As automation is on the rise in the world at large, the ability to automatically differentiate objects in datasets via machine learning is of growing interest. This report details an experimental evaluation of supervised learning on point cloud data using random forest with varying setups. Acquired via airborne LiDAR using drones, the data holds a 3D representation of a landscape area containing power line corridors. Segmentation was performed with the goal of isolating data points belonging to power line objects from the rest of the surroundings. Pre-processing was performed on the data to extend the machine learning features used with geometry-based features that are not inherent to the LiDAR data itself. Due to how large-scale the data is, the labels were generated by the customer, Airpelago, and supervised learning was applied using this data. With their labels as benchmark, F1 scores of over 90% could be generated for both of the classes pertaining to power line objects. The best results were obtained when the data classes were balanced and both relevant intrinsic and extended features were used for the training of the classification models.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-196171 |
| Date | January 2023 |
| Creators | Li Persson, Leon |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0024 seconds