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Found 6 papers, 3 papers with code
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.
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities.
Ranked #1 on
Panoptic Segmentation
on PASTIS-R
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.
Ranked #2 on
Time Series Classification
on s2-agri
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series.
