Building extraction via convolutional neural networks from an open remote sensing building dataset

Abstract

Automatic extraction of buildings from remote sensing images is significant to city planning, popular estimation, map making and updating.We report several important developments in building extraction. Automatic building recognition from remote sensing data has been a scientific challenge of more than 30 years. Traditional methods based on empirical feature design can hardly realize automation. Advanced deep learning based methods show prospects but have two limitations now. Firstly, large and accurate building datasets are lacking while such dataset is the necessary fuel for deep learning. Secondly, the current researches only concern building's pixel wise semantic segmentation and the further extractions on instance-level and vector-level are urgently required. This paper proposes several solutions. First, we create a large, high-resolution, accurate and open-source building dataset, which consists of aerial and satellite images with both raster and vector labels. Second,we propose a novel structure based on fully neural network which achieved the best accuracy of semantic segmentation compared to most recent studies. Third, we propose a building instance segmentation method which expands the current studies of pixel-level segmentation to building-level segmentation. Experiments proved our dataset's superiority in accuracy and multi-usage and our methods' advancement. It is expected that our researches might push forward the challenging building extraction study.