CrossGeoNet: A Framework for Building Footprint Generation of Label-Scarce Geographical Regions

Abstract

• Planet satellite imagery is utilized for building mapping in less developed regions (e.g., cities in Africa) • A co-segmentation learning framework is proposed to tackle the problem of insufficient labeled samples in target cities for the task of building footprint generation • A cross-geolocation attention module is proposed to effectively learn the underlying similarity between different geolocations Building footprints are essential for understanding urban dynamics. Planet satellite imagery with daily repetition frequency and high resolution has opened new opportunities for building mapping at large scales. However, suitable building mapping methods are scarce for less developed regions, as these regions lack massive annotated samples to provide strong supervisory information. To address this problem, we propose to learn cross-geolocation attention maps in a co-segmentation network, which is able to improve the discriminability of buildings within the target city and provide a more general building representation in different cities. In this way, the limited supervisory information resulting from insufficient training examples in target cities can be compensated. Our method is termed as CrossGeoNet, and consists of three elemental modules: a Siamese encoder, a cross-geolocation attention module, and a Siamese decoder. More specifically, the encoder learns feature maps from a pair of images from two different geo-locations. The cross-location attention module aims at learning similarity based on these two feature maps and can provide a global overview of common objects (e.g., buildings) in different cities. The decoder predicts segmentation masks of buildings using the learned cross-location attention maps and the original convolved images. The proposed method is evaluated on two datasets with different spatial resolutions, i.e., Planet dataset (3 m/pixel) and Inria dataset (0.3 m/pixel), which are collected from various locations around the world. Experimental results show that CrossGeoNet can well extract buildings of different sizes and alleviate false detections, which significantly outperforms other competitors.