A DOMAIN GENERALIZED MASK R-CNN FOR BUILDING INSTANCE SEGMENTATION

Abstract

Building instance segmentation is a critical task for urban planning. It has been extensively studied through state-of-the-art instance segmentation models, and great advances have been reported. However, the issue of domain shift referring to disparities between training and target data distributions remains elusive. Although domain adaptation can help tackle it, it still requires access to target domain data. In this paper, we explore the problem of building extraction in the domain generalization setting, where no access to either target labels or target data is assumed. Mask R-CNN is equipped with image, instance, and pixel-level domain adversarial modules in order to encourage the extraction of domain-invariant features. The preliminary results obtained with cross-continent domain generalization are promising. For the sake of reproducibility, the code and models are publicly available on this website: https://github.com/efkandurakli/DGMaskRCNN.