1 code implementation • 25 Apr 2022 • Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata
The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry.
no code implementations • 20 Jan 2022 • Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds.
no code implementations • 27 Dec 2021 • Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform.
1 code implementation • 14 Dec 2021 • Vivien Sainte Fare Garnot, Loic Landrieu, Nesrine Chehata
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
2 code implementations • CVPR 2020 • Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata
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
no code implementations • 29 Jan 2019 • Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series.