Building footprint generation through convolutional neural networks with attraction field representation

Installation

You can get the code from GitHub

We propose to learn attraction field representation for building boundaries, which is capable of providing an enhanced representation power. Our method comprises two elemental modules: an Img2AFM module and an AFM2Mask module. More specifically, the former aims at learning an attraction field representation conditioned on an input image, which is capable of enhancing building boundaries and suppressing the background. The latter module predicts segmentation masks of buildings using the learned attraction field map.

Citations

Li, Qingyu, Lichao Mou, Yuansheng Hua, Yilei Shi, and Xiao Xiang Zhu. “Building footprint generation through convolutional neural networks with attraction field representation.” IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-17.