  Buildings classification using satellite images is becoming more important
for several applications such as damage assessment, resource allocation, and
population estimation. We focus, in this work, on buildings damage assessment
(BDA) and buildings type classification (BTC) of residential and
non-residential buildings. We propose to rely solely on RGB satellite images
and follow a 2-stage deep learning-based approach, where first, buildings'
footprints are extracted using a semantic segmentation model, followed by
classification of the cropped images. Due to the lack of an appropriate dataset
for the residential/non-residential building classification, we introduce a new
dataset of high-resolution satellite images. We conduct extensive experiments
to select the best hyper-parameters, model architecture, and training paradigm,
and we propose a new transfer learning-based approach that outperforms
classical methods. Finally, we validate the proposed approach on two
applications showing excellent accuracy and F1-score metrics.
