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Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

Hellekes, Jens and Kehlbacher, Ariane and López Díaz, María and Merkle, Nina Marie and Henry, Corentin and Kurz, Franz and Heinrichs, Matthias (2022) Parking space inventory from above: Detection on aerial images and estimation for unobserved regions. IET Intelligent Transport Systems, pp. 1-13. Institution of Engineering and Technology (IET). doi: 10.1049/itr2.12322. ISSN 1751-956X.

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Official URL: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/itr2.12322

Abstract

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. We find a systematic relationship between the road length and type and the parking area obtained. We suggest that our results are informative to those needing information on parking in structurally similar regions.

Item URL in elib:https://elib.dlr.de/191145/
Document Type:Article
Title:Parking space inventory from above: Detection on aerial images and estimation for unobserved regions
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hellekes, JensUNSPECIFIEDhttps://orcid.org/0000-0002-0080-3124UNSPECIFIED
Kehlbacher, ArianeUNSPECIFIEDhttps://orcid.org/0000-0003-3898-858XUNSPECIFIED
López Díaz, MaríaUNSPECIFIEDhttps://orcid.org/0000-0002-7986-3970UNSPECIFIED
Merkle, Nina MarieUNSPECIFIEDhttps://orcid.org/0000-0003-4177-1066UNSPECIFIED
Henry, CorentinUNSPECIFIEDhttps://orcid.org/0000-0002-4330-3058UNSPECIFIED
Kurz, FranzUNSPECIFIEDhttps://orcid.org/0000-0003-1718-0004UNSPECIFIED
Heinrichs, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-0175-2787UNSPECIFIED
Date:29 December 2022
Journal or Publication Title:IET Intelligent Transport Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1049/itr2.12322
Page Range:pp. 1-13
Publisher:Institution of Engineering and Technology (IET)
ISSN:1751-956X
Status:Published
Keywords:Aerial imagery; Deep learning; Image segmentation; Parking space detection; On-street parking; Bayes methods; OpenStreetMap
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 - VMo4Orte - Vernetzte Mobilität für lebenswerte Orte
Location: Berlin-Adlershof , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Institute of Transport Research > Mobility and Urban Development
Deposited By: Hellekes, Jens
Deposited On:29 Nov 2022 14:24
Last Modified:28 Apr 2023 16:57

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