STPLS3D stands for Semantic Terrain Points Labeling – Synthetic 3D, which aims to provide a large-scale aerial measurement dataset (including synthetic & real annotated 3D point clouds) for semantic segmentation and instance segmentation tasks. The dataset contains:

• Point cloud of 1.27 square kilometers of landscape.
• 62 synthetic point clouds with different building styles, vegetation types and terrain shapes The synthetic dataset covers about 16 square kilometers of urban landscape, with up to 18 fine-grained semantic classes and 14 instance classes. Cornell University released this dataset.

Citation

If you find our work useful in your research, please consider citing: @inproceedings{Chen_2022_BMVC, author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues THOMAS and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman}, title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset}, booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022}, publisher = {{BMVA} Press}, year = {2022}, url = {https://bmvc2022.mpi-inf.mpg.de/0429.pdf} }