Geographia Technica, Vol 21(2), Special Issue: Artificial Intelligence Applications in Geography, 2026, pp. 1-18
FLOOD MAPPING IN PHRA NAKHON SI AYUTTHAYA, THAILAND, UTILIZING SENTINEL-1 SAR IMAGERY AND DEEP LEARNING APPROACHES
Kritchayan INTARAT 
, Phatsachon KUANKHAMNUAN , Woraman JANGSAWANG
ABSTRACT: This study presents a deep learning (DL)-based flood detection framework for Phra Nakhon Si Ayutthaya Province, Thailand. The framework integrates U-Net architecture with three encoder variants-ResNet50, ResNet101, and ResNet152-using synthetic aperture radar (SAR) imagery from the Sentinel-1 satellite. Model performance is assessed through statistical metrics including accuracy, precision, recall, F1-score, Dice Loss, intersection over union (IoU), and computational time. The U-Net model with a ResNet101 encoder achieved the best performance, with an accuracy of 91.5%, F1-score of 0.886, Dice Loss of 0.116, and IoU of 86.8%, requiring about 24 minutes of training. Despite longer training, the ResNet101-based U-Net substantially enhances flood detection accuracy, highlighting its value as a reliable tool for real-time monitoring and rapid response in flood-prone areas of Thailand.
Keywords: Flood Detection; SAR Images; U-Net; ResNet; Phra Nakhon Si Ayutthaya Province
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