Extracted Features from ISPRS Vaihingen 3D Dataset

This dataset contains 3D point cloud data from the Vaihingen dataset, enriched with newly introduced Inner-Cube and Outer-Cube geometric features. It has been used for semantic labeling and classification tasks using various machine learning models, including Support Vector Machine (SVM), Random Forest (RF), Deep Neural Networks (DNN), PointNet++, and their combinations with Conditional Random Fields (CRF).Key Features: Original Vaihingen 3D Point Cloud Data, Inner-Cube and Outer-Cube Features for geometric property extraction, Labeled Data for Semantic Labeling, Compatible with ML and Deep Learning ModelsUsed in "Enhancing 3D Point Cloud Semantic Labeling by Introducing Innovative Cubic Neighborhoods" (This paper has been submitted and is awaiting review.)This dataset is publicly available for research purposes, particularly for point cloud classification, LiDAR data analysis, and ML-based 3D data processingFor more details, refer to the GitHub repository: https://github.com/FarzanehAghighi/InnerOuterCube-CRF-ML. (It will be uploaded soon)