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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.
7 Research products, page 1 of 1

  • Rural Digital Europe
  • Research data
  • Open Access
  • Dataset
  • NL
  • DataverseNL

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  • Open Access
    Authors: 
    Plakman, Veerle; Rosier, Job Fabian; Van Vliet, Jasper;
    Publisher: DataverseNL
    Country: Netherlands

    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 VIIRS night-light intensity. These features are subsequently used to train and validate a range of RF models to select the most promising model setup. The training datasets consisted of subsampled presence/absence data, oversampled presence/absence data, and multiple land use categories. The best-performing model used an oversampled dataset trained on all 10- and 20- meter resolution spectral bands and the radar backscatter properties of one period. Independent test results show an overall classification accuracy of 99.97% (Kappa: 0.90). For this result, the producer accuracy was 85.86% for solar park objects and of 99.999% for non-solar park objects. The user accuracy was 92.39% for solar park objects and of 99.999% for non-solar park objects. These high classification accuracies indicate that our approach is suitable for transfer learning and is able to detect solar parks in new study areas.

  • Open Access
    Authors: 
    Newig, Jens;
    Publisher: DataverseNL
    Country: Netherlands

    Data for the Hase area cooperation in Lower Saxony case. Contains qualitative and quantitative information on the conditions, processes, and outcomes of a specific instance of collaborative governance involving public, private, and/or community actors.

  • Open Access
    Authors: 
    Berthod, Olivier;
    Publisher: DataverseNL
    Country: Netherlands

    Data for the Foodborne disease outbreak in Germany case. Contains qualitative and quantitative information on the conditions, processes, and outcomes of a specific instance of collaborative governance involving public, private, and/or community actors.

  • Open Access
    Authors: 
    Weber, Edward;
    Country: Netherlands

    Data for the Blackfoot Challenge (Montana, USA) case. Contains qualitative and quantitative information on the conditions, processes, and outcomes of a specific instance of collaborative governance involving public, private, and/or community actors.

  • Open Access
    Authors: 
    De Croon, Guido; De Wagter, Christophe; Seidl, Tobias;
    Country: Netherlands

    This repository contains all data and code necessary to reproduce the experiments and figures in the article: "Enhancing optical flow-based control by learning visual appearance cues for flying robots". It allows to reproduce both the experiments in simulation and the real-world experiments with the Parrot Bebop 2 drone. Please see the README in the repository for a detailed explanation. Please note that the Paparazzi code included in this data set is subject to a GNU left license. See https://github.com/paparazzi/paparazzi/blob/master/LICENSE for more details.

  • Open Access
    Authors: 
    Li, Mengmeng; Koks, Elco; Taubenböck, Hannes; van Vliet, Jasper;
    Publisher: DataverseNL
    Country: Netherlands

    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 important variable for estimating building footprint, while backscatter intensity of Synthetic Aperture Radar (SAR) is the most important variable for estimating building height. A combination of the two is essential to estimate building volume. Our analysis further highlights the heterogeneity of 3D building structure across space. Specifically, buildings in China tend to be taller on average (10.35 m) compared to Europe (7.37 m) and the US (6.69 m). At the same time, the building volume per capita in China is lowest, with 302.3 m3 per capita, while Europe and the US show estimates of 404.6 m3 and 565.4 m3, respectively. The results of this study (3D building structure data for Europe, the US, and China) are publicly available, and can be used for further analysis of urban environment, spatial planning, and land use projections.

  • Open Access
    Authors: 
    Nohrstedt, Daniel; Bodin, Örjan;
    Publisher: DataverseNL
    Country: Netherlands

    Data for the Swedish wildfire responder network case. Contains qualitative and quantitative information on the conditions, processes, and outcomes of a specific instance of collaborative governance involving public, private, and/or community actors.