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Lozano-Garzon, C., Bravo-Cordoba, G., Castro, H., Gonzalez-Rodriguez, G., Nino, D., Nunez, H., et al. (2022) Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops. IEEE Access, 10, 17542-17555.
https://doi.org/10.1109/access.2022.3148691

has been cited by the following article:

• TITLE: Identification and Solar Potential Estimation Using Machine Learning and Sentinel-2 Satellite Imagery in Rural Regions of Colombia and Chile

  AUTHORS: Ivan Carroll-Janer

  KEYWORDS: Sentinel-2, MaxEnt, Copernicus, Machine Learning, Photovoltaic Tool, Remote Sensing, Rooftop Solar PV, Distributed Generation, Energy Transition, Colombia, Chile

  JOURNAL NAME: Energy and Power Engineering, Vol.18 No.10, October 13, 2026

  ABSTRACT: This paper applies a MaxEnt classification workflow to Sentinel-2 spectral bands and derived indices to map rooftops, non-intervened land, and intervened land across selected rural areas in Colombia and Chile. The study compares detected rooftop counts with housing-census counts and links the classification output to a photovoltaic screening tool. It identifies cloud cover, snow-related spectral confusion, and roof geometry as important constraints on wider application.