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Found 3 papers, 2 papers with code
Towards general deep-learning-based tree instance segmentation models
This emphasizes the need for forest point clouds with diverse data characteristics for model development.
Learning to Predict Structural Vibrations
To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates.
TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Ground-Based LiDAR Forest Point Clouds
We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
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