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Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

  • Published:2022-08 Issue: Volume:112 Page:102863
  • ISSN:1569-8432
  • Container-title:International Journal of Applied Earth Observation and Geoinformation
  • language:en
  • Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Kalinicheva Ekaterina,Landrieu Loic,Mallet Clément,Chehata Nesrine

Funder

Centre National d’Etudes Spatiales

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds;Sensors;2024-03-28

2. Comparison of 2D and 3D vegetation species mapping in three natural scenarios using UAV-LiDAR point clouds and improved deep learning methods;International Journal of Applied Earth Observation and Geoinformation;2023-12

3. Semantic segmentation of remote sensing image based on Contextual U-Net;Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023);2023-08-23

4. Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans;2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2022-06
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