Update README.md

README.md

@@ -479,6 +479,7 @@ Semantic Segmentation
 * [CVNet](https://github.com/xzq-njust/CVNet) -> code for 2022 paper: CVNet: Contour Vibration Network for Building Extraction
 * [CFENet](https://github.com/djzgroup/CFENet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/9/2276): A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery
 * [HiSup](https://github.com/SarahwXU/HiSup) -> code for 2022 [paper](https://arxiv.org/abs/2208.00609): Accurate Polygonal Mapping of Buildings in Satellite Imagery
+* [BuildingExtraction](https://github.com/KyanChen/BuildingExtraction) -> code for 2021 [paper](https://www.mdpi.com/2072-4292/13/21/4441): Building Extraction from Remote Sensing Images with Sparse Token Transformers
 
 ### Segmentation - Solar panels
 * [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)