Data types
CartograFit datasets are organized into complementary data types (referred to internally as DatasetDTO), enabling different levels of analysis of parking and urban planning.
Data architecture
┌──────────────────────────────────────────────────────────────────┐
│ Cellules H3 │
│ (Agrégation par zone hexagonale) │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Cellule 1 │ │ Cellule 2 │ │ Cellule N │ │
│ │ - Stats │ │ - Stats │ │ - Stats │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Réseau routier │ │
│ │ ┌─────────────────┐ ┌───────────────────────────────┐ │ │
│ │ │ Routes │◄──│ Segments de route │ │ │
│ │ │ (OSM enrichi) │ │ (détail par tronçon) │ │ │
│ │ └────────┬────────┘ └───────────────────────────────┘ │ │
│ └───────────│──────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Cartographie terrain │ │
│ │ ┌────────────────┐ ┌────────────────┐ ┌──────────────┐ │ │
│ │ │ Parking │ │ Panneaux │ │ Marquages │ │ │
│ │ │ Areas │ │ routiers │ │ au sol │ │ │
│ │ └────────────────┘ └────────────────┘ └──────────────┘ │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Mobilier urbain │ │
│ └──────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────┘
Data sources
CartograFit combines two complementary imagery sources to deliver the most complete and reliable parking cadastre on the market. Each source brings distinct value, and it is their combination that makes our datasets so rich.
Street-level imagery
Our street-level imagery captures the urban landscape at street level, the way a pedestrian or a driver perceives it.
| Characteristic | Detail |
|---|---|
| GPS accuracy | ~1-2 meters |
| Attributes | Complete: parking type, road markings, signage, side of the road, match to the road network |
| Key strength | Richness of information — every space is characterized with a high level of detail |
| Limitation | Coverage depends on the availability of street-level imagery across the territory |
HD satellite imagery
Our HD satellite imagery provides an aerial view of the territory from latest-generation optical satellites (resolution 0.15 to 0.30 m/pixel depending on the area).
| Characteristic | Detail |
|---|---|
| GPS accuracy | ~3-5 meters |
| Attributes | Partial: some details are not observable from a nadir view (fine markings, signage, side of the road) |
| Key strength | Exhaustive coverage and freshness — satellite acquisitions are schedulable and cover an entire territory, including areas hard to reach with street-level imagery |
| Unique complement | Identifies elements invisible from the street: set-back parking, real ground footprints, off-street parking areas |
Why two separate layers?
Our datasets deliver street-level and satellite data in separate layers, and this is a deliberate choice:
- Transparency — you know exactly where every piece of data comes from and what accuracy to expect from it
- Freedom of use — depending on your need, you work with the most suitable layer: street-level accuracy for a regulatory inventory, satellite coverage for a fast territorial diagnostic
- Overlay — in QGIS or any other GIS, the two layers naturally overlay for a combined view of the territory
- Freshness — since satellite acquisitions are schedulable, satellite layers are often more recent than the available street-level imagery
Four data types are available in two variants (-street and -satellite): parking areas, street furniture, road markings and road signs. Parking lots are available only in the satellite variant. Roads are common to all sources. Road segments and H3 cells come in street-level (-street) and satellite (-satellite) variants with the same data formats.
HD satellite imagery
In addition to the vector layers, CartograFit offers the HD satellite imagery of your territory as an option, delivered as XYZ tiles accessible through a dedicated server with token-based authentication.
| Characteristic | Detail |
|---|---|
| Access