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Found 4 papers, 4 papers with code
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages.
ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields
To make full use of the point cloud and the underlying images, we introduce ImpliCity, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos.
ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images
Modern optical satellite sensors enable high-resolution stereo reconstruction from space.
ResDepth: Learned Residual Stereo Reconstruction
We propose an embarrassingly simple but very effective scheme for high-quality dense stereo reconstruction: (i) generate an approximate reconstruction with your favourite stereo matcher; (ii) rewarp the input images with that approximate model; (iii) with the initial reconstruction and the warped images as input, train a deep network to enhance the reconstruction by regressing a residual correction; and (iv) if desired, iterate the refinement with the new, improved reconstruction.
