Delineating Landslide and Debris Flow Detection in Japan through Aerial Photography: A YOLO v8 Approach to Disaster Management

OPARA Jonpaul Nnamdi, MORIWAKI Ryo, CHUN Pang-jo

doi:10.11532/jsceiiai.5.1_111

<p>This study delves into the escalating challenge of landslides and debris flows in Japan, prompted by its unique topography and climatic conditions that render it vulnerable to geological hazards. Recognizing the pressing need for innovative solutions, the research focuses on the application of the YOLO v8 computer vision model. With a dataset comprising 1,352 aerial images from disaster sites, the study employs YOLO v8 for hazard detection and segmentation. The model exhibits a precision of 0.49 for detection and 0.76 for segmentation, reflecting its accuracy in positive predictions. Noteworthy recall values of 0.42 for detection and 0.54 for segmentation underscore the model’s proficiency in capturing positive cases. The mAP50, a comprehensive accuracy measure, stands at 0.39 for detection and 0.52 for segmentation, underscoring the model’s efficacy in hazard detection. The research emphasizes the instrumental role of AI in disaster management and advocates for the continuous exploration of innovative methodologies.</p>

Delineating Landslide and Debris Flow Detection in Japan through Aerial Photography: A YOLO v8 Approach to Disaster Management

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