Using of machines learning in extraction of urban roads from DEM of LIDAR data: Case study at Baghdad expressways, Iraq

Abstract

Road extraction from remotely perceived information may be a difficult issue and has been approached in many various ways in which by photogrammetrists and digital image processors. This study was given extraction of roads from DEM of LiDAR with IKONOS image using machines learning (ML). Two set of data were used in this study. IKONOS Image and Digital Elevation Model (DEM) data were combined to produce thematic mapping. The spatial resolution of data is 1 m and was acquired on 2010. The result f thematic map based on theses images and the methods was used three models of machines learning. The problem of this study, when was used the LIDAR data to extract the road is very difficult because the LiDAR data is too noisy and employed it also so hard. Moreover, this article will describe an effective and compare between several machines learning algorithms (RF, BYO and MLP) for detection the roads from LIDAR data. The statistical indictors such as an overall accuracy (OA), kappa analysis statistic (K), (MAE) which is Mean Absolute Error and finally the (RMSE) which is Root Mean Squared Error. All these will be use to get the accuracy of classification assessment and the best model to produce the thematic map.