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Datasets:
xmpuspus
/
solar-map-ph

Modalities:
Geospatial
Tags:
remote-sensing
solar-energy
rooftop-solar
philippines
geospatial
sentinel-2
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
_meta: struct<n_cities_with_detections: int64, n_total_high: int64, n_total_candidate: int64, n_total_new_h (... 35 chars omitted)
  child 0, n_cities_with_detections: int64
  child 1, n_total_high: int64
  child 2, n_total_candidate: int64
  child 3, n_total_new_high: int64
  child 4, share_new_high: double
rows: list<item: struct<name: string, province: string, n_high: int64, n_candidate: int64, n_new_high: int (... 166 chars omitted)
  child 0, item: struct<name: string, province: string, n_high: int64, n_candidate: int64, n_new_high: int64, n_confi (... 154 chars omitted)
      child 0, name: string
      child 1, province: string
      child 2, n_high: int64
      child 3, n_candidate: int64
      child 4, n_new_high: int64
      child 5, n_confirmed_high: int64
      child 6, n_new_candidate: int64
      child 7, n_confirmed_candidate: int64
      child 8, sum_kwp_high: double
      child 9, sum_kwp_all: double
      child 10, area_km2: double
      child 11, high_per_km2: double
validation_methodology: string
cases: list<item: struct<id: string, city: string, province: string, lat: double, lon: double, area_m2: int (... 96 chars omitted)
  child 0, item: struct<id: string, city: string, province: string, lat: double, lon: double, area_m2: int64, kwp_equ (... 84 chars omitted)
      child 0, id: string
      child 1, city: string
      child 2, province: string
      child 3, lat: double
      child 4, lon: double
      child 5, area_m2: int64
      child 6, kwp_equiv: int64
      child 7, note: string
      child 8, v0_hotspot_idx: string
      child 9, image_path: string
      child 10, image_ok: bool
generated_utc: timestamp[s]
to
{'generated_utc': Value('timestamp[s]'), 'validation_methodology': Value('string'), 'cases': List({'id': Value('string'), 'city': Value('string'), 'province': Value('string'), 'lat': Value('float64'), 'lon': Value('float64'), 'area_m2': Value('int64'), 'kwp_equiv': Value('int64'), 'note': Value('string'), 'v0_hotspot_idx': Value('string'), 'image_path': Value('string'), 'image_ok': Value('bool')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              _meta: struct<n_cities_with_detections: int64, n_total_high: int64, n_total_candidate: int64, n_total_new_h (... 35 chars omitted)
                child 0, n_cities_with_detections: int64
                child 1, n_total_high: int64
                child 2, n_total_candidate: int64
                child 3, n_total_new_high: int64
                child 4, share_new_high: double
              rows: list<item: struct<name: string, province: string, n_high: int64, n_candidate: int64, n_new_high: int (... 166 chars omitted)
                child 0, item: struct<name: string, province: string, n_high: int64, n_candidate: int64, n_new_high: int64, n_confi (... 154 chars omitted)
                    child 0, name: string
                    child 1, province: string
                    child 2, n_high: int64
                    child 3, n_candidate: int64
                    child 4, n_new_high: int64
                    child 5, n_confirmed_high: int64
                    child 6, n_new_candidate: int64
                    child 7, n_confirmed_candidate: int64
                    child 8, sum_kwp_high: double
                    child 9, sum_kwp_all: double
                    child 10, area_km2: double
                    child 11, high_per_km2: double
              validation_methodology: string
              cases: list<item: struct<id: string, city: string, province: string, lat: double, lon: double, area_m2: int (... 96 chars omitted)
                child 0, item: struct<id: string, city: string, province: string, lat: double, lon: double, area_m2: int64, kwp_equ (... 84 chars omitted)
                    child 0, id: string
                    child 1, city: string
                    child 2, province: string
                    child 3, lat: double
                    child 4, lon: double
                    child 5, area_m2: int64
                    child 6, kwp_equiv: int64
                    child 7, note: string
                    child 8, v0_hotspot_idx: string
                    child 9, image_path: string
                    child 10, image_ok: bool
              generated_utc: timestamp[s]
              to
              {'generated_utc': Value('timestamp[s]'), 'validation_methodology': Value('string'), 'cases': List({'id': Value('string'), 'city': Value('string'), 'province': Value('string'), 'lat': Value('float64'), 'lon': Value('float64'), 'area_m2': Value('int64'), 'kwp_equiv': Value('int64'), 'note': Value('string'), 'v0_hotspot_idx': Value('string'), 'image_path': Value('string'), 'image_ok': Value('bool')})}
              because column names don't match

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SolarMap.PH data products

Open rooftop-solar detections for the Philippines, built from satellite imagery by SolarMap.PH. Rooftops are detected with CLIP image embeddings plus a gradient-boosted classifier, then segmented and snapped to OpenStreetMap buildings. All geometry is EPSG:4326 (WGS84). CC-BY-4.0.

The trained model itself lives in a separate repo: xmpuspus/solar-map-ph-clf-v4.

Coverage

Per-building and per-tile detections across NCR plus Cebu, Davao, Cagayan de Oro, Iloilo, Calabarzon, Bacolod, and Legazpi, with city- and barangay-level aggregates.

What's here

File Granularity
per_building_solar_ncr.geojson Per OSM building (commercial/industrial/public; residential roofs suppressed)
rooftop_solar_<region>.geojson Per 240 m tile center, by region
solar_map_ph_2026Q2.geojson Per city
city_detection_counts*.json, city_solar_saturation.json City and 240 m-cell aggregates
solar_saturation_ncr.geojson, hot_spots_2026Q2.geojson Saturation and hotspot layers
residential_solar_aggregate.json Residential counts only, no geometry (privacy)
franchise_cities_polygons.geojson Distribution-utility franchise boundaries
SCHEMA.md Full field-by-field schema for every file
BENCHMARKS.md, MODEL_CARD.md Evaluation tables and model card
dataset_v4.npz The labeled training tiles

See SCHEMA.md for the exact property schema of each file (building id, footprint area, panel area, kWp estimate, calibrated confidence, and so on).

Notes

  • Residential rooftops are intentionally suppressed in the per-building file; only aggregate counts are published, for privacy.
  • kwp_estimate is a rule-of-thumb (panel area / 6.0), not a metered figure.
  • Detections are model output, not a verified installation registry.

Links and citation

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