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Detecting and Estimating On-street Parking Areas from Aerial Images

López Díaz, María and Kehlbacher, Ariane and Hellekes, Jens and Merkle, Nina and Henry, Corentin and Heinrichs, Matthias (2022) Detecting and Estimating On-street Parking Areas from Aerial Images. Transportation Research Board (TRB) 101st Annual Meeting, 2022-01-09 - 2022-01-13, Washington, D.C., USA.

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Abstract

Parking is an essential part of transportation systems and urban planning, but the availability of data on parking is limited and therefore posing problems, for example, estimating search times for parking spaces in travel demand models. This paper presents an on-street parking area prediction model developed using remote sensing and open geospatial data of the German city of Brunswick. Neural networks are used to segment the aerial images in parking and street areas. To enhance the robustness of this detection, multiple predictions over same regions are fused. We enrich this information with publicly available data and formulate a Bayesian inference model to predict the parking area per street meter. The model is estimated and validated using detected parking areas from the aerial images. We find that the prediction accuracy of the parking area model at mid to high levels of parking area per street meter is good, but at lower levels uncertainty increases. Using a Bayesian inference model allows the uncertainty of the prediction to be passed on to subsequent applications to track error propagation. Since only open source data serve as input for the prediction model, a transfer to structurally similar regions, for which no aerial images are available, is possible. The model can be used in a wide range of applications like travel demand models, parking regulation and urban planning.

Item URL in elib:https://elib.dlr.de/148454/
Document Type:Conference or Workshop Item (Poster)
Title:Detecting and Estimating On-street Parking Areas from Aerial Images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
López Díaz, MaríaUNSPECIFIEDhttps://orcid.org/0000-0002-7986-3970UNSPECIFIED
Kehlbacher, ArianeUNSPECIFIEDhttps://orcid.org/0000-0003-3898-858XUNSPECIFIED
Hellekes, JensUNSPECIFIEDhttps://orcid.org/0000-0002-0080-3124UNSPECIFIED
Merkle, NinaUNSPECIFIEDhttps://orcid.org/0000-0003-4177-1066UNSPECIFIED
Henry, CorentinUNSPECIFIEDhttps://orcid.org/0000-0002-4330-3058UNSPECIFIED
Heinrichs, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-0175-2787UNSPECIFIED
Date:2022
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:aerial imagery, deep learning, image segmentation, parking space detection, on-street parking, Bayesian inference, OpenStreetMap
Event Title:Transportation Research Board (TRB) 101st Annual Meeting
Event Location:Washington, D.C., USA
Event Type:international Conference
Event Start Date:9 January 2022
Event End Date:13 January 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr (old), V - UrMo Digital (old), R - Optical remote sensing
Location: Berlin-Adlershof , Oberpfaffenhofen
Institutes and Institutions:Institute of Transport Research > Mobility and Urban Development
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: López Díaz, María
Deposited On:09 Feb 2022 11:14
Last Modified:24 Apr 2024 20:46

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