Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings

Abstract

Remote sensing mapping plays an important role in understanding regional development and geographical environment characteristics. Traditional remote sensing mapping at different levels usually fails to consider the shape, quantity, distribution, and position features of map objects. Therefore, a multilevel representation of urban buildings is realized based on the proposed framework for multilevel mapping from remote sensing images. In this process, the Mask R-CNN method is first applied to extract buildings from remote sensing images. Then, the orthogonal shape features of the extracted buildings are reconstructed based on corner detection, and urban roads are generated by extracting the internal structural characteristics of urban buildings for further multilevel representation. Finally, three innovative raster-based generalization algorithms, including simplification, aggregation, and typification based on Hough line detection technology, are developed for a multilevel representation of urban buildings. The experimental results reveal that the proposed methods can effectively realize multilevel mapping of urban buildings from remote sensing images while meeting basic cartographic requirements.