发表机构
Department of Electrical, Computer and Biomedical Engineering, University of Pavia; Department of Engineering, University of Sannio; Italian Space Agency (ASI)（帕维亚大学电气、计算机和生物医学工程系; 桑尼奥大学工程系; 意大利航天局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对灾后城市恢复监测难题，提出基于多时间SAR观测和深度学习异常检测的无监督框架，用COSMO - SkyMed时间序列生成恢复地图，应用于四个受地震影响城市，揭示重建动态，还对比了与夜间灯光指标的互补性，提供有效监测方法。
AI 中文摘要
监测灾后恢复对于理解城市系统如何重建并逐步恢复功能至关重要。然而，由于可靠的地面实况信息往往稀缺且恢复过程随时间演变，追踪重建仍然困难。本文提出了一个基于多时间合成孔径雷达（SAR）观测和深度学习异常检测的无监督恢复监测框架。利用COSMO - SkyMed时间序列识别与重建活动相关的持续时间异常，并生成空间明确的恢复地图。该框架应用于受2023年土耳其-叙利亚地震严重影响的四个城市，揭示了不同城市背景下的异质重建动态。结果显示了与受损和清理区域、临时集装箱定居点及新住宅区重建相关的持续异常的空间结构模式。与从SDGSAT - 1数据得出的夜间灯光恢复指标的比较突出了两种模式的互补性。结果表明，当没有标记的恢复数据集时，多时间SAR数据与无监督学习相结合为监测灾后重建提供了一种有效且可扩展的方法。
英文摘要
Monitoring post-disaster recovery is essential for understanding how urban systems rebuild and progressively return to functionality. However, tracking reconstruction remains difficult because reliable ground-truth information is often scarce and recovery processes evolve over time. This paper proposes an unsupervised framework for recovery monitoring based on multi-temporal synthetic aperture radar (SAR) observations and deep-learning anomaly detection. COSMO-SkyMed time series are used to identify persistent temporal anomalies associated with reconstruction activities and to generate spatially explicit recovery maps. The framework is applied to four cities severely affected by the 2023 Turkiye-Syria earthquakes, revealing heterogeneous reconstruction dynamics across different urban contexts. The results show spatially structured patterns of persistent anomalies related to reconstruction over damaged and cleared areas, temporary container settlements, and new residential districts. Comparison with nighttime-light recovery indicators derived from SDGSAT-1 data highlights the complementary nature of the two modalities: nighttime lights reflect the restoration of electricity supply and nighttime socioeconomic activity, whereas SAR anomalies capture structural changes in the built environment and may reveal reconstruction at earlier stages. The results demonstrate that multi-temporal SAR data combined with unsupervised learning provide an effective and scalable approach for monitoring post-disaster reconstruction when labeled recovery datasets are unavailable.
CommentsSubmitted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)
