Skip to content

EarthPV

Free, Open and Global Mapping of Photovoltaic Systems Above 10 kWp - Including Capacity, Growth and Orientation

Active development

EarthPV is still a research prototype. It is actively experimenting with new solar detection methods, and its detectors, calibration and headline numbers are still being tested and revised rather than settled.

EarthPV fine-tunes the open TerraMind geospatial foundation model, developed by IBM and ESA and accessed through TerraTorch, using Sentinel-2 imagery. Sentinel-2 provides free, global coverage with imagery refreshed every five days. Each model detection is then presented to OpenStreetMap mappers for verification, and the verified results are fed back into subsequent rounds of training. The model, code, training labels, and capacity estimates are all openly available, and every input is derived from globally accessible datasets. As a result, the approach does not depend on imagery, proprietary licences, or data sources that are restricted to any single country.

Pakistan is the first pilot, not the final destination. It is where EarthPV was initially built and developed.

The EarthPV evidence atlas: Pakistan's rooftop solar capacity, best estimate 24,330 MWp (90 percent range 20,822 to 33,582) -- a night-lights style map of estimated capacity per 0.1 degree cell concentrated in the Punjab corridor and the Karachi industrial belt.

Open the interactive version

How it works: two detectors, one atlas

At Sentinel-2's 10 m resolution a large array has a shape you can trace and a small one does not, so EarthPV runs two instruments and combines them.

  • Segmentation outlines individual arrays above roughly 400 m². These are the mapping leads, and the only instrument for ground-mounted solar at any size.
  • roofclf answers a smaller question for everything below that floor: does this building carry PV? A 100 m² array is a handful of mixed pixels, too few to outline but often enough to classify.

Both are calibrated against small areas where every installation has been hand-mapped, then combined into the evidence atlas, de-duplicated against OpenStreetMap and each other.

The evidence atlas workflow: Sentinel-2 imagery, OpenStreetMap solar mapping and VIDA building footprints feed two detectors, TerraMind segmentation for arrays of 400 square metres and above plus all ground-mount, and the per-building roofclf classifier cross-checked with SPPI. Both are calibrated against 30 hand-mapped ground-truth quadrats, then combined one best instrument per component with overlaps removed and each cell floored at hand-mapped OSM plus roofclf-and-SPPI agreement, producing the published evidence atlas: Best estimate 24,330 MWp with a 90 percent range of 20,822 to 33,582. The evidence atlas workflow: Sentinel-2 imagery, OpenStreetMap solar mapping and VIDA building footprints feed two detectors, TerraMind segmentation for arrays of 400 square metres and above plus all ground-mount, and the per-building roofclf classifier cross-checked with SPPI. Both are calibrated against 30 hand-mapped ground-truth quadrats, then combined one best instrument per component with overlaps removed and each cell floored at hand-mapped OSM plus roofclf-and-SPPI agreement, producing the published evidence atlas: Best estimate 24,330 MWp with a 90 percent range of 20,822 to 33,582.

Full detail, including the optional glint and growth instruments and everything that was tried and rejected: How it works.

Why free imagery, when sharper imagery exists

Sentinel-2 is free, global and coarse. Esri, Bing and Mapbox resolve individual panels but only allow a person to trace from them inside the OpenStreetMap editor.

So EarthPV only ever reads Sentinel-2, people only ever read the high-resolution layers, and the installations they map become ordinary, openly licensed OpenStreetMap features: legitimate training data for the next model.

The mapping flywheel: OpenStreetMap labels train a TerraMind model on Sentinel-2 imagery, the model publishes ranked candidates as mapping leads, local mappers verify each lead against high-resolution imagery in the OpenStreetMap editor, and the verified installations become the next round of training labels. The mapping flywheel: OpenStreetMap labels train a TerraMind model on Sentinel-2 imagery, the model publishes ranked candidates as mapping leads, local mappers verify each lead against high-resolution imagery in the OpenStreetMap editor, and the verified installations become the next round of training labels.

The consequence is that the cost of the next update is close to zero, and anyone can reproduce, check or improve the result.

How small an installation does it find?

Measured on 30 exhaustively hand-mapped Pakistani calibration areas (123,898 buildings), at the same operating point the published atlas uses, binned by how much panel actually sits on the roof:

Panel area on the roof Roughly Found
Above 100 m² above 20 kWp 92% rising to over 99%
50 to 100 m² 10 to 20 kWp 83%
20 to 50 m² 4 to 9 kWp 49%
Below 20 m² below 4 kWp 22 to 31%

What did not work

Most of what was tried here failed, and the negative results are documented because they map where the 10 m resolution limit actually is: band stacking, Sentinel-1 corner reflection, two routes from glint to density, roof-axis orientation priors, three super-resolution variants, spectral unmixing, and two retrains that won in-sample and lost on held-out data. Every one has runnable code in scripts/.

The register with a verdict and the measurement behind each: Experiments. What is still undecided: Open questions.

Add your country: the atlas is meant to be collective

The goal is a global PV evidence atlas assembled from many countries, each run and verified by people who know the ground. Nothing in this pipeline is Pakistan-specific: every input is a global dataset, so the intended shape of the project is a fork per country and this repository as the place their results come back together. Full runbook, including the agent prompt and the review checklist: Contribute your country atlas back.

How to contribute

Map. The most valuable contribution is verified installations in OpenStreetMap. Load the mapping leads into MapRoulette or JOSM, check each against the high-resolution layers, and map what is real. Tag conventionally (generator:source=solar, or power=plant with plant:source=solar) so the next label pull finds it.

Map a quadrat. Exhaustively mapping every installation inside a drawn boundary is worth far more per hour than scattered mapping, because it measures what the model misses rather than only confirming what it finds. 31 quadrats exist so far; the protocol is in Quadrat mapping protocol.

The highest-value next quadrat is a sparse rural one. A quadrat only widens the calibrated domain if its own average building density falls below the current floor, and a boundary traced around a village never does, because it is the farmland between settlements that pulls the average down. Sizing a box to include that open land on purpose is what took the calibrated domain from 163 cells to 2,957 (most recently Nasirabad Rural, 2026-08-13, own density 48.5 bldg/km2).

Review a calibration sample. earthpv calibrate-sample emits a stratified sample of unmapped candidates for human verdicts. Twenty verdicts in the 100 to 500 m2 bin would collapse the widest remaining term in the calibration table. Several random-cell validation batches are also generated and waiting for review, which measures precision against an unbiased population rather than the curated quadrats: see roofclf random-cell validation.

Run it somewhere new. Running on a new region needs nothing pre-downloaded. Target countries for the programme are Mexico, Japan, Korea, Indonesia, India, Brazil, South Africa and Nigeria.

File what you find. Issues and pull requests at open-energy-transition/earthpv.

TraceTheSun

TraceTheSun is an emerging community bringing together the most prominent open-source projects in PV detection and the most skilled PV mappers in OpenStreetMap, to address tagging and mapping solar worldwide in an open, verifiable and cost-effective way.

Currently forming, it includes:

The Centre for Water Informatics and Technology (WIT), LUMS

Centre for Water Informatics and Technology (WIT), LUMS

The Pakistani side of EarthPV is a collaboration with the Centre for Water Informatics and Technology (WIT) at the Lahore University of Management Sciences. TraceTheSun was conceived by Muhammad Awais and Tobias, and a team of WIT students has worked alongside Open Energy Transition since the pilot began, taking EarthPV from a trained model to a working rooftop solar mapping pipeline. Their contribution runs across the entire workflow: they trace and verify Pakistani solar installations in OpenStreetMap against high-resolution imagery, contribute the local context that satellite data alone cannot capture, and build the exhaustively mapped ground-truth quadrats against which every recall figure on this site is measured.

The WIT student contributors are:

This work is the reason the Pakistani model performs as well as it does. Adding in-domain training chips drawn from the mapping loop raised detection recall on Punjabi rooftops from 0.18 to 0.55 for large arrays, and the calibration quadrats the students map remain the only means of checking whether the model's own recall estimates are too optimistic.

For WIT, EarthPV is both a research dataset and a shared design exercise. Co-developing the pipeline has given the students involved a practical introduction to open geospatial machine learning, and the national photovoltaic database it produces already supports the centre's own research. The longer-term goal is to connect this dataset to energy and power-system models and to integrated-assessment scenarios, so that an open and independently verifiable solar capacity map can feed directly into energy planning instead of remaining a standalone map.

Licence

Code is MIT. Imagery from Copernicus Sentinel-2; building footprints from VIDA Open Buildings and Overture Maps; labels from OpenStreetMap contributors under ODbL; administrative boundaries from geoBoundaries under CC-BY.

Published data outputs (the evidence atlas, capacity parquets, raw detections and any other derived dataset offered for download, e.g. under "Download the underlying data" on the atlas page or as a GitHub Release asset) are derivative databases of OpenStreetMap's ODbL-licensed solar labels and, via VIDA Open Buildings, of Microsoft/Google building footprints. Under ODbL's share-alike clause, these data releases are themselves licensed under the Open Database License (ODbL) v1.0, with attribution to © OpenStreetMap contributors required on any use, alongside VIDA Open Buildings (CC BY 4.0) for the footprints and, for anything derived from the Germany/MaStR validation, the Marktstammdatenregister (Bundesnetzagentur, Datenlizenz Deutschland -- Namensnennung -- Version 2.0).

The full programme description is in the TraceTheSun concept note.