Photogrammetry and Remote Sensing (Prof. Schindler)

Open Opportunities

Tree species identification using deep learning ETH Zurich Forest Resources Management Other organizations: Photogrammetry and Remote Sensing (Prof. Schindler) Tree species maps are essential for better forest management, forest cover, biomass, and biodiversity assessment. The temporal and spatial location and identification of tree species is extremely important and necessary for forest management and conservation. The use of remote sensing products in forestry allows for time flexible and cost-effective assessment of forest characteristics. Deep learning methods enable high predictive accuracy and have the potential to revolutionize forestry understanding, data collection and enable the development of numerous applications. Tree species identification is essential for assessing biodiversity, understanding forest resilience to climate change, and developing forest management strategies. However, identifying tree species is challenging, and further research needs to focus on developing new models to address this issue. Artificial Intelligence and Signal and Image Processing, Forestry Sciences, Geomatic Engineering ETH Zurich (ETHZ), Master Thesis

Detecting biodiversity promotion elements in the Swiss agricultural landscape Agroscope Agroecology and Environment Other organizations: Photogrammetry and Remote Sensing (Prof. Schindler) This master's thesis aims to develop a deep learning framework to automatically detect and map biodiversity promotion elements in the Swiss agricultural landscape. Computer Vision, Photogrammetry and Remote Sensing Master Thesis, Semester Project