Developing countries usually lack the proper governance means to generate and
regularly update a national rooftop map. Using traditional photogrammetry and
surveying methods to produce a building map at the federal level is costly and
time consuming. Using earth observation and deep learning methods, we can
bridge this gap and propose an automated pipeline to fetch such national urban
maps. This paper aims to exploit the power of fully convolutional neural
networks for multi-class buildings' instance segmentation to leverage high
object-wise accuracy results. Buildings' instance segmentation from sub-meter
high-resolution satellite images can be achieved with relatively high
pixel-wise metric scores. We detail all engineering steps to replicate this
work and ensure highly accurate results in dense and slum areas witnessed in
regions that lack proper urban planning in the Global South. We applied a case
study of the proposed pipeline to Lebanon and successfully produced the first
comprehensive national building footprint map with approximately 1 Million
units with an 84% accuracy. The proposed architecture relies on advanced
augmentation techniques to overcome dataset scarcity, which is often the case
in developing countries.

使用地球观测和深度学习方法，该论文旨在充分利用完全卷积神经网络来实现多类建筑物实例分割，以实现相对较高的像素度量分数，进而在缺乏适当城市规划的地区实现密集区域和贫民窟地区的高准确性结果，并以黎巴嫩为案例，成功产生了首个包含约 100 万个单位且准确率达 84% 的综合性国家建筑物足迹地图。