Monitoring Post-Disaster Urban Recovery Using High-Resolution SAR Time Series and Unsupervised Learning: Evidence from the 2023 Türkiye-Syria Earthquake

Near-Real-Time Flood Disaster Monitoring Based on Moonlight and Multi-Source Remote Sensing: A Case Study of Zhengzhou Rainstorm

The increasing frequency of natural disasters such as floods has led to severe casualties and substantial economic losses. Consequently, near-real-time flood disaster monitoring has become critical for effective disaster response and accurate damage assessment. However, traditional approaches relying on daytime optical imagery and SAR data often suffer from long satellite revisit cycles and data acquisition latency, while nighttime Earth observation remains limited by the absence of adequate illumination. This study proposes an innovative framework that integrates moonlight Earth observation to enable near-real-time flood monitoring and loss assessment and is applied to the 7.20 Zhengzhou rainstorm event. Compared to conventional disaster monitoring techniques, integrating moonlight observations shortens the effective data acquisition period to approximately one day, significantly improving temporal responsiveness compared with conventional techniques. The innovative framework identifies a flood-affected area of 722.93 km2 and estimated economic losses of 107.96 billion yuan in the disaster-affected region. The results indicate that the moonlight data source has significant capacity for near-real-time disaster monitoring and disaster loss assessment and highlight the potential broad impact of this study in relevant fields.

Post-earthquake urban reconstruction, land use, and tropospheric NO₂ exposure in Malatya, Türkiye

Land-use decisions made during post-disaster reconstruction can reshape transport systems, built-up environments, and the spatial distribution of environmental exposures. However, these relationships remain insufficiently examined in earthquake-affected cities. This study evaluates the temporal and spatial patterns of tropospheric nitrogen dioxide (NO₂) in Malatya, Türkiye, during 2019–2025 and compares the 2019–2022 baseline with the 2023–2025 reconstruction period. Sentinel-5P TROPOMI observations were integrated with land use/land cover, road density, elevation, NDVI, and NDBI within a common spatial framework comprising 108,626 pixel-year observations. Tropospheric NO₂ was modelled using XGBoost, variable contributions were interpreted through SHAP, and spatial dependence was assessed using Global Moran’s I . High NO₂ levels were consistently concentrated along the Battalgazi-Yeşilyurt urban axis, where dense road networks, artificial surfaces, and intensive urban functions overlap, whereas lower values were observed in higher-elevation and lower-density areas. Land use/land cover had the greatest gain-based contribution to the model, while elevation exerted a stronger influence on pixel-level predictions. The model showed moderate predictive performance on the holdout test set ( R 2 = 0.5941, RMSE = 2.816 μmol m −2 , and MAE = 1.865 μmol m −2 ). Annual Global Moran’s I values ranged from 0.9893 to 0.9922 ( p = 0.001), indicating strong spatial clustering of tropospheric NO₂ throughout the study period. The reconstruction period was characterized by a more fragmented and spatially heterogeneous NO₂ pattern rather than a uniform province-wide increase. These findings suggest that changing transport flows, built-up development, topographic controls, and post-disaster spatial reorganization may jointly differentiate environmental exposure conditions. The study provides interpretable spatial evidence for health-sensitive land-use planning, air-quality monitoring, and post-disaster urban management.

AIoT-enabled urban platform for flood detection and impact mapping: towards near-real-time spatial decision support in disaster management

Flooding is one of the most pervasive and destructive natural hazards, with its frequency and intensity expected to worsen under climate change. While advances in geospatial analytics, Internet of Things infrastructures, and artificial intelligence have enhanced urban data ecosystems, existing smart city platforms remain limited in their capacity to provide automated, real-time flood intelligence. Most platforms focus on delineating flood extent without extending to impact assessment, leaving critical gaps in disaster response and recovery. As a result, exposure analyses of residents, buildings, and infrastructure are often conducted manually, delaying emergency services, rescue operations, and longer-term recovery planning. This study introduces an Artificial Intelligence of Things-enabled urban platform designed for flood detection, impact mapping, and spatial decision support. The conceptual architecture integrates distributed sensing, satellite imagery, and pretrained deep learning models within the Esri ArcGIS ecosystem, operationalising the principles of platform urbanism for disaster management. Demonstration of the platform draws on the March 2022 flood event in South East Queensland, with a focus on selected suburbs in Logan and the Gold Coast. Using the Prithvi-Flood Segmentation model and harmonised Sentinel-2 imagery, the workflow automates the delineation of flood extent and links outputs with exposure analytics to identify affected suburbs, railway stations, roads, buildings, and residents. Future research should focus on fine-tuning pretrained models for local contexts and scaling the architecture to incorporate additional AI-driven modules, such as road extraction, infrastructure vulnerability assessment, or population displacement modelling, thereby extending the platform’s utility across multiple hazards and governance contexts.

AI-based multi-temporal analysis of urban dynamics using Sentinel-2 data. A case study over Osmaniye, Turkey

Abstract. Monitoring urban dynamics in hazard-prone regions is essential for understanding long-term urban growth and assessing the impact of disruptive events. This study presents a multi-temporal framework for urban monitoring that integrates Sentinel-2 multispectral imagery with semantic segmentation techniques. A U-Net architecture trained using World Settlement Footprint (WSF) reference data was employed to automatically extract built-up areas and reconstruct the temporal evolution of urban expansion in the city of Osmaniye (Türkiye) between 2015 and 2025. The trained model was applied to the full Sentinel-2 time series to generate yearly built-up maps and analyse changes in the urban footprint over the decade. The results reveal a clear and sustained expansion of built-up areas throughout the study period. The multi-temporal analysis also captures a distinct disruption in the urban trajectory associated with the 2023 earthquake, followed by a rapid rebound likely related to post-disaster reconstruction activities. By combining semantic segmentation with multi-temporal satellite observations, the proposed framework enables the detection of both gradual urban expansion and abrupt disaster-related changes using openly available Earth Observation data. The results highlight the potential of Artificial Intelligence and Remote Sensing for continuous urban monitoring, disaster impact assessment, and data-driven urban planning.

Multi-Temporal Flood Mapping and Prediction using Sentinel-1 and Landsat-8 Data Fusion

An Interpretable Transformer-Based Model for Post-Disaster Infrastructure Damage Assessment Using High-Resolution Remote Sensing Imagery

Climate change impacts have raised the frequency of natural disasters throughout the world. The impacts of these natural events, like drought, floods, cyclones, fire, hurricanes, and others, are complex for both developing and developed countries. Specifically, post-disaster recovery and disaster risk management become highly significant to ensure strong development. With the emerging accessibility of high-resolution satellite images, AI applications like machine learning and deep learning (DL) are employed to systematize the process of interpretation. This study introduces a Post-Disaster Structural Damage Analysis using Remote Sensing Images (PDSDA-RSI) technique to design an explainable deep learning framework for detecting and assessing post-disaster structural damage from high-resolution remote sensing images. Soft voting-based classification integrated with a stacked sparse denoising autoencoder, a bidirectional gated recurrent network, and a Siamese neural network is performed to classify the post-disaster damages into multiple classes. Furthermore, rectified Adam is applied for stabilizing the model training. At last, Grad-CAM++ is employed to visualize and interpret model predictions by highlighting key regions in the image. To exhibit the improved performance of the PDSDA-RSI model, extensive simulations take place the outcomes are investigated under numerous measures. The comparison analysis reported the betterment of the PDSDA-RSI model under several metrics.