FireCCI_Africa_2019_S2: reference fire perimeters obtained from Sentinel-2 imagery over Africa continental for the year 2019
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The FireCCI_Africa_2019_S2 reference dataset was derived from S2 images over a set of 50 tiles (sampling units) sampled following a design customized for the Sentinel tiling grid system and to represent major fire regimes of the African continent in the different ecoregions. Over each tile S2 time series were defined based on a set of conditions for minimizing cloud cover and to guarantee series length and a minimum time lag between image pairs. S2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters of depicting areas that burned between the two dates that were combined in a synthetic burned area reference dataset. This dataset represent for each unit burned and unburned polygons and masked areas.  A detailed description of the dataset can be found in Stroppiana et al.(2022) ('Sampling design an' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Accuracy assessment","Biomes","Burned area","Fire disturbance","Probalility sampling","Remote sensing"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The FireCCI_Africa_2019_S2 reference dataset was derived from S2 images over a set of 50 tiles (sampling units) sampled following a design customized for the Sentinel tiling grid system and to represent major fire regimes of the African continent in the different ecoregions. Over each tile S2 time series were defined based on a set of conditions for minimizing cloud cover and to guarantee series length and a minimum time lag between image pairs. S2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters of depicting areas that burned between the two dates that were combined in a synthetic burned area reference dataset. This dataset represent for each unit burned and unburned polygons and masked areas.  A detailed description of the dataset can be found in Stroppiana et al.(2022) ('Sampling design an' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The FireCCI_Africa_2019_S2 reference dataset was derived from S2 images over a set of 50 tiles (sampling units) sampled following a design customized for the Sentinel tiling grid system and to represent major fire regimes of the African continent in the different ecoregions. Over each tile S2 time series were defined based on a set of conditions for minimizing cloud cover and to guarantee series length and a minimum time lag between image pairs. S2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters of depicting areas that burned between the two dates that were combined in a synthetic burned area reference dataset. This dataset represent for each unit burned and unburned polygons and masked areas.  A detailed description of the dataset can be found in Stroppiana et al.(2022) ('Sampling design an
Accuracy assessment; Biomes; Burned area; Fire disturbance; Probalility sampling; Remote sensing