3 citations · 5 across the 3 of their papers we have counts for
4 papers
Appearance Based Deep Domain Adaptation for the Classification of Aerial Images
Dennis Wittich, Franz Rottensteiner
This paper addresses domain adaptation for the pixel-wise classification of remotely sensed data using deep neural networks (DNN) as a strategy to reduce the requirements of DNN wi…
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
Chun Yang, Franz Rottensteiner, Christian Heipke
Land use as contained in geospatial databases constitutes an essential input for different applica-tions such as urban management, regional planning and environmental monitoring. I…
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Michael Kölle, Dominik Laupheimer, Stefan Schmohl +4
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural…
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN
Max Coenen, Franz Rottensteiner
The retrieval of the 3D pose and shape of objects from images is an ill-posed problem. A common way to object reconstruction is to match entities such as keypoints, edges, or conto…