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Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI

Merkle, Nina and Bahmanyar, Gholamreza and Henry, Corentin and Azimi, Seyed Majid and Yuan, Xiangtian and Schopferer, Simon and Gstaiger, Veronika and Auer, Stefan and Schneibel, Anne and Wieland, Marc and Kraft, Thomas (2023) Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI. In: IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1-18. IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2.-6. Okt. 2023, Paris, Frankreich.

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In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time and effort required to collect such information by enabling a rapid survey of large areas. To achieve this, the main challenge is the automatic extraction of relevant information from remotely sensed data. In this work, we show how the combination of drone-based data with deep learning methods enables automated and large-scale situation assessment. In addition, we demonstrate the integration of onboard image processing techniques for the deployment of autonomous drone-based aid delivery. The results show the feasibility of a rapid and large-scale image analysis in the field, and that onboard image processing can increase the safety of drone-based aid deliveries.

Item URL in elib:https://elib.dlr.de/196563/
Document Type:Conference or Workshop Item (Poster)
Title:Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Merkle, NinaUNSPECIFIEDhttps://orcid.org/0000-0003-4177-1066UNSPECIFIED
Bahmanyar, GholamrezaUNSPECIFIEDhttps://orcid.org/0000-0002-6999-714X142554129
Azimi, Seyed MajidUNSPECIFIEDhttps://orcid.org/0000-0002-6084-2272UNSPECIFIED
Yuan, XiangtianUNSPECIFIEDhttps://orcid.org/0000-0001-7648-5938UNSPECIFIED
Gstaiger, VeronikaUNSPECIFIEDhttps://orcid.org/0000-0001-7328-7485UNSPECIFIED
Auer, StefanUNSPECIFIEDhttps://orcid.org/0000-0001-9310-2337142554131
Wieland, MarcUNSPECIFIEDhttps://orcid.org/0000-0002-1155-723XUNSPECIFIED
Kraft, ThomasUNSPECIFIEDhttps://orcid.org/0000-0003-3270-5606142554132
Date:October 2023
Journal or Publication Title:IEEE International Conference on Computer Vision Workshops (ICCVW)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-18
Keywords:Disaster Relief, Deep Learning, Remote Sensing
Event Title:IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Event Location:Paris, Frankreich
Event Type:international Conference
Event Dates:2.-6. Okt. 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence, R - Optical remote sensing
Location: Braunschweig , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Institute of Flight Systems > Unmanned Aircraft
German Remote Sensing Data Center > Geo Risks and Civil Security
Institute of Optical Sensor Systems > Security Research and Applications
Deposited By: Merkle, Nina
Deposited On:18 Sep 2023 14:39
Last Modified:20 Oct 2023 17:08

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