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Automatic Object Segmentation To Support Crisis Management Of Large-scale Events

Azimi, Seyed Majid and Kiefl, Ralph and Gstaiger, Veronika and Bahmanyar, Reza and Merkle, Nina Marie and Henry, Corentin and Rosenbaum, Dominik and Kurz, Franz (2021) Automatic Object Segmentation To Support Crisis Management Of Large-scale Events. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 433-440. ISPRS 2021, 5.-9. Juli 2021, Nice, Frankreich. doi: 10.5194/isprs-archives-XLIII-B2-2021-433-2021. ISSN 1682-1750.

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Official URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/433/2021/

Abstract

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.

Item URL in elib:https://elib.dlr.de/144380/
Document Type:Conference or Workshop Item (Poster)
Title:Automatic Object Segmentation To Support Crisis Management Of Large-scale Events
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Azimi, Seyed Majidseyedmajid.azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Kiefl, RalphRalph.Kiefl (at) dlr.dehttps://orcid.org/0000-0001-7622-5458
Gstaiger, VeronikaVeronika.Gstaiger (at) dlr.deUNSPECIFIED
Bahmanyar, Rezareza.bahmanyar (at) dlr.dehttps://orcid.org/0000-0002-6999-714X
Merkle, Nina MarieNina.Merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066
Henry, CorentinCorentin.henry (at) dlr.dehttps://orcid.org/0000-0002-4330-3058
Rosenbaum, DominikDominik.Rosenbaum (at) dlr.deUNSPECIFIED
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004
Date:June 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
DOI :10.5194/isprs-archives-XLIII-B2-2021-433-2021
Page Range:pp. 433-440
Series Name:XLIII-B2-2021
ISSN:1682-1750
Status:Published
Keywords:Crisis Management, Segmentation, Aerial Imagery, Large-scale Events, Machine Learning
Event Title:ISPRS 2021
Event Location:Nice, Frankreich
Event Type:international Conference
Event Dates:5.-9. Juli 2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe, R - Optical remote sensing, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Azimi, Seyedmajid
Deposited On:08 Oct 2021 12:27
Last Modified:15 Nov 2021 13:26

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