Photogrammetry and Remote Sensing (Prof. Schindler)

Open Opportunities

Better maps of plant functional traits - towards planttraits.earth v2 ETH Zurich Photogrammetry and Remote Sensing (Prof. Schindler) Functional traits describe biophysically relevant properties of plants and form an important basis for understanding ecosystem dynamics and the Earth system. Planttraits.earth has recently produced global high-resolution maps of many plant traits (some of which have never before been mapped globally), by combining field data from plant scientists, crowd-sourced data from citizen scientists, and remote sensing imagery. The present project will develop methods to improve those maps and bring plant trait mapping to the next level. Ecology and Evolution, Information, Computing and Communication Sciences, Photogrammetry and Remote Sensing Master Thesis, Semester Project

Estimating Real-Height Digital Surface Models from a Single RGB Image Using Generative Models ETH Zurich Photogrammetry and Remote Sensing (Prof. Schindler) This thesis investigates the use of generative diffusion models for estimating Digital Surface Models (DSMs) with at least relative surface height from a single RGB image. While DSMs are traditionally derived from stereo imagery, monocular estimation offers a lightweight alternative for applications where only single-view input is available. Building on recent advances in monocular depth estimation, such as DepthAnythingV2 and Marigold, this work explores whether diffusion-based approaches can effectively bridge the gap between relative depth predictions and real-world surface structure. Information, Computing and Communication Sciences, Photogrammetry and Remote Sensing Bachelor Thesis, Master Thesis, Semester Project

Tree species identification using deep learning ETH Zurich Forest Resources Management Other organizations: Photogrammetry and Remote Sensing (Prof. Schindler) Tree species maps are crucial for effective forest management, biomass assessment, and biodiversity monitoring. Remote sensing products offer flexible and cost-effective ways to assess forest characteristics, while deep learning methods promise high predictive accuracy and transformative applications in forestry. This study aims to apply novel deep learning approaches to detect and identify individual trees and tree species in mixed forests. By addressing the challenges of tree species identification, this research will enhance biodiversity assessment, forest resilience understanding, and management strategies. Artificial Intelligence and Signal and Image Processing, Forestry Sciences, Geomatic Engineering ETH Zurich (ETHZ), Master Thesis, Semester Project