Evaluating Two Approaches for Mapping Solar Installations to Support Sustainable Land Monitoring: Semantic Segmentation on Orthophotos vs. Multitemporal Sentinel-2 Classification

Adolfo Lozano-Tello (), Andrés Caballero-Mancera, Jorge Luceño and Pedro J. Clemente
Additional contact information
Adolfo Lozano-Tello: Quercus Software Engineering Group, Universidad de Extremadura, 10003 Cáceres, Spain
Andrés Caballero-Mancera: Quercus Software Engineering Group, Universidad de Extremadura, 10003 Cáceres, Spain
Jorge Luceño: Quercus Software Engineering Group, Universidad de Extremadura, 10003 Cáceres, Spain
Pedro J. Clemente: Quercus Software Engineering Group, Universidad de Extremadura, 10003 Cáceres, Spain

Sustainability, 2025, vol. 17, issue 19, 1-30

Abstract: This study evaluates two approaches for detecting solar photovoltaic (PV) installations across agricultural areas, emphasizing their role in supporting sustainable energy monitoring, land management, and planning. Accurate PV mapping is essential for tracking renewable energy deployment, guiding infrastructure development, assessing land-use impacts, and informing policy decisions aimed at reducing carbon emissions and fostering climate resilience. The first approach applies deep learning-based semantic segmentation to high-resolution RGB orthophotos, using the pretrained “Solar PV Segmentation” model, which achieves an F1-score of 95.27% and an IoU of 91.04%, providing highly reliable PV identification. The second approach employs multitemporal pixel-wise spectral classification using Sentinel-2 imagery, where the best-performing neural network achieved a precision of 99.22%, a recall of 96.69%, and an overall accuracy of 98.22%. Both approaches coincided in detecting 86.67% of the identified parcels, with an average surface difference of less than 6.5 hectares per parcel. The Sentinel-2 method leverages its multispectral bands and frequent revisit rate, enabling timely detection of new or evolving installations. The proposed methodology supports the sustainable management of land resources by enabling automated, scalable, and cost-effective monitoring of solar infrastructures using open-access satellite data. This contributes directly to the goals of climate action and sustainable land-use planning and provides a replicable framework for assessing human-induced changes in land cover at regional and national scales.

Keywords: photovoltaic installation detection; remote sensing for sustainable land management; Sentinel-2 multispectral imagery; deep learning for land use planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/19/8628/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/19/8628/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:19:p:8628-:d:1758246

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().