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Found 6 papers, 2 papers with code
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
This approach is the first to use geographical metadata for UDA in semantic segmentation.
Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry.
Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds.
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform.
ICDAR 2021 Competition on Historical Map Segmentation
Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy.
Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task.
