In this thesis, a method for detecting ditches using digital elevation data derived from LiDAR scans was developed in collaboration with the Swedish Forest Agency. The objective was to compare a machine learning based method with a state-of-the-art automated method, and to determine which LiDAR-based features represent the strongest ditch predictors. This was done by using the digital elevation data to develop several new features, which were used as inputs in a random forest machine learning classifier. The output from this classifier was processed to remove noise, before a binarisation process produced the final ditch prediction. Several metrics including Cohen's Kappa index were calculated to evaluate the performance of the method. These metrics were then compared with the metrics from the results of a reproduced state-of-the-art automated method. The confidence interval for the Cohen's Kappa metric for the population was calculated to be [0.567 , 0.645] with a 95 % certainty. Features based on the Impoundment attribute derived from the digital elevation data overall represented the strongest ditch predictors. Our method outperformed the state-of-the-art automated method by a high margin. This thesis proves that it is possible to use AI and machine learning with digital elevation data to detect ditches to a substantial extent.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hj-45261 |
| Date | January 2019 |
| Creators | Flyckt, Jonatan, Andersson, Filip |
| Publisher | Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik |
| 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 0.0023 seconds