BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning

1 code implementation • 14 Apr 2023 • Cheng Liao, Han Hu, Xuekun Yuan, Haifeng Li, Chao Liu, Chunyang Liu, Gui Fu, Yulin Ding, Qing Zhu

This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings.

Change Detection Contrastive Learning

MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery

1 code implementation • 26 Oct 2019 • Qing Zhu, Cheng Liao, Han Hu, Xiaoming Mei, Haifeng Li

This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries.