Current Projects

mav4bimThis project investigates the usability of using image sequences from Micro Aerial Vehicles (MAVs) for generating complete and high resolutional 3D building information models (BIM). Beside modelling the building exterior in a global reference frame, the interior should be reconstructed from independent indoor flights as well. An automatic alignment of the reconstructed indoor and outdoor building models offer the generation of LOD-4 building models.

cityscapeModeling the traffic infrastructure based on aerial and ground images becomes ever more important, especially since autonomous driving systems seem to be a part of the near future. This project aims to develop algorithms to process images captured by dash cams mounted on top of  a car in order to detect traffic relevant objects, such as traffic participants and all infrastructure elements. In this context we also use aerial images to analyze group behavior and predict traffic actions.

lulc overviewIn the field of Computer Vision, tasks similar to land use and land cover classification, e.g., image recognition, scene classification and semantic segmentation are very active fields of research. Using Sentinel-2 images and Volunteered Geographic Data (VGI)  this project aims to re-train state-of-the-art image recognition CNNs for the LULC classification task.

super resIn optical remote sensing, spatial resolution of images is crucial for applications using image data. Post-processing operations e.g., segmentation, classification or object extraction in general can benefit from detailed and distinguishable structures obtained by a single-image super resolution pre-processing step. This research focuses on single-image super resolution techniques for multispectral image data.

Remote Sensing Technology
Prof. Richard Bamler

Technische Universität München
Arcisstr. 21
D-80333 München

 

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