Hiroyuki Miura, Osamu Murao, Ryo Saito, Mufidatun Khoiriyah, Muzailin Affan
This study proposes a novel method for monitoring long-term urban recovery and expansion by integrating freely available medium-resolution satellite data with machine learning. While traditional recovery assessments often rely on regional-based and timely sparse data, we demonstrated that two decades of building dynamics were assessed using time-series Landsat imagery. We applied this method to Banda Aceh, Indonesia, which was devastated by the 2004 Indian Ocean Tsunami. A random forest regression model was developed using spectral indices, trained on building counts from high-resolution imagery (2004, 2007, and 2024). The model achieved high estimation accuracy, with a coefficient of determination (R2) of 0.79, showing strong agreement with visual interpretation and field investigations. Our analysis captured regional variations in recovery speed and significant recent expansion in both unaffected and inundated areas. These findings provide a scalable approach for disaster risk reduction and urban planning, offering a vital tool for policymakers to monitor reconstruction progress and manage future urban growth in disaster-prone regions. © 2026 The Authors.
Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan; International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, Japan; Graduate School of Information Sciences, Tohoku University, Sendai, Japan; Graduate School of Architecture and Building Science, Tohoku University, Sendai, Japan; Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
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