Dataset: Census of individual trees of Tucunare Vichada (2020)
This dataset contains information of individual trees in Tucunare, Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to t...
| Autores principales: | , , , , , , |
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| Formato: | Conjunto de datos |
| Lenguaje: | Inglés |
| Publicado: |
2024
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| Acceso en línea: | https://hdl.handle.net/10568/163200 |
| _version_ | 1855533267917209600 |
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| author | Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres |
| author_browse | Arango Mejia, Jacobo Arrechea-Castillo, Darwin Alexis Cardoso Arango, Juan Andres Costa Junior, Ciniro Matiz Rubio, Natalia Pérez Bolaños, Juliana Ruiz-Hurtado, Andres Felipe |
| author_facet | Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres |
| author_sort | Ruiz-Hurtado, Andres Felipe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This dataset contains information of individual trees in Tucunare, Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree
Metodology:Tree information was obtained by leveraging the High-Resolution 1 m Global Canopy Height map generated by Meta's AI model, trained on a large dataset of satellite images and LIDAR data, to predict canopy heights with a mean absolute error of 2.8 meters. The map tiles were downloaded from Amazon Web Services (AWS) S3 Bucket and processed using additional steps: merging and clipping tiles to cover the region of interest of Tucunare, vectorizing, filtering, and georeferencing individual trees to derive attributes like area, perimeter, equivalent diameter, and circularity representing the visual crown of each tree, and finally applying zonal statistics to assign tree height values. The resulting vector layer contains detailed tree metrics, supporting advanced canopy analysis. |
| format | Conjunto de datos |
| id | CGSpace163200 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1632002025-06-03T13:34:56Z Dataset: Census of individual trees of Tucunare Vichada (2020) Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres remote sensing artificial intelligence This dataset contains information of individual trees in Tucunare, Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree Metodology:Tree information was obtained by leveraging the High-Resolution 1 m Global Canopy Height map generated by Meta's AI model, trained on a large dataset of satellite images and LIDAR data, to predict canopy heights with a mean absolute error of 2.8 meters. The map tiles were downloaded from Amazon Web Services (AWS) S3 Bucket and processed using additional steps: merging and clipping tiles to cover the region of interest of Tucunare, vectorizing, filtering, and georeferencing individual trees to derive attributes like area, perimeter, equivalent diameter, and circularity representing the visual crown of each tree, and finally applying zonal statistics to assign tree height values. The resulting vector layer contains detailed tree metrics, supporting advanced canopy analysis. 2024 2024-12-09T07:55:49Z 2024-12-09T07:55:49Z Dataset https://hdl.handle.net/10568/163200 en Open Access Ruiz Hurtado, A.F.; Perez Bolanos, J.; Arrechea Castillo, D.A.; Matiz Rubio, N.; Costa Junior, C.; Arango Mejia, J.; Cardoso Arango, J.A. (2024) Dataset: Census of individual trees of Tucunare Vichada (2020). https://doi.org/10.7910/DVN/RTO8MM |
| spellingShingle | remote sensing artificial intelligence Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title | Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title_full | Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title_fullStr | Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title_full_unstemmed | Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title_short | Dataset: Census of individual trees of Tucunare Vichada (2020) |
| title_sort | dataset census of individual trees of tucunare vichada 2020 |
| topic | remote sensing artificial intelligence |
| url | https://hdl.handle.net/10568/163200 |
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