Deep Learning for Time-Series Analysis of Optical Satellite Imagery

Kondmann, Lukas (2023) Deep Learning for Time-Series Analysis of Optical Satellite Imagery. Dissertation, Technical University Munich.

PDF
29MB

Official URL: https://mediatum.ub.tum.de/?id=1705991

Abstract

In this cumulative thesis, I cover four papers on time-series analysis of optical satellite imagery. The contribution is split into two parts. The first one introduces DENETHOR and DynamicEarthNet, two landmark datasets with high-quality ground truth data for agricultural monitoring and change detection. Second, I introduce SiROC and SemiSiROC, two methodological contributions to label-efficient change detection.

Item URL in elib:

https://elib.dlr.de/199713/

Document Type:

Thesis (Dissertation)

Title:

Deep Learning for Time-Series Analysis of Optical Satellite Imagery

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Kondmann, Lukas	UNSPECIFIED	https://orcid.org/0000-0002-2253-6936	UNSPECIFIED

Date:

24 April 2023

Refereed publication:

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Number of Pages:

140

Status:

Published

Keywords:

Change detection, agricultural monitoring

Institution:

Technical University Munich

Department:

TUM School of Engineering and Design

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

Location:

Oberpfaffenhofen

Institutes and Institutions:

Remote Sensing Technology Institute > EO Data Science

Deposited By:

Camero, Dr Andres

Deposited On:

28 Nov 2023 12:47

Last Modified:

28 Nov 2023 12:47

Repository Staff Only: item control page