Learning Color Distributions from Bitemporal Remote Sensing Images to Update Existing Building Footprints

Abstract

For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time, and the area of change is variable; hence, automated methods for generating up-to-date building footprints are urgently needed. However, the labels of current buildings or changed areas are usually lacking, and the conditions for acquiring images from different periods are not perfectly consistent, which can severely limit deep learning methods when attempting to learn deep information about buildings. In addition, common update methods can ignore the strictly accurate historical labels of unchanged areas. To solve the above problem, we propose a new update algorithm to update the existing building database to the current state without manual relabeling. First, the difference between the data distributions of different time-phase images is reduced using the image color translation method. Then, a semantic segmentation model predicts the segmentation results of the images from the latest period, and, finally, a post-processing update strategy is applied to strictly retain the existing labels of unchanged regions to attain the updated results. We apply the proposed algorithm on the Wuhan University change detection dataset and the Beijing Huairou district land survey dataset to evaluate the effectiveness of the method in building surface and complex labeling scenarios in urban and suburban areas. The F1 scores of the updated results obtained for both datasets reach more than 96%, which proves the applicability of our proposed algorithm and its ability to efficiently and accurately extract building footprints in real-world scenarios.