S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

TL;DR

S2Looking introduces a large-scale satellite image dataset with side-looking, off-nadir angles for building change detection, addressing limitations of existing datasets and challenging current deep learning models.

Contribution

The paper presents S2Looking, a novel dataset with diverse viewing angles and rural images, enhancing building change detection research.

Findings

Deep learning algorithms perform more poorly on S2Looking compared to near-nadir datasets.

The dataset exhibits larger illumination variances and increased complexity.

Preliminary tests show S2Looking is significantly more challenging for current models.

Abstract

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate {the} use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms. The dataset is available at https://github.com/S2Looking/.