Automated National Urban Map Extraction
Hasan Nasrallah Abed Ellatif Samhat Cristiano Nattero Ali J. Ghandour
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.