POI Detection of High-Rise Buildings Using Remote Sensing Images: A Semantic Segmentation Method Based on Multitask Attention Res-U-Net

A point-of-interest (POI) represents a specific point location that may be useful or interesting for people, and therefore, each and every building footprint in a topographic map can be recognized as a POI. Automatic extraction of building footprints using remote sensing images has become a challenging and important research topic, which is in demand for urban planning and development. Extensive studies have explored a variety of semantic segmentation methods using deep learning algorithms to achieve better performance in building footprint extraction; however, the existing algorithms were shown to have some limitations, which lead to poor segmentation results. Building roofs were recognized as building footprints in the previous studies. This is prone to error especially for high-rise buildings due to different sensor view angles. In this article, we propose a multitask Res-U-Net model with an attention mechanism for the extraction of the building roofs and the whole building shapes from remote sensing images and then use an offset vector method to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. We also apply the online food delivery (OFD) data to parse the POI name of every building footprint. Several strategies are also developed in combination with the proposed model, including data augmentation and postprocessing. We conduct numerical experiments using real data of remote sensing images and OFD historical order data. Results demonstrate that our proposed model achieves a total F1-score of 77.05% and an intersection over union (IoU) of 63.55% in terms of the building roof segmentation, and an overall F1-score of 79.02% and an IoU of 66.05% for the whole building shape segmentation, which both achieve the best performance among all baseline models.