Best practices in combining multi-hazard damage imagery training datasets for damage detection for a deep learning neural network
ABSTRACT Accurate and timely damage assessment is important after any natural disaster event. Accurate damage assessments enhance the efficient distribution of resources. Building damage levels are an important outcome of damage assessment, especially in urban areas. Although at present, most building damage assessments are collected manually from post-disaster satellite images or aerial photographs, efforts are […]
