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.

翻译：利用卫星图像对建筑物进行分类,对于损害评估、资源分配和人口估计等若干应用,越来越重要。我们在这项工作中侧重于住宅和非住宅建筑物的建筑物损坏评估和建筑物类型分类。我们提议完全依赖RGB卫星图像,并采用基于深层次学习的两阶段方法,首先,采用语义分割模型提取建筑物的足迹,然后对耕地图像进行分类。由于缺乏住宅/非住宅建筑分类的适当数据集,我们采用了一套高分辨率卫星图像的新数据集。我们进行了广泛的实验,以选择最佳的超参数、模型结构和培训范式。我们提出了一种超越传统方法的新的基于学习的转让方法。最后,我们验证了拟议的两种应用方法,这些应用显示极精确性和F1-核心指标。