In Colombia, national land administration directives and remote sensing frameworks have increasingly prioritized satellite telemetry to democratize spatial technology and formalize rural infrastructure. Within this directive, multidisciplinary initiatives led by Los Andes University have advanced satellite telemetry applications using Sentinel-2 and data cube architectures across diverse domains, including deforestation tracking, agricultural monitoring [1][2], biomass aerosol transport [3], ecosystem intervention [4], and land-use land-cover change from native forests to extensive agriculture [5]. Building directly upon these remote sensing workflows, the present study couples MaxEnt machine-learning roof extraction with solar radiation modeling across high-priority rural municipalities, providing a scalable spatial pipeline for decentralized photovoltaic potential mapping.

Sentinel-2 imagery from the Copernicus program was ingested via Google Earth Engine for each study polygon. Median surface reflectance composites were computed for the period between January 1, 2023, and May 31, 2023, filtered for cloud coverage below 10%. As illustrated in Figure 1, Sentinel-2 spectral bands with 10-metre spatial resolution (B2 at 490 nm, B3 at 560 nm, B4 at 665 nm) as well as bands 8, 8A, 11, and 12 were extracted. Visible spectrum bands (B2 at 490 nm, B3 at 560 nm, B4 at 665 nm) and the Near-Infrared band (B8 at 842 nm) operate at a 10-meter spatial resolution.

Adapted from European Space Agency online archive [6].

Figure 1. Sentinel-2 10 metre spatial resolution.

Figure 3 illustrates the spatial boundaries of the target study polygons across Colombia and Chile, where eleven distinct Sentinel-2 satellite images were processed.

Map prepared by the author using QGIS.

Figure 3.Division of study areas for the application of the rooftop identification algorithm.

Model evaluations were re-run iteratively whenever unclassified pixel ratios exceeded 10% or rooftop spatial distribution deviated significantly from official housing census baselines [16][17]. Official census data served as a structural baseline under the assumption that one residential dwelling corresponds to at least one primary rooftop. However, classified rooftop counts were expected to moderately exceed census figures, as rural homesteads frequently feature detached structures (e.g., separate kitchens, outdoor sanitation facilities, or livestock shelters).

Although iterative re-training increased processing time, it ensured high classification fidelity across heterogeneous rural landscapes and minimized unclassified pixels.

Individual Sentinel-2 scenes cover an area of 110 km × 110 km. Custom scripts executed via Google Earth Engine clipped imagery directly to target study polygons. Where study regions exceeded single scene footprints, target areas were sub-divided. In Colombia, study zones were partitioned as follows:

Bolivar Department (PDET Municipalities): Northern Bolivar (Cordoba, El Carmen de Bolivar, El Guamo, Maria la Baja, San Jacinto, San Juan Nepomuceno, Zambrano); North-South Bolivar (Arenal, Morales, Simití); Central South Bolivar (Santa Rosa del Sur); Southernmost Bolivar (Cantagallo, San Pablo).Cesar Department (PDET Municipalities): Northern Cesar (Pueblo Bello, Valledupar); North-South Cesar (San Diego, La Paz, Manaure); Central-South Cesar (Agustín Codazzi); Southernmost Cesar (Becerril, La Jagua de Ibirico).

Map prepared by the author using QGIS.

Figure 4.Map of Chile showing the division of study areas for the application of the rooftop identification algorithm.

The total area evaluated in Colombia comprised 125,340,904 pixels of 100 m² (12,534 km²) across Bolivar and 107,528,976 pixels of 100 m² (10,752 km²) across Cesar, totaling 23,286 km² (a spatial extent larger than the European nation of Slovenia equivalent to20,273 km²).

In Chile, the study targeted three agricultural and administrative communes: Casablanca, Parral, and Victoria, as illustrated in Figure 4.

A total of 47,580,000 pixels of 100 m² (4758 km²) were evaluated—breakdown by commune: Casablanca (1114 km²), Parral (2035 km²), and Victoria (1609 km²). Median composite scenes (Jan-May 2023, cloud cover < 10%) were processed per commune via Google Earth Engine. Boundary buffering was evaluated but discarded due to scene spatial limit constraints. To prevent spatial autocorrelation from artificially inflating model performance, accuracy metrics were evaluated using spatial block cross-validation (spatial holdout splits) rather than random point selection. Test points were geographically separated from training clusters using spatial buffering to guarantee independence.

