Theses and Projects - Revision history

Woelk1: /* Open Theses and Student Project Topics */

2026-01-16T17:48:46Z

Open Theses and Student Project Topics

← Older revision		Revision as of 19:48, 16 January 2026
Line 71:		Line 71:
	Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]		Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]

	~~=== [Occupied] Image-to-Image Translation of Different Nightlight Image Types (B/M/P) ===~~

	Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated.

	~~Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]~~

	~~=== [Occupied] Satellite Image Indices and Machine Learning for Socio-economic Estimation (B/M/P) ===~~

	There are several indices, which can be derived from satellite images. For example the Normalized Difference Vegetation Index (NDVI) indicates the presence and condition of vegetation, while the Normalized Difference Built-up Index (NDBI) indicates the presence of built-up areas such as buildings or roads. The distributions of these and other indices may have different explanatory power to estimate the socio-economic status of locations. Therefore in this project/thesis the regression performance of machine learning models - using statistics of these indices as features - to estimate socio-economic indicators shall be evaluated for the individual and also combined indices. Optionally, Convolutional Neural Networks (CNNs) can be applied additionally, which take the derived index images as input.

	~~Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]~~

	=== * 3D natural hazard simulator ===		=== * 3D natural hazard simulator ===

Woelk1: /* Open Theses and Student Project Topics */

2026-01-16T17:48:09Z

Open Theses and Student Project Topics

← Older revision		Revision as of 19:48, 16 January 2026
Line 58:		Line 58:


	~~=== * '''New!''' Context Specific Self-supervised Pre-Training for Remote Sensing Applications (Semantic Segmentation, Change Detection, Socio-Economic Indicator Estimation, ...) (B/M/P) ===~~

	Satellite images in combination with Machine/Deep Learning models have shown to be an effective tool for analysis and monitoring tasks regarding disasters, deforestation, climate change, socio-economic estimation and others. The training of these models usually rely on labelled ground-truth data, which is labour intensive and therefore often scarcely available. To overcome this limitation, models are often trained in self-supervised approaches with unlabelled data, such as Contrastive Learning or Masked Autoencoders. However, these approaches are completely independent and not related to the intended downstream task. In this project/thesis the relationship between the pre-training task and the model performance on the downstream task will be explored and self-supervised training approaches tailored for a selected remote sensing downstream task (semantic segmentation of trees, tree crown share per pixel estimation, change detection of disasters, socio-economic estimation, ...) will be developed.

	~~Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]~~


	~~=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===~~

	A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.

	~~Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]~~

	=== * '''New!''' Emotional Support Conversation Generation based on LLM (B/M/P)===		=== * '''New!''' Emotional Support Conversation Generation based on LLM (B/M/P)===

Wwu1 at 08:25, 9 April 2025

2025-04-09T08:25:49Z

← Older revision		Revision as of 10:25, 9 April 2025
Line 38:		Line 38:

	Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]		Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]

			=== * '''New!''' Using LLM for Sign Language Translation (B/M/P)===

			Sign language is the primary means of communication for the deaf and hard-of-hearing community, yet most people do not understand it. This topic explores the integration of Large Language Models (LLMs) with computer vision to build an advanced sign language translation system - with special focus on overcoming the critical challenge of understanding long, continuous sign language videos. We welcome students passionate about Natural Language Processing (NLP) and Computer Vision (CV) to explore the cutting edge of sign language translation technology.

			Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]

	=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===		=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===

	Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure~~. We need students for this topic~~. We expect you have a background in knowledge graph and programming skills in Python.		Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We expect you have a background in knowledge graph and programming skills in Python.

	Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]		Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]

Yuan4 at 09:47, 8 April 2025

2025-04-08T09:47:18Z

← Older revision		Revision as of 11:47, 8 April 2025
Line 119:		Line 119:
	Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)		Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)

	=== * Privacy-preserved Video Analytics===		=== * '''[Closed]''' Privacy-preserved Video Analytics===
	This project/thesis topic focuses on the protection of privacy in video analytics.		This project/thesis topic focuses on the protection of privacy in video analytics.

Line 134:		Line 134:
	[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].		[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].

	=== * AI for networking adaption ===		=== * '''[Closed]''' AI for networking adaption ===
	In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).		In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).
	You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.		You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.

Dwu1 at 15:16, 25 March 2025

2025-03-25T15:16:31Z

← Older revision		Revision as of 17:16, 25 March 2025
Line 26:		Line 26:

	Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]		Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]


	=== * '''New!''' Efficient Live Volumetric Video Streaming System===		=== * '''New!''' Efficient Live Volumetric Video Streaming System===

Dwu1 at 15:16, 25 March 2025

2025-03-25T15:16:10Z

← Older revision		Revision as of 17:16, 25 March 2025
Line 20:		Line 20:
	* (M) Master thesis		* (M) Master thesis
	* (P) Student project		* (P) Student project

			=== * '''New!''' Task Offloading and Resource Allocation Optimization===

			With the continuous advancement of the next-generation wireless communication technologies and the population of mobile devices, a variety of Internet of Things (IoT) applications are emerging and seeking efficient task execution paradigms. This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency. We expect you have a background in edge computing, optimization algorithms, and programming skills.

			Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]

Woelk1: /* Open Theses and Student Project Topics */

2025-03-20T10:07:09Z

Open Theses and Student Project Topics

← Older revision		Revision as of 12:07, 20 March 2025
Line 45:		Line 45:

	Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]		Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]


			=== * '''New!''' Context Specific Self-supervised Pre-Training for Remote Sensing Applications (Semantic Segmentation, Change Detection, Socio-Economic Indicator Estimation, ...) (B/M/P) ===

			Satellite images in combination with Machine/Deep Learning models have shown to be an effective tool for analysis and monitoring tasks regarding disasters, deforestation, climate change, socio-economic estimation and others. The training of these models usually rely on labelled ground-truth data, which is labour intensive and therefore often scarcely available. To overcome this limitation, models are often trained in self-supervised approaches with unlabelled data, such as Contrastive Learning or Masked Autoencoders. However, these approaches are completely independent and not related to the intended downstream task. In this project/thesis the relationship between the pre-training task and the model performance on the downstream task will be explored and self-supervised training approaches tailored for a selected remote sensing downstream task (semantic segmentation of trees, tree crown share per pixel estimation, change detection of disasters, socio-economic estimation, ...) will be developed.

			Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]


	=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===		=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

Yuan4: /* Open Theses and Student Project Topics */

2025-03-18T14:39:45Z

Open Theses and Student Project Topics

← Older revision		Revision as of 16:39, 18 March 2025
Line 33:		Line 33:

	Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]		Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]

	~~=== * '''New!''' Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks===~~

	The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. We need students for this topic. We expect you have a background in computer network and programming skills in Python.

	~~Please contact Jin Xie [jin.xie@stud.uni-goettingen.de]~~

	=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===		=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===

Woelk1: /* * [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) */

2025-02-17T16:06:16Z

* [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P)

← Older revision		Revision as of 18:06, 17 February 2025
Line 52:		Line 52:
	Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]		Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]

	=== * [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===		=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

	A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.		A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.

Woelk1: /* * New! Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) */

2025-02-17T16:06:03Z

* New! Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P)

← Older revision		Revision as of 18:06, 17 February 2025
Line 52:		Line 52:
	Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]		Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]

	=== * ~~'''New!'''~~ Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===		=== * [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

	A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.		A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.