DOI: 10.6084/m9.figshare.18982199.v5
title: Connecticut Solar PV Semantic Segmentation Dataset

description: <b>Background Information</b><br>Annotated overhead imagery dataset for the paper "<i>SolarMapper: estimating solar array location, size, and capacity using deep learning and overhead imagery</i>".<br>The paper's Github page: energydatalab/mrs: Models for Remote Sensing (github.com)<br><br>This dataset is a subset of the very high resolution aerial imagery provided through the <i>Connecticut Department of Energy and Environmental Protection shared via the University of Connecticut (CT ECO 2016 Imagery & Elevation (uconn.edu)</i>). The original resolution of the imagery is 3 inch (7.62 cm) and we downsampled all of the imagery to a resolution of 30 cm, which is the same resolution as most high resolution satellite imagery.<br>87 image tiles in total are included in this dataset. We manually annotated all visible solar PV panels with polygons for semantic segmentation purposes. We split the dataset into a training and a validation set at a 2:1 ratio. Details of each subset are shown below:<br><b>Training set</b>: 57 image tiles, 33.12 km<sup>2</sup> of ground area, 608 solar PV arrays, 0.058 km<sup>2</sup> of solar PV panel area.<br><b>Validation set</b>: 30 image tiles, 17.43 km<sup>2</sup> of ground area, 1,003 solar PV arrays, 0.091 km<sup>2</sup> of solar PV panel area.<br><br>Additionally, we compared our sampled imagery with <i>Microsoft's US Building Footprints dataset (microsoft/USBuildingFootprints: Computer generated building footprints for the United States (github.com))</i> to split all image tiles into 3 brackets (high, medium, and low building density) by the number of buildings with in an image tile. More details about how we assigned these image tiles could be found in the paper. Such assignment was only applied to the 30 validation tiles.<br><b>File Description</b><b><br></b><b>ct_images_and_labels.zip</b><b><br></b>This <i>.zip</i> file contains all aerial image (<i>.jpg</i>) files and corresponding semantic segmentation annotation mask (<i>.png</i>) files. All .jpg imagery files are 8-bit RGB images and all <i>.png</i> files are binary arrays where 1 is for solar PV pixels and 0 is for non-solar PV pixels. An aerial imagery file and its corresponding mask file share the same filename but have different filename extensions. E.g., <i>000795_sw.jpg</i> is the aerial imagery file for tile <i>000795_sw</i>, and <i>000795_sw.png</i> is the corresponding mask file.<br><b>tile_assignments.csv</b><b><br></b>This .csv file records if an image tile is in the training or validation set, and if it's a high, medium or low building density tile. There are 3 columns in the <i>.csv</i> file:<b>- tile_name (String)</b>: The tile name ID of a tile. This corresponds to filanmes of <i>.jp</i>g aerial imagery files and <i>.png</i> annotation mask files.<b>- training_split (</b><b>Categorical</b><b>)</b>: Whether the image tile is in the training or the validation set. Values are either 'train' for the training set or "valid" for the validation set.<b>- density_split (Categorical)</b>: Which building density bracket does the image tile belong to. Since the building density based assignment was only applied to the 30 validation tiles, all training tiles have "NA" in this column. For the validation tiles, values are either "high", "med", or "low" corresponding to the 3 building density brackets (high, medium and low).<br><b>ct_labeled_tile_boundaries_gcs.geojson</b><br><b><br></b>This .geojson file records boundary polygons of the tiles in this dataset. Tile name information is stored in the "tile_name" field. Descriptions on other fields could be found at CRCOG Orthoimagery (uconn.edu).<br><b>sample.pptx</b><br>Samples from each of the other 3 files.