Cascaded Deep Neural Networks for Predicting Biases Between Building Polygons in Vector Maps and New Remote Sensing Images

Tremendous historical and recent building vector maps are available from private surveying and mapping departments or open-source platforms such as Open Street Map (OSM). However, there invariably exist offsets between the building vectors and new remote sensing images, caused by geometric registration errors and parallax from hypsography. Consequently, these vector maps cannot be directly used as labels for the up-to-date GIS productions, as well as popular supervised machine learning. As the first work of this kind, this paper proposes an offset correction method, based on deep learning, to predict the bias between a vector building map and a new remote sensing image. The method is based on a dense regression model to predict the offsets between the image and the vector map at the pixel level, which are then processed to retrieve the offsets of each polygon. Two datasets consist of remote sensing images and biased building polygons, the WHU change detection and London datasets, are prepared to test the effectiveness of our approach. In the WHU change detection dataset, approaching 80% polygons can be correctly registered within the tolerance of three pixels, in the London dataset, more than 60% polygons can be corrected by a pre-trained model on an available open-source dataset, both of which demonstrated that our method can contribute to the GIS map updating and more efficient preparation of training samples for a deep learning model.