6 citations · 6 across the 4 of their papers we have counts for
6 papers · 1 filter
Machine-learned 3D Building Vectorization from Satellite Imagery
Yi Wang, Stefano Zorzi, Ksenia Bittner
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and pan…
Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer
In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due…
Machine-learned Regularization and Polygonization of Building Segmentation Masks
Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer
We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building seg…
A Generalized Multi-Task Learning Approach to Stereo DSM Filtering in Urban Areas
Lukas Liebel, Ksenia Bittner, Marco Körner
City models and height maps of urban areas serve as a valuable data source for numerous applications, such as disaster management or city planning. While this information is not gl…
Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN
Ksenia Bittner, Marco Körner, Peter Reinartz
We present the workflow of a DSM refinement methodology using a Hybrid-cGAN where the generative part consists of two encoders and a common decoder which blends the spectral and he…
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
Ksenia Bittner, Marco Körner, Peter Reinartz
We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists…