CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery.
The underlying machine learning technology has been tested, developed and proven. A successful pilot land cover detection was completed in Honduras. There is ongoing work on making the system operational, optimizing the system training process and potentially expanding the range of land cover types the system is capable of detecting.
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| Main Author: | |
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| Format: | Report biblioteca |
| Language: | English |
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2020-12-31
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| Subjects: | crops, technology, training, agroforestry, development, rural development, learning, land, land cover, satellite imagery, systems, agrifood systems, machine learning, detection, imagery, satellite, |
| Online Access: | https://hdl.handle.net/10568/123071 |
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| Summary: | The underlying machine learning technology has been tested, developed and proven. A successful pilot land cover detection was completed in Honduras. There is ongoing work on making the system operational, optimizing the system training process and potentially expanding the range of land cover types the system is capable of detecting. |
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