Replication Data for: Solar park detection from publicly available satellite imagery
<|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:'Detecting large-scale photovoltaic installations, or solar parks, is important to monitor their amount and allocation and assess their. However, existing databases are not complete, as the number of solar parks increase rapidly. Therefore, satellite imagery might offer a solution. While their spectral signature suggests that solar parks can be identified among other land uses, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm. Subsequently, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2, as well as for the VIIR' . 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" : ["Land cover","Remote Sensing","Solar photovoltaics"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Detecting large-scale photovoltaic installations, or solar parks, is important to monitor their amount and allocation and assess their. However, existing databases are not complete, as the number of solar parks increase rapidly. Therefore, satellite imagery might offer a solution. While their spectral signature suggests that solar parks can be identified among other land uses, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm. Subsequently, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2, as well as for the VIIR' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Detecting large-scale photovoltaic installations, or solar parks, is important to monitor their amount and allocation and assess their. However, existing databases are not complete, as the number of solar parks increase rapidly. Therefore, satellite imagery might offer a solution. While their spectral signature suggests that solar parks can be identified among other land uses, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm. Subsequently, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2, as well as for the VIIR
Land cover; Remote Sensing; Solar photovoltaics