Theses and Projects: Difference between revisions

Theses and Projects (view source)

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 * (P) Student project
+===  * '''New!''' 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!''' 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]
+===  * '''New!''' 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]
 ===  * '''New!''' 3D natural hazard simulator  ===
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 Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de], Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/M/P)
-=== * [Occupied] Convolutional neural networks and transfer learning for change estimation with satellite images ===
-Satellite images are a popular input to estimate wealth measures (e.g. income or consumption) on the spatial scale, so to determine which locations are richer or poorer than others within a certain time interval. However, the use of these images for estimation of the changes in these measures over time for given locations is investigated only insufficiently. This project/thesis topic intends to address this problem by applying Convolutional Neural Networks (CNNs) with Transfer Learning on a small data set of images from villages in Thailand and Vietnam. Among other things, this topic contains experimental comparisons of different approaches and CNNs in both Regression and Classification.
-Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de](B/M/P)
 === * [Occupied] AI for Games  ===