Web: http://arxiv.org/abs/2201.10523

Jan. 26, 2022, 2:11 a.m. | Thomas Y. Chen

cs.LG updates on arXiv.org arxiv.org

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 neural networks satellite

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