Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction
This provides a practical tool for researchers and practitioners in remote sensing, but it is incremental as it packages existing methods rather than introducing new ones.
The authors tackled the lack of a unified package for building segmentation and outline extraction in remote sensing by introducing Geoseg, which implements over nine state-of-the-art models and achieves performance and computational efficiency evaluated through a unified dataset.
Recently, deep learning algorithms, especially fully convolutional network based methods, are becoming very popular in the field of remote sensing. However, these methods are implemented and evaluated through various datasets and deep learning frameworks. There has not been a package that covers these methods in a unifying manner. In this study, we introduce a computer vision package termed Geoseg that focus on building segmentation and outline extraction. Geoseg implements over nine state-of-the-art models as well as utility scripts needed to conduct model training, logging, evaluating and visualization. The implementation of Geoseg emphasizes unification, simplicity, and flexibility. The performance and computational efficiency of all implemented methods are evaluated by comparison experiment through a unified, high-quality aerial image dataset.