The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 123–129, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-123-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 123–129, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-123-2020

  24 Aug 2020

24 Aug 2020

USING DEEP LEARNING TO DIGITIZE ROAD ARROW MARKINGS FROM LIDAR POINT CLOUD DERIVED IMAGES

M. L. R. Lagahit and Y. H. Tseng M. L. R. Lagahit and Y. H. Tseng
  • Dept. of Geomatics, National Cheng Kung University, Tainan, Taiwan

Keywords: Deep Learning, Neural Network, Image Segmentation, Mobile Mapping, LIDAR Point Cloud, Road Marking

Abstract. The concept of Autonomous vehicles or self-driving cars has recently been gaining a lot of popularity. Because of this, a lot of research is being done to develop the technology. One of which is High Definition (HD) Maps, which are centimeter-level precision 3D maps that contain a lot of geometric and semantic information about the road which can assist the AV when driving. An important component of HD maps is the road markings which indicates a set of rules on how a vehicle should navigate itself on the road. For example, lane lines indicate which part of the road a vehicle can drive on in a certain direction. This research proposes a methodology that uses deep learning techniques to detect road arrows, road markings that show possible driving directions, on LIDAR derived images, and extract them as polyline vector shapefiles. The general workflow consists of (1) converting the LIDAR point cloud to images, (2) training and applying U-Net – a fully convolutional neural network, (3) creating masks from image segmentation results that have been transformed to fit the local coordinates, (4) extracting the polygons and polylines, and finally (5) exporting the vectors in shapefile format. The proposed methodology has shown promising results with object segmentation accuracies comparable with previous related works.