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Found 5 papers, 0 papers with code
Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors
The performance of these models heavily relies on high-quality ground truth segment labels.
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
In contrast, we propose to represent surface topological structure by a contour tree skeleton, which is a polytree capturing the evolution of surface contours at different elevation levels.
Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach
In contrast, we recently proposed a novel machine learning model called geographical hidden Markov tree that integrates spectral features of pixels and topographic constraints from Digital Elevation Model (DEM) data (i. e., water flow directions) in a holistic manner.
Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint
These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations.
Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)
Anisotropic spatial dependency is incorporated in the hidden class layer with a reverse tree structure.
