Drone-based water level detection in flood disasters

ドローンを用いた洪水災害時の水没度検出手法

Drone-based water level detection in flood disasters

Keywords

DroneFloodwater level detection

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition.

Published Papers

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2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), SPT-IoT 2026, pp. 1366–1371

Disaster CommunicationLoRa +4

A Lightweight Vision-Language Model for Disaster Image Summarization

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2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerconAI 2026, pp. 1203–1208

Semantic CommunicationDisaster Response +4

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2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), URBSENSE 2026, pp. 1094–1099

Landslide PredictionPhysics-Integrated Learning +3

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2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerRad 2026, pp. 328–333

ISACBeyond 5G +4

A Simulation Framework for Precision Formation Flying of Massive Satellite Swarms

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2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerVehicle , pp. 230–235

Satellite Formation FlyingDistributed Simulation +4

A Digital Twin Approach for Crowd Flow Modeling on Railway Station Platforms

Yu Yasuda, Tatsuya Amano and Hirozumi Yamaguchi

IEEE International Conference on Smart Computing (SMARTCOMP), pp. 82-89

DOI 10.1109/SMARTCOMP65954.2025.00069

Digital TwinCrowd Simulation +1