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.
A. Pigna, Pietro Di Stasio, D. Tapete et al.· The International Archives o...· 0 citations
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihood mapping in C\'ordoba, Argentina, integrating high-resolution PlanetScope multispectral (MS) imagery, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, and medium-resolution PRISMA hyperspectral (HS) observations. The problem is formulated as a patch-level classification task using the official Registro Nacional de Barrios Populares (ReNaBaP) inventory as reference, and the models are evaluated through four geographically partitioned folds. SAR-only and MS-only baselines, their configurations with PRISMA HS support, and early fusion (EF), middle fusion (MF), and late fusion (LF) strategies are systematically compared. Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity, while PRISMA contributes complementary spectral information alongside the higher-resolution MS and SAR representations. Beyond the standard evaluation against ReNaBaP, an external municipal vulnerability layer is used to interpret detections outside the official polygons, showing that several apparent false positives overlap broader vulnerable urban areas. Thermal analysis further shows that ReNaBaP settlements exhibit significantly higher surface temperatures than their immediate surroundings during a heatwave event, indicating localised surface-heat amplification. Taken together, these results suggest that multi-sensor EO fusion can support both the mapping of ReNaBaP settlements and the interpretation of broader urban vulnerability patterns.
Luigi Russo, A. Ferral, S. Ullo et al.· 0 citations
An unsupervised framework for recovery monitoring based on multi-temporal synthetic aperture radar (SAR) observations and deep-learning anomaly detection and COSMO-SkyMed time series is applied to four cities severely affected by the 2023 Turkiye-Syria earthquakes, revealing heterogeneous reconstruction dynamics across different urban contexts.
Luigi Russo, D. Tapete, S. Ullo et al.· arXiv.org· 0 citations
This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies.
Luigi Russo, D. Tapete, S. Ullo et al.· ISPRS Annals of the Photogra...· 1 citation
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