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...
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| Format: | Informe técnico |
| Language: | Inglés |
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2020
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| Online Access: | https://hdl.handle.net/10568/123071 |
| _version_ | 1855538338872688640 |
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| author | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_browse | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_facet | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_sort | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Informe técnico |
| id | CGSpace123071 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| record_format | dspace |
| spelling | CGSpace1230712023-03-14T12:22:36Z CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. CGIAR Research Program on Climate Change, Agriculture and Food Security crops technology training agroforestry development rural development learning land land cover satellite imagery systems agrifood systems machine learning detection imagery satellite 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. 2020-12-31 2022-10-06T14:19:50Z 2022-10-06T14:19:50Z Report https://hdl.handle.net/10568/123071 en Open Access application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security. 2020. CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. Reported in Climate Change, Agriculture and Food Security Annual Report 2020. Innovations. |
| spellingShingle | crops technology training agroforestry development rural development learning land land cover satellite imagery systems agrifood systems machine learning detection imagery satellite CGIAR Research Program on Climate Change, Agriculture and Food Security CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title | CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title_full | CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title_fullStr | CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title_full_unstemmed | CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title_short | CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. |
| title_sort | ciat developed machine learning technology identifies agroforestry crops and other land cover types using publicly available free satellite imagery |
| topic | crops technology training agroforestry development rural development learning land land cover satellite imagery systems agrifood systems machine learning detection imagery satellite |
| url | https://hdl.handle.net/10568/123071 |
| work_keys_str_mv | AT cgiarresearchprogramonclimatechangeagricultureandfoodsecurity ciatdevelopedmachinelearningtechnologyidentifiesagroforestrycropsandotherlandcovertypesusingpubliclyavailablefreesatelliteimagery |