EcoVision Lab

Open Opportunities

Mapping spruce density using aerial imagery and deep learning ETH Zurich Forest Resources Management Other organizations: EcoVision Lab Climate change is increasing tree mortality due to drought and biotic infestations, but current detection methods are limited by data availability and low transferability. This study aims to use deep learning with true color near-infrared RGBI aerial imagery to detect spruce mortality in mixed forests. By integrating field inventories and RGB imagery, the method will be analyzed using R or ArcGIS Pro to accurately assess vegetation conditions. Environmental Sciences, Geomatic Engineering, Information, Computing and Communication Sciences Master Thesis, Semester Project

Enhancing tree species identification using multi-view Convolutional Neural Networks ETH Zurich Forest Resources Management Other organizations: EcoVision Lab Tree species identification is crucial for biodiversity monitoring, forest management, and understanding ecological processes. Advances in computer vision and deep learning have enabled the use of multi-view convolutional neural networks (CNNs) to classify species by integrating complementary information from different views. This thesis explores the integration of multi-view data and citizen science images to develop a scalable, high-accuracy tree species identification framework. By addressing challenges related to data variability and leveraging diverse georeferenced plant images, the study aims to enhance the training and generalization of multi-view CNN models. Computer Vision, Forestry Sciences, Photogrammetry and Remote Sensing Master Thesis