。共有157名参与者提交了1,289份作品，46个团队进入了最后阶段。两个获胜解决方案的测试平均精度均值（mAP）分别为0.182和0.181，约为0.021的公共基线的8.7倍，但仍远低于领域内最佳保留分数0.513。在两个阶段排名的所有团队中，性能急剧下降，排名顺序也发生了很大变化。两个领先的解决方案独立地倾向于特定模态编码、分阶段或后期光学 - SAR融合，以及将建筑物定位与损伤识别进行以光学为主的分离。获胜方法还使用了场景感知阈值调整和伪标签自适应。这些结果表明跨事件泛化和稳定的严重程度区分是主要的剩余挑战。所有数据、注释、基线代码和获胜解决方案都可在该https网址公开获取。

英文摘要

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.

发表机构

• RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能项目中心（AIP），理化学研究所）
• Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
• State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
• School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）

机构由 AI 辅助整理，请以论文原文为准。