Monitoring Post-Disaster Urban Recovery Using High-Resolution SAR Time Series and Unsupervised Learning: Evidence from the 2023 Türkiye-Syria Earthquake
2026-07-27 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors created a new method to track how cities rebuild after disasters using radar images taken over time. They used deep learning to find unusual changes in the data that show reconstruction activities without needing pre-labeled examples. Their approach was tested on cities hit by the 2023 Turkiye-Syria earthquakes and found different rebuilding patterns in various areas. They also compared their results with nighttime light data, showing that radar detects structural rebuilding earlier than light signals, which indicate restored electricity and activity. This method can help monitor recovery efforts when detailed ground information is missing.
post-disaster recoverysynthetic aperture radar (SAR)deep learninganomaly detectionmulti-temporal dataCOSMO-SkyMedurban reconstructionnighttime lightsSDGSAT-1unsupervised learning
Authors
Luigi Russo, Deodato Tapete, Silvia Liberata Ullo, Paolo Gamba
Abstract
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.