DEVELOPMENT OF THE BUILDING DAMAGE DETECTION MODEL USING OBLIQUE AERIAL PHOTOGRAPHY AND DEEP LEARNING

Abstract

For the purpose of damage detection immediately after a disaster, we developed a deep learning model using aerial photographs taken from an oblique direction with a helicopter or drone. This model automatically extracts damages to buildings and landslides, then divides into four classes: no damage, damage, collapse and landslide. As a result of discrimination using unlearned test aerial photographs using this model, it was confirmed that the average Fmeasure of each class was about 64% and mAP was about 0.35.