Summary of Individual Mapping Of Large Polymorphic Shrubs in High Mountains Using Satellite Images and Deep Learning, by Rohaifa Khaldi et al.
Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning
by Rohaifa Khaldi, Siham Tabik, Sergio Puertas-Ruiz, Julio Peñas de Giles, José Antonio Hódar Correa, Regino Zamora, Domingo Alcaraz Segura
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: This research paper presents a novel approach to monitoring long-living large shrubs, such as junipers, in high-mountain ecosystems. The authors release a large dataset of individual shrub delineations on satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve. To optimize performance, they introduce a dual data construction approach using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. The authors also develop a soft version of the Intersection over Union metric to account for the polymorphic nature of junipers. The model achieves an F1-score in shrub delineation of 87.87% on PI data and 76.86% on FW data, with promising results for canopy cover and density of shrubs per size class. This study demonstrates the potential of deep learning applied to freely available high-resolution satellite imagery for detecting medium to large shrubs of high ecological value at the regional scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Scientists are trying to figure out how to better monitor big shrubs in mountainous areas that help keep ecosystems healthy. They’re using a special kind of computer model that can look at pictures taken from space and identify where these big shrubs are. To make sure the model is accurate, they used two different types of data: one was based on photos taken by humans, and the other was based on measurements made in person. The model did pretty well, especially when it came to detecting bigger shrubs at lower elevations and smaller ones at higher elevations. This research shows that using space-based images can be a useful way to track these important plants and understand how they’re affected by changes in the environment. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Instance segmentation
