Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery. (arXiv:2201.10523v1 [cs.CV])

Natural disasters ravage the world's cities, valleys, and shores on a regular
basis. Deploying precise and efficient computational mechanisms for assessing
infrastructure damage is essential to channel resources and minimize the loss
of life. Using a dataset that includes labeled pre- and post- disaster
satellite imagery, we take a machine learning-based remote sensing approach and
train multiple convolutional neural networks (CNNs) to assess building damage
on a per-building basis. We present a novel methodology of interpretable deep
learning that seeks …

arxiv building classification convolutional neural networks cv interpretability networks neural networks satellite