  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/.
