Summary of Pureforest: a Large-scale Aerial Lidar and Aerial Imagery Dataset For Tree Species Classification in Monospecific Forests, by Charles Gaydon et al.
PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests
by Charles Gaydon, Floryne Roche
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents PureForest, a large-scale, open, multimodal dataset designed for tree species classification from Aerial Lidar Scanning (ALS) point clouds and Very High Resolution (VHR) aerial images. The dataset features 18 tree species grouped into 13 semantic classes, spanning 339 km2 across 449 distinct monospecific forests. This makes it the largest and most comprehensive Lidar dataset for identifying tree species to date. The authors aim to provide a challenging benchmark dataset to support the development of deep learning approaches for tree species identification from Lidar and/or aerial imagery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big, helpful database called PureForest that can be used to identify different types of trees using special machines that scan the ground from the air. Right now, there aren’t many datasets like this one that have lots of different kinds of trees in them. The dataset has 18 types of trees and covers a really big area. By making it available to everyone, the authors hope to help people develop new ways to use computers to identify tree species. |
Keywords
» Artificial intelligence » Classification » Deep learning
