Summary of Unified Deep Learning Model For Global Prediction Of Aboveground Biomass, Canopy Height and Cover From High-resolution, Multi-sensor Satellite Imagery, by Manuel Weber et al.
Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height and Cover from High-Resolution, Multi-Sensor Satellite Imagery
by Manuel Weber, Carly Beneke, Clyde Wheeler
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A new methodology is proposed for estimating carbon stock in forests using multi-sensor, multi-spectral imagery and deep learning-based models. The approach predicts above ground biomass density (AGBD), canopy height (CH), canopy cover (CC), and provides uncertainty estimates for these variables. The model is trained on millions of GEDI-L2/L4 measurements and validated globally for 2023 and annually from 2016 to 2023 over selected areas. Results show a mean absolute error of 26.1 Mg/ha (3.7 m, 9.9 %) and root mean squared error of 50.6 Mg/ha (5.4 m, 15.8 %) on a globally sampled test dataset, outperforming previous results. The model is also tested against independently collected ground measurements, showing high correlation across varying conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forests are important for the environment and scientists need to know how much carbon they store. Right now, it’s hard to measure this accurately because we don’t have enough data from the ground. A new way of using satellite images and artificial intelligence is being developed to solve this problem. This method can estimate three things: how dense the trees are, how tall they are, and what percentage of the forest is covered by trees. The model was tested on a large dataset and did better than previous methods. It’s also good at working with data from different places and conditions. |
Keywords
» Artificial intelligence » Deep learning
