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Theses and Projects: Difference between revisions
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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) === | ||