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Found 8 papers, 1 papers with code
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint
The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization.
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network
A low-resolution digital surface model (DSM) features distinctive attributes impacted by noise, sensor limitations and data acquisition conditions, which failed to be replicated using simple interpolation methods like bicubic.
Machine-learned 3D Building Vectorization from Satellite Imagery
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization.
Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
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.
Machine-learned Regularization and Polygonization of Building Segmentation Masks
We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks.
A Generalized Multi-Task Learning Approach to Stereo DSM Filtering in Urban Areas
Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM.
Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN
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 height information within one network.
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
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 of two parallel networks merged at the last stage forming a WNet architecture.
