Replication Data for: Continental-scale mapping and analysis of 3D building structure
<|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:'Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most imp' . 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" : ["3D building","Building height","Building volume","Urban density","Urban form"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most imp' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most imp
3D building; Building height; Building volume; Urban density; Urban form