Collective Sensing and artificial intelligence techniques for natural hazard risk and impact assessment

Geiß, Christian (2025) Collective Sensing and artificial intelligence techniques for natural hazard risk and impact assessment. Habilitation, Julius-Maximilians-University of Würzburg.

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Item URL in elib:

https://elib.dlr.de/214526/

Document Type:

Thesis (Habilitation)

Title:

Collective Sensing and artificial intelligence techniques for natural hazard risk and impact assessment

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Geiß, Christian	Christian.Geiss (at) dlr.de	https://orcid.org/0000-0002-7961-8553	UNSPECIFIED

DLR Supervisors:

Contribution	DLR Supervisor	Institution or E-Mail	DLR Supervisor's ORCID iD
Thesis advisor	Dech, Stefan	Stefan.Dech (at) dlr.de	UNSPECIFIED

Date:

2025

Open Access:

Yes

Number of Pages:

Status:

Published

Keywords:

multimodal earth vision, machine learning, AI, natural hazard risk

Institution:

Julius-Maximilians-University of Würzburg

Department:

Department of Geography and Geology

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 - Remote Sensing and Geo Research

Location:

Oberpfaffenhofen

Institutes and Institutions:

German Remote Sensing Data Center > Geo Risks and Civil Security

Deposited By:

Geiß, Christian

Deposited On:

03 Jul 2025 16:17

Last Modified:

03 Jul 2025 16:17

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