Abstract
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.
Highlights
Streets, sidewalks, roads or public spaces in general are places where advantages and disadvantages of urban life lead to overlapping challenges (de Magalhaes and Carmona, 2009)
With the temporal evaluation of the parking space we show different applications which are based on our mapping of the MPS
In order to evaluate the proposed approach, we conducted an experiment based on data obtained from an already existing mapping campaign
Summary
Sidewalks, roads or public spaces in general are places where advantages and disadvantages of urban life lead to overlapping challenges (de Magalhaes and Carmona, 2009). As the demands on cities intensify, shared use becomes a competition for this limited resource. To address this development, it is crucial to quantify public space itself and its usage. In this paper we propose to solve this task by exploiting the continuous acquisition of environmental data with vehicle sensors and the subsequent application of a deep learning (DL) model tailored to semantic segmentation of mobile mapping data (Peters and Brenner, 2019). The segmented data is aggregated to retrieve spatial and statistical usage information
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