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Project Overview

Project Code: ED 23

Project name:

4D Remote Sensing Fusion Strategies for Environmental Monitoring

TUM Department:

ED - Aerospace and Geodesy

TUM Chair / Institute:

Professorship of Remote Sensing Applications

Research area:

Photogrammetry and Remote Sensing

Student background:

Aerospace / GeodesyComputer Science/ InformaticsEnvironmental Engineering

Further disciplines:

Participation also possible online only:

Planned project location:

Campus Ottobrunn (not city center)
Lise-Meitner-Str. 9
85521 Ottobrunn

Project Supervisor - Contact Details


Title:

Prof. Dr.

Given name:

Katharina

Family name:

Anders

E-mail:

k.anders@tum.de

Phone:

00498928955780

Additional Project Supervisor - Contact Details


Title:

Given name:

Jiapan

Family name:

Wang

E-mail:

jiapan.wang@tum.de

Phone:

Additional Project Supervisor - Contact Details


Title:

Dr.

Given name:

Mathilde

Family name:

Letard

E-mail:

mathilde.letard@tum.de

Phone:

Project Description


Project description:

The objective of this project is to develop a spatiotemporal data fusion strategy for multi-source 4D remote sensing data in the context of improved monitoring of river environments. The method is required to integrate multi-source data with different spatial and temporal sampling, in order to derive accurate information about sediment dynamics and evolution/transport of deadwood in an automated manner. Data will comprise terrestrial and UAV point clouds acquired by laser scanning and photogrammetry, as well as aerial hyperspectral imagery, and possibly optical and radar satellite observations. Input data is readily available from a natural river stretch of the Isar (https://t1p.de/r52ea). There is the option to participate in fieldwork during August 2025.
The student will investigate different fusion strategies (point cloud- and image-based) with a particular focus on the time series character of the heterogeneous data sources. Methods may stem from state-of-the-art computer vision algorithms and recent machine learning / deep learning capabilities. The expected outcome is an automatic tool that fuses diverse remote sensing data sources and provides a dataset for subsequent change analysis. This provides an important contribution to operational environmental monitoring for different geographic environments.
Subject related prerequisites are motivation to process image/geospatial data and basic programming skills, preferably in Python.
The project will be part of the research project Extract4D (https://www.asg.ed.tum.de/en/rsa/research/extract4d/).

Working hours per week planned:

35

Prerequisites


Required study level minimum (at time of TUM PREP project start):

2 years of bachelor studies completed

Subject related:

Subject related prerequisites are motivation to process image/geospatial data and basic programming skills, preferably in Python.

Other:

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