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Citywide estimation of parking space using aerial imagery and OSM data fusion with deep learning and fine-grained annotation

Henry, Corentin and Hellekes, Jens and Merkle, Nina and Azimi, Seyedmajid and Kurz, Franz (2021) Citywide estimation of parking space using aerial imagery and OSM data fusion with deep learning and fine-grained annotation. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII, pp. 479-485. ISPRS 2021, 04.-10. July 2021, Nice, France (virtual event). doi: 10.5194/isprs-archives-XLIII-B2-2021-479-2021. ISSN 1682-1750.

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Official URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/479/2021/isprs-archives-XLIII-B2-2021-479-2021.pdf

Abstract

Emerging traffic management technologies, smart parking applications, together with transport researchers and urban planners are interested in fine-grained data on parking space in cities. However, there are no standardized, complete and up-to-date databases for many urban areas. Moreover, manual data collection is expensive and time-consuming. Aerial imagery of entire cities can be used to inventory not only publicly accessible and dedicated parking lots, but also roadside parking areas and those on private property. For a realistic estimation of the total parking space, the observed use of multi-functional traffic areas is taken into account by segmenting not only parking areas but also roads according to their purpose. In this paper, different U-Net based architectures are tested for detecting all these types of visible traffic areas. A new large-scale, high-quality dataset of manual annotations is used in combination with selected contextual information from OpenStreetMap (OSM) to depict the actual use as parking space. Our models achieve a good performance on parking area segmentation, and we show the significant impact of OSM data fusion in deep neural networks on the simultaneous extraction of multiple traffic areas compared to using aerial imagery alone.

Item URL in elib:https://elib.dlr.de/142407/
Document Type:Conference or Workshop Item (Speech)
Title:Citywide estimation of parking space using aerial imagery and OSM data fusion with deep learning and fine-grained annotation
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Henry, Corentincorentin.henry (at) dlr.deUNSPECIFIED
Hellekes, JensJens.Hellekes (at) dlr.dehttps://orcid.org/0000-0002-0080-3124
Merkle, NinaNina.Merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.deUNSPECIFIED
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004
Date:July 2021
Journal or Publication Title:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:XLIII
DOI :10.5194/isprs-archives-XLIII-B2-2021-479-2021
Page Range:pp. 479-485
ISSN:1682-1750
Status:Published
Keywords:Deep Learning, Aerial Imagery, Image Segmentation, Parking Space Management, OpenStreetMap
Event Title:ISPRS 2021
Event Location:Nice, France (virtual event)
Event Type:international Conference
Event Dates:04.-10. July 2021
Organizer:International Society for Photogrammetry and Remote Sensing (ISPRS)
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 - UrMo Digital, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Merkle, Nina Marie
Deposited On:31 May 2021 14:14
Last Modified:30 Nov 2021 18:08

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