The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021

  28 Jun 2021

28 Jun 2021

DETERMINATION OF PARKING SPACE AND ITS CONCURRENT USAGE OVER TIME USING SEMANTICALLY SEGMENTED MOBILE MAPPING DATA

A. Leichter, U. Feuerhake, and M. Sester A. Leichter et al.
  • Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Germany

Keywords: Smart City, Mobile Mapping, Deep Learning, Data Fusion, Parking, Public Space

Abstract. Public space is a scarce good in cities. There are many concurrent usages, which makes an adequate allocation of space both difficult and highly attractive. A lot of space is allocated by parking cars – even if the parking spaces are not occupied by cars all the time. In this work, we analyze space demand and usage by parking cars, in order to evaluate, when this space could be used for other purposes. The analysis is based on 3D point clouds acquired at several times during a day. We propose a processing pipeline to extract car bounding boxes from a given 3D point cloud. For the car extraction we utilize a label transfer technique for transfers from semantically segmented 2D RGB images to 3D point cloud data. This semantically segmented 3D data allows us to identify car instances. Subsequently, we aggregate and analyze information about parking cars. We present an exemplary analysis of the urban area where we extracted 15.000 cars at five different points in time. Based on this aggregated we present analytical results for time dependent parking behavior, parking space availability and utilization.