Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas

Frederick Kistner, Sina Keller

2025

Abstract

Integrating renewable energy systems into urban areas is crucial for sustainable development. This study assesses the potential for installing photovoltaic (PV) systems in parking areas, focusing on a case study region in Hesse, Germany. A machine learning approach is developed to classify parking lots larger than 900 m2 into suitable and unsuitable categories. The input data includes OpenStreetMap (OSM), the Authoritative Topographic-Cartographic Information System (ATKIS), and high-resolution geospatial datasets. A reference dataset for the two classification categories is created. Multiple input features are generated, and their significance for the classification task is evaluated. Additionally, several shallow machine learning models are implemented and assessed. The XGBoost model demonstrates the highest accuracy at 99 % and is used to classify 10,894 parking areas throughout Hesse. Key suitability features include the Normalized Difference Vegetation Index (NDVI), surface sealing ratios, and vegetation height. The results indicate that approximately 21.8 km2 of the parking area is suitable for PV installations, requiring minimal ecological intervention. The methodological approach is scalable for application in other regions, and validation in Frankfurt am Main confirms a strong correlation with solar radiation levels. This study provides a data-driven framework for optimizing urban energy systems and supporting sustainability initiatives.

Paper Citation

in Harvard Style

Kistner F. and Keller S. (2025). Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-741-2, SciTePress, pages 244-252. DOI: 10.5220/0013476300003935

in Bibtex Style

@conference{gistam25,
author={Frederick Kistner and Sina Keller},
title={Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2025},
pages={244-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013476300003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas
SN - 978-989-758-741-2
AU - Kistner F.
AU - Keller S.
PY - 2025
SP - 244
EP - 252
DO - 10.5220/0013476300003935
PB - SciTePress