High-spatial-resolution imagery <1 m obtained from Google Earth Pro.

Figure 5. Rooftop categories illustrating structural and color variations included in the training dataset.

As shown in Figure 5, high-spatial-resolution imagery (<1 m) obtained from Google Earth Pro covers all structural roofs regardless of color or material composition. Visible color variations (white, gray, red, blue, orange) and structural materials (corrugated zinc, clay tile, plastic, concrete) were incorporated into model feature spaces without requiring individual material-class pre-labeling.

For each study polygon, 300 supervised ground-truth points were established using high-resolution Google Earth Pro imagery: 100 points for “Rooftops”, 100 for “Non-Intervened land”, and 100 for “Intervened land”, as follows considerations:

Non-Intervened: Natural, unbuilt land covers, including primary forests, open water bodies, paramo vegetation, and high-altitude snowpack.10,000 background points randomly sampled across non-built land-cover classes within each study polygon.Intervened: Human-altered landscapes excluding buildings, such as agricultural crops, pastures, paved roads, and unpaved/dirt roads.Unclassified: Pixels remaining unassigned by the MaxEnt classification threshold.

In Colombia, across 8 study polygons, a total of 2400 training points (800 rooftops, 800 non-intervened, 800 intervened) were carefully sampled, each maintaining a minimum concentric radius of 10 meters. In Chile, across 3 study polygons, 900 points were sampled following identical spatial buffering criteria.

Prior field implementation experience in regions like the Choco department demonstrated that persistent tropical cloud cover poses significant constraints for optical satellite analysis. Atmospheric moisture accumulation along the mountain slopes of Colombia’s three Andean ranges frequently obstructs optical sensors.

Consequently, Bolivar and Cesar departments were selected as robust proof-of-concept regions due to consistently low cloud cover during dry seasons. Scaling this framework nationwide will require cloud-masking algorithms or synthetic aperture radar (SAR) integration to mitigate persistent atmospheric interference.

According to long-term climatological observations from NASA’s MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) satellite dataset, high- and mid-level cloud fractions across Colombia in 2022 were heavily concentrated over the Pacific coastal region, as shown in Figure 6.

Analysis and maps retrieved from the NASA Earth data Portal.

Figure 6.Annual average spatial distribution of high-altitude and mid-altitude cloud fractions over Colombia for 2022, derived from NASA MERRA-2 satellite data.

Rooftop raster outputs were integrated directly into the web-based photovoltaic visualization tool. Solar PV generation potential was calculated using the following formulation:

P_PV = G*A*η

where P_PV represents the solar photovoltaic power output (W), G is the global solar irradiance (W/m²), A is the total rooftop panel area (m²), and η is the system conversion efficiency (η ≈ 0.20).

The satellite screen capture of the Maxent process shown in Figure 7 highlights a prominent classification challenge: spectral confusion between high-altitude snowpack and light-colored rooftops due to near-identical visible and shortwave reflectance signatures.

A prominent classification challenge involved spectral confusion between high-altitude snowpack and light-colored rooftops due to near-identical visible and shortwave reflectance signatures. This misclassification was observed in high-elevation areas of Parral (Chile) and Central-Southern Cesar (Colombia).

Satellite screen capture of Maxent process acquired by the author.

Figure 7. Classification error due to high-altitude snowpack interference during rooftop mapping. Model training was conducted in Google Earth Pro, and the Machine Learning model was executed using Copernicus Sentinel-2 satellite imagery (satellite image in MaxEnt captured by the author).

Iterative model tuning confirmed that combining multi-spectral bands with vegetation, water, and built-up indices provides a robust foundation for automated rooftop detection. However, as illustrated in Figure 8, spatial limitations in usable rooftop surface area often arise due to specific building design features, as observed in the Parral Commune, Chile.

Advanced spatial post-processing using commercial GIS platforms (e.g., ArcGIS Spatial Analyst) can enhance technical assessments by incorporating 3D building height models and rooftop pitch analysis. Tools such as “Area Solar Radiation” can calculate micro-level solar insolation by accounting for seasonal solar azimuth, shadows from adjacent structures, roof slope, and aspect orientation.

While rural rooftop structures in Colombia exhibit relatively homogeneous, flat geometries, residential architecture in Chile features complex roof slopes, gables, and dormer windows. These architectural features introduce spatial shading constraints and reduce effective usable panel area. High-resolution spatial analysis in Chilean communes showed that decorative architectural elements reduce contiguous roof plane lengths (e.g., from an overall roof dimension of 11.89 m down to 3.10 m usable space before encountering structural obstructions). and import your prepared text file. You are now ready to style your paper.

High-spatial-resolution imagery (<1 m) obtained from Google Earth Pro.

Figure 8.Spatial limitations in usable rooftop surface area due to building design features in the Parral Commune, Chile.

High-spatial-resolution imagery (<1 m) obtained from Google Earth Pro.

Figure 9.High-spatial-resolution image showing the roof surface of the Municipal Sports Complex in Cordoba (Bolívar, Colombia).

The methodology also facilitates identifying large-format public infrastructure suitable for community solar projects. For example, the roof of the Municipal Sports Complex in Cordoba (Bolivar, Colombia) measures 35 m × 38 m, as shown in Figure 9.

Deploying rooftop PV on large civic structures can accelerate rural electrification, lower public energy overhead, and support community development goals.

When evaluating machine-learning outputs against national housing surveys (DANE in Colombia; INE in Chile), it is critical to distinguish between their underlying spatial constructs. Census baselines measure residential dwellings and households, whereas satellite-based classification identifies physical roof-containing structures (which include agricultural sheds, commercial units, industrial facilities, and multi-dwelling apartment blocks). Discrepancies between total pixel counts and census numbers stem from these structural divergences rather than classification error alone.

Community engagement and local stakeholder alignment remain critical for ensuring long-term technical adoption and social integration of distributed solar energy systems in rural areas.

Under the “Colombia Sostenible” framework, rural development initiatives were established across vulnerable communities. Future phases should connect distributed rooftop PV generation directly to productive agricultural infrastructure (e.g., crop processing, refrigeration, and irrigation) rather than limiting energy usage strictly to basic household lighting.

For example, in livestock management and silvopastoral systems, small-scale solar arrays can power electric fencing and water pumping systems, fostering sustainable agricultural transition.

Academia and public institutions must collaborate to present these spatial tools to local communities, demonstrating how decentralized clean energy reduces socioeconomic vulnerability and energy poverty.

Because the developed MaxEnt workflow effectively discriminates linear infrastructure, future research will focus on automated mapping and characterization of unpaved tertiary road networks. Mapping tertiary access routes will enable precise routing assessments for distribution lines, transformer placement, and grid extension planning in remote regions.

Finally, given the structural baseline utility and high spatial agreement within urban cores demonstrated by machine learning classification across Colombia (DANE) and Chile (INE), national statistical agencies are strongly advised to integrate remote sensing and automated land-cover modeling into census methodologies. While satellite mapping identifies physical structures rather than strictly residential households, deploying satellite-based structural identification offers significant strategic advantages:

Cost Reduction and Operational Efficiency: It optimizes administrative budgets by significantly decreasing logistical overhead, paper-based surveys, and extensive field personnel deployment.Risk Mitigation in Conflict and Remote Zones: In regions characterized by active armed conflict, social instability, or ongoing post-peace-agreement transition, automated spatial mapping replaces physical site inspection, directly safeguarding field enumerators and researchers from physical security threats.Transparency and Bureaucratic Streamlining: By anchoring housing and infrastructure baselines to verifiable, open-access satellite telemetry, public agencies can streamline administrative workflows, minimize bureaucratic friction, and enhance data transparency, thereby mitigating risks of data manipulation or corruption during local census execution.

Consequently, integrating machine-learning workflows into national statistical protocols provides a scalable, secure, and cost-effective paradigm for modernizing population and housing updates in developing and conflict-affected nations.

I would like to express my sincere gratitude to Professors Fernando Jiménez, Guillermo Jiménez, and Rocío Sierra from the Faculty of Engineering at Los Andes University, as well as the Chilean team for their active engagement and valuable contributions, whose unwavering support, trust, and guidance, which made the execution of this research project possible.