Geospatial AI in Rooftop Solar Mapping

India’s solar targets depend increasingly on distributed generation. Dense urban areas, Hyderabad, Pune, Ahmedabad, Delhi NCR, and others, hold vast untapped rooftop potential but suffer from fragmented data, inconsistent site surveys, and long sales cycles. Geospatial AI changes that: using high-resolution imagery, machine learning, and spatial analytics, cities and DISCOMs can automate rooftop identification, estimate PV capacity, and prioritize rollouts with near-real-time accuracy.

The problem: slow, manual, inconsistent

• Fragmented basemaps: Building outlines, heights, and roof shapes are incomplete or outdated.

• Shading uncertainty: Trees, tanks, HVAC units, and adjacent high-rises reduce usable area and complicate feasibility.

• Sales friction: Lead qualification and engineering estimates take weeks, increasing churn.

• Planning blind spots: Utilities lack ward-level potential maps to stage investments and grid upgrades.

“Would you adopt such an automated rooftop assessment now, or wait for standardized citywide data models?”

Figure 1: Commercial and institutional roofs often yield the highest per-site capacity; apartments need shared-rooftop or carport strategies.

The solution: an automated geospatial AI pipeline

1) Data ingestion

• Imagery: Sub-meter satellite tiles or drone orthomosaics; optional LiDAR for height.

• Context layers: Building footprints, parcel boundaries, tree cover, road networks, power lines, and transformer zones.

• Irradiance baselines: Long-term solar resource datasets for Indian latitudes.

2) Roof detection & segmentation

• Footprints: CNNs or transformers segment buildings from imagery; vectorized to polygons.

• Roof planes: Instance segmentation separates flat vs pitched surfaces; RANSAC planes (from LiDAR or stereo) estimate slope/aspect.

• Obstruction mapping: Object detection flags tanks, HVAC, parapets; buffers define exclusion zones.

3) Shading & usable area

• Sun-path modeling: Hourly sun vectors, roof orientation, and horizon masks (nearby buildings/trees) yield cell-level shading factors.

• Usable area: Roof polygon minus obstructions and safety offsets; array layout heuristics for module spacing and tilt.

4) System sizing & production

• Capacity (kW): Usable m² × module density × DC/AC ratio.

• Energy (kWh): TMY-based irradiance × tilt/azimuth correction × shading × system losses.

• Financials: Levelized cost, payback, IRR with standard O&M and tariff assumptions.

5) Prioritization & rollout

• Ward-level ranking: Potential (MW/ward), payback, and feeder capacity constraints.

• Equity lenses: Public buildings, schools, hospitals, and low-income housing first, where benefits are systemic.

• Operationalization: Exportable site packs (KML/GeoJSON/CAD) for on-ground validation and permitting.

Figure 2: Fixed-tilt yield typically peaks around the local latitude band; small tilt deviations rarely break viability, which helps standardize mounting designs.

Technical building blocks (India-ready)

A. Models & methods

• Segmentation: UNet/DeepLab variants for roof masks; Mask R-CNN or DETR for obstructions.

• 3D inference: If LiDAR absent, infer relative height from multi-view or shadow geometry for basic plane estimation.

• Shading engine: Solar geometry + vectorized surroundings; hourly shading matrices drive performance estimates.

B. Data quality controls

• Active learning loop: Human QA of uncertain segments (odd roof materials, dense shadows) continuously improves the model.

• Confidence bands: Publish P50/P90 energy outputs so financiers can price risk.

• Temporal refresh: Re-map annually (or after storms/vegetation growth) to maintain accuracy.

C. System integration

• Utility stack: Feed ward-level potential into feeder planning, voltage rise checks, and transformer upgrades.

• City portals: Pre-approved layouts for public buildings; simplified permits with auto-validated roof plans.

• Installer CRM: Auto-generated proposals with site snapshots, expected kWh, and payback to cut sales cycles.

Example: Hyderabad ward pilot (hypothetical)

• Scope: 25 wards, ~120,000 buildings; 15 cm drone orthos for four sample wards; sub-meter satellite elsewhere.

• Findings: Commercial/industrial roofs contributed ~62% of total potential despite being <15% of building count. Institutional roofs (schools/hospitals) offered high daytime self-consumption, improving ROI. Apartment complexes showed latent potential via carports and community solar.

• Impact: A staged 40 MW program with feeder-aware phasing, predictable permitting, and bundled procurement.

Benefits & ROI (why this matters)

• 50–80% faster lead qualification vs manual surveys.

• Higher conversion with instant, site-specific proposals.

• Better grid stability through feeder-level visibility and phasing.

• Lower soft costs (engineering, permitting) via standardized outputs.

• Policy alignment with transparent ward-wise targets and public-building first plans.

Implementation checklist

• Data: Acquire recent high-res imagery; ingest footprints and utility layers.

• Models: Fine-tune roof/obstruction models on local roof types (RCC, asbestos, metal decks).

• Validation: Field-audit a stratified sample; calibrate shading and loss assumptions.

• Integrations: Connect outputs to utility planning tools and installer CRMs.

• Governance: Standardize metadata, versioning, and privacy for citywide rollouts.

Conclusion

Rooftop solar will scale when discovery, design, and decision-making are automated. Geospatial AI delivers that automation, turning imagery into deployable capacity, at city scale, with utility-grade confidence. The next step is simple: pick two wards, run a pilot, tighten the models, and expand. Cities that operationalize this stack first will unlock faster rooftop uptake, cleaner grids, and lower bills for consumers.

Note: All figures are illustrative