EcoVision LabOpen OpportunitiesUnderstanding forest structure, including species-specific density, is essential for sustainable forest management and climate resilience. Spruce (Picea abies) a dominant tree species in Swiss forests, plays a critical ecological and economic role, yet quantifying its density over large areas remains challenging. While traditional methods rely on labor-intensive field surveys, advancements in high-resolution remote sensing and deep learning enable the extraction of tree-level information across large spatial scales. True-color near-infrared (RGBI) aerial imagery, combined with deep learning, provides a promising approach for accurate, scalable mapping of spruce density. - Environmental Sciences, Geomatic Engineering, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Tree species identification is essential 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. While such techniques hold promise, the variability in resolution, size, and perspective of the data presents challenges that must be addressed for robust identification. Citizen science data provide a wealth of georeferenced plant images captured by volunteers. These datasets, which include diverse environments, seasonal conditions, and perspectives, can enhance the training and generalization of multi-view CNN models. This thesis explores the integration of multi-view data and citizen science images to develop a scalable, high-accuracy tree species identification framework. - Computer Vision, Forestry Sciences, Photogrammetry and Remote Sensing
- Master Thesis
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