Annotated High-Resolution Aerial Imagery of the Dutch Landscape for Solar Panel Detection and Segmentation

Overview This dataset consists of true-ortho high-resolution aerial images captured in 2023 by the local government of Emmen, Netherlands for the purpose of solar panel segmentation. The images are taken under similar conditions using a small aircraft, providing a significantly higher spatial resolution (7.5 cm per pixel) compared to satellite imagery (30 cm per pixel). This results in sharper and more detailed images suitable for solar panel detection and related spatial analyses. Data Collection and Processing The aerial images cover an area of 346.26 km², with four selected regions comprising diverse building types and vegetation, totaling 18.55 km². The selected areas are divided into 30x30 meter grid cells, resulting in 20,618 squares of 900 m² each. Within these areas, solar panels were manually annotated with polygons, identifying 4,389 unique solar panel objects. Given that the proportion of solar panel surface relative to the total area is small, the dataset includes only 224x224 pixel RGB images from grid cells that either contain solar panels or are in close proximity to a cell with solar panels. This selection avoids a significant class imbalance. The final dataset consists of 5,327 annotated images, of which 1,743 contain solar panels. For our research purposes, the dataset is enriched with elevation and slope data from the (also publicly available) Actueel Hoogtebestand Nederland (AHN) dataset: Elevation Data (AHN4 DEMs and LiDAR-derived Point Cloud Data): AHN4 provides precise elevation measurements with a minimum of 10 measurements per square meter. The digital terrain model (DTM) was generated using a Squared Inverse Distance Weighting (IDW) method. Slope Calculation: The tilt of surfaces was computed based on the AHN-4 dataset using the Planar Method. Dataset Structure The dataset is organized into the following directories: /input -- Stores the raw aerial images - 000000.png - 000001.png /mask -- Pixel-wise ground truth masks indicating the presence of solar panels - 000000.png (corresponds to input 0000000.png) - 000001.png /height -- Elevation data derived from the AHN4 dataset for each pixel - 000000.tiff - 000001.tiff /slope -- Slope values computed using a 3 × 3 sliding window for each pixel - 000000.tiff - 000001.tiff /annotations -- Contains meta information about the annotations - annotations.shp -- the annotations in polygon form - grids.shp -- the grid cells indicating the whole area that we annotated and contains for each cell whether it was included fold_info.pkl -- Python dictionary containing indices for stratified 5-fold cross-validation Code The code for producing baseline deep learning models on this dataset can be found at the following Gitlab repository. How to cite If you want to cite this work, please cite our underlying paper found here.