Authors: Lei Li, Tianfang Zhang, Zhongyu Jiang, Cheng-Yen Yang, Jenq-Neng Hwang, Stefan Oehmcke, Dimitri Pierre Johannes Gominski, Fabian Gieseke, Christian Igel

Published on: March 04, 2024

Impact Score: 8.6

Arxiv code: Arxiv:2403.01932

Summary

• What is new: The fusion of 3D LiDAR measurements with 2D imagery for tree counting.
• Why this is important: Difficulty in counting individual trees in dense areas using traditional 2D remote sensing imagery.
• What the research proposes: A deep learning approach that combines 3D LiDAR data with 2D imagery for improved tree counting accuracy.
• Results: FuseCountNet outperforms existing algorithms in tree counting accuracy.

Technical Details

Technological frameworks used: Deep Learning

Models used: FuseCountNet

Data used: 3D airborne LiDAR data and 2D imagery from the NeonTreeCount data set

Potential Impact

Sustainable forest management, Climate change mitigation sectors, Carbon credit market

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