The DeepSolaris/ DeepGeoStat dataset was developed in two projects with the same names, during the period 2017 - 2022. The dataset contains 20m x 20m cutouts from an aerial image of the Netherlands. Multiple annotators labeled each cutout for the presence or absence of solar panels. The dataset can be used to train machine or deep learning models on a solar panel classification task. The dataset contains cutouts from the 2018 high-resolution (DOP 10) aerial image of the Netherlands. Each cutout is 200x200 pixels which translates to a 20m x 20m grid. Cutouts were sampled from two nearby regions in the Netherlands: 1. The region around the city of Heerlen 2. The region around the city of Valkenburg Between both regions, a 100m strip was left free in which no cutouts were sampled. Both image subsets are therefore independent of each other. In total 26 annotators, annotated 63258 unique pictures with 200795 annotations; 34523 positive annotations indicating that the annotator saw a solar panel in the image, and 166272 negatives indicating that the annotator didn't see a solar panel in the image. More information can be found in the README.md in the archive. Python source code for training models on this dataset can be found here: https://gitlab.com/CBDS/deepgeostat-wp-5. PyTorch weights for a trained model can be found here: https://zenodo.org/record/7547703#.Y8l0phPMLap. This research was conducted under: the ESS action 'Merging Geostatistics and Geospatial Information in Member States' (grant agreement no.: 08143.2017.001-2017.408), under the ESS topic B5674-2020-GEOS (project 101033951 2020-NL-GEOS-DEEP-GEO-STAT), an investment of [Statistics Netherlands](https://www.cbs.nl) for the development of Deep Learning models, practices, and methodology. The researchers want to furthermore thank everyone involved in helping to create and annotate this dataset.