An interactive method for bridging the gap between deep learning based building contour segmentation and manual annotation

Abstract

ABSTRACT Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation.