SEMCITY TOULOUSE: A BENCHMARK FOR BUILDING INSTANCE SEGMENTATION IN SATELLITE IMAGES

Abstract

In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning.