Open Semester Projects and Thesis
Note: Topics labeled as "semester project" are suitable for the "Geomatics Project I" or "Geomatics Project II" in the MSc curriculum 2022.
Pixel to Vector: Converting Semantic Segmentation of Roofs into Regularised Vector Representations with Realistic Appearance
This thesis investigates the transformation of semantic segmentation roof maps into regularized vector representations with realistic appearance attributes. The study focuses on bridging the gap between raster-based segmentation outputs and high-quality vector models suitable for GIS and 3D urban applications. By integrating segmentation post-processing, vectorization, polygon regularization, and appearance attribution, the thesis aims to create geometrically accurate and visually plausible roof models that can be used for urban planning, digital twins, and visualization purposes.
Keywords
Vectorization, Image processing, Remote Sensing
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Bachelor Thesis
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Published since: 2025-12-17 , Earliest start: 2025-12-01 , Latest end: 2026-04-30
Organization Photogrammetry and Remote Sensing (Prof. Schindler)
Hosts Nicolicioiu Armand
Topics Information, Computing and Communication Sciences , Engineering and Technology
Remote Sensing Image Instance Segmentation
This project aims to develop a robust instance segmentation model for remote sensing imagery. The goal is to adapt existing architectures (like Mask2Former) and leverage foundation models (like SAM) to accurately detect and segment individual objects (e.g., buildings, vehicles) in large-scale aerial datasets like iSAID.
Keywords
instance segmentation, remote sensing, aerial imagery, deep learning, computer vision, foundation models
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Semester Project
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Published since: 2025-10-30 , Earliest start: 2025-10-31 , Latest end: 2027-09-30
Applications limited to ETH Zurich
Organization Photogrammetry and Remote Sensing (Prof. Schindler)
Hosts Chen Yuxing
Topics Engineering and Technology
Distilling a Semantic Change Representation from Mono-Temporal Pretrained Features for Unsupervised Change Analysis
This project develops an unsupervised framework to learn a dense semantic representation of change from bi-temporal remote sensing images. By designing a "Change Distillation" module that processes features from a pretrained backbone (e.g., DINOv2), the goal is to generate a single "change vector" per pixel that explicitly encodes the "from-to" transaction, enabling the automatic discovery of different semantic change types.
Keywords
unsupervised change detection, semantic change analysis, remote sensing, feature distillation, pretrained models, representation learning
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Semester Project , Bachelor Thesis
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Published since: 2025-10-30 , Earliest start: 2025-10-31 , Latest end: 2027-09-30
Applications limited to ETH Zurich
Organization Photogrammetry and Remote Sensing (Prof. Schindler)
Hosts Chen Yuxing
Topics Engineering and Technology
Unsupervised Remote Sensing Image Segmentation using Slot Attention and Pretrained Features
This project develops an unsupervised segmentation framework for remote sensing (RS) imagery. By adapting the Slot Attention mechanism and integrating features from large pretrained models (like DINOv2 or Prithvi), the goal is to automatically discover and segment detailed land cover classes without pixel-level labels.
Keywords
unsupervised semantic segmentation, remote sensing, slot attention, self-supervised learning, feature extraction
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Semester Project , Bachelor Thesis
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Published since: 2025-10-30 , Earliest start: 2025-10-31 , Latest end: 2027-09-30
Applications limited to ETH Zurich , Department of Civil, Environmental and Geomatic Engineering
Organization Photogrammetry and Remote Sensing (Prof. Schindler)
Hosts Chen Yuxing
Topics Engineering and Technology
Structured Roof Geometry Generation from Remote Sensing Imagery Using Conditional Diffusion Models
This thesis explores a generative pipeline for structured roof geometry reconstruction from remote sensing imagery (RGB and/or DSM). Using conditional diffusion models, we estimate roof primitives and their geometric relationships, representing them as a graph with raster-based geometry, relational edges, and vectorized outlines.
Keywords
City modeling, Machine Learning, Layout Graphs, Remote Sensing
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Semester Project , Master Thesis
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Published since: 2025-09-29 , Earliest start: 2025-05-04 , Latest end: 2026-04-30
Organization Photogrammetry and Remote Sensing (Prof. Schindler)
Hosts Schindler Konrad
Topics Information, Computing and Communication Sciences , Engineering and Technology
Tree species identification using deep learning
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.
Keywords
Tree species identification, computer vision, CNN
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-09-15 , Earliest start: 2025-01-06 , Latest end: 2026-08-31
Applications limited to ETH Zurich , Department of Environmental Systems Science , Department of Civil, Environmental and Geomatic Engineering , Institute of Geodesy and Photogrammetry
Organization Forest Resources Management
Hosts Schindler Konrad , Beloiu Schwenke Mirela , Hangartner Ariane
Topics Agricultural, Veterinary and Environmental Sciences , Information, Computing and Communication Sciences , Engineering and Technology