| AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS |
| Paper ID : 1118-SMPR |
| Authors: |
|
Fabio Remondino *, Emre Ozdemir FBK Trento, Italy |
| Abstract: |
| With the advances in technology, 3D city models became more available for many implementations, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are generated have been evolving towards automation with different approaches. As most of these approaches are based on point clouds with proper semantic classes, our aim is to classify aerial point clouds into four semantic classes as follows: ground level objects (including roads and pavements), vegetation, buildings’ facades and buildings’ roofs. In this study we tested various machine learning algorithms for point cloud classification, including three deep learning algorithms and one machine learning algorithm. The aim is to evaluate their performances and results. In our experiments, we used several geometric features, where four of them are custom features. Moreover, unconventionally, we utilized these geometric features also for deep learning. |
| Keywords: |
| point cloud, classification, machine learning, deep learning, urban areas, geometric features |
| Status : Conditional Accept (Oral Presentation) |
