Evaluation of several software packages in context of point cloud classification
The national survey agency is responsible for generating nation-wide digital terrain and surface models (DTM and DSM). For this reason, they are gathering Airborne Laser Scanning (ALS) data of their area every three years, which have a rather coarse resolution of 4~10 points/m². At the same time, they derive point clouds from optical image data using a method called ‘Dense Image Matching’ (DIM) resulting in point clouds with a resolution of 100 points/m².
The goal of this project is to evaluate established software tools for both data types (ALS and DIM). The data sets have to be classified using ArcGIS software as well as an approach proposed by Maltezos and Ioannidis (2015). In this paper, they use a combination of cloud compare functions to calculate normal and roughness features of the point clouds as well as some written code, which utilizes those features for classification. Lastly, both classification methods on both data sets have to be evaluated using manually classified reference point clouds.
- Preparation of ground truth labels for a certain area within the data set into several classes using ArcGIS
- Classification of ALS and DIM data using ArcGIS tools
- Classification of ALS and DIM data using Cloud Compare tools and own code according to Maltezos and Ioannidis (2015)*
- Evaluation of the obtained results with the ground truth (completeness, correctness, quality)
- Optional: classification of ALS and DIM data using other software packages
*Maltezos, E. & Ioannidis, C., 2015: Automatic detection of building points from lidar and dense image matching point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5, p. 33-40.
- ArcGIS 10.4
- Cloud Compare
- Knowledge in programming with Python or Matlab
Florian Politz (Email florian.politzikg.uni-hannover.de, Tel. 762-19436)
Institute of Cartography and Geoinformatics, Appelstraße 9a, 30167 Hannover, room 616