Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks

Abstract

Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. However, the laborious and expensive task of gathering and maintaining a vast array of diverse training data poses a challenge to the widespread adoption of these methods for large-scale built-up area mapping. This paper presents a two-step framework enabling an automated extraction of built-up areas using Sentinel-1 and Sentinel-2 data. Initially, training data for built-up and non-built-up classes are automatically sampled and labeled from Sentinel-1 and Sentinel-2 data for a given area of interest. Subsequently, a cross-fusion neural network is trained using the samples from the first step to produce a built-up map for the entire study area. To enhance the network’s resilience to label noise, a contextual virtual adversarial training (CVAT) regularization is introduced within the mean-teacher architecture. Our proposed framework was tested on 48 different study areas across the world. Both quantitative and qualitative evaluations demonstrate its robustness and effectiveness for large-scale built-up area extraction. The versatility of our framework in generating accurate and up-to-date built-up information, which is essential for monitoring urban environments and assessing economic losses resulting from natural disasters, is highlighted through comparisons with four state-of-the-art global built-up products: Global Human Settlement Built-up map based on 2018 Sentinel-2 composites (GHS-BUILT-S2), World Settlement Footprint 2019 (WSF 2019), ESA World Cover, and Dynamic World.