Summary of Oam-tcd: a Globally Diverse Dataset Of High-resolution Tree Cover Maps, by Josh Veitch-michaelis et al.
OAM-TCD: A globally diverse dataset of high-resolution tree cover maps
by Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer, Eben N. Broadbent, David Dao, Ce Zhang, Angelica Almeyda Zambrano, Simeon Max
First submitted to arxiv on: 16 Jul 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 The novel open-access dataset for individual tree crown delineation (TCD) in high-resolution aerial imagery, OAM-TCD, comprises 5072 images with associated human-labeled instance masks for over 280k individual and 56k groups of trees. The dataset is sourced from OpenAerialMap (OAM), allowing for a better capture of the diversity and morphology of trees in different terrestrial biomes and environments. Trained reference instance and semantic segmentation models using OAM-TCD compare favorably to existing state-of-the-art models, with assessments performed through k-fold cross-validation and comparison with existing datasets. The dataset and models are publicly released under permissive open-source licenses: Creative Commons (majority CC BY 4.0), and Apache 2.0 respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new dataset for identifying individual trees in aerial images. This is important because it helps us understand how forests are doing and what we can do to help them. The dataset has lots of pictures from around the world, showing different kinds of trees in different places. It’s like a big photo album! The researchers used this data to train computers to recognize individual trees, and their results are really good. |
Keywords
* Artificial intelligence * Semantic segmentation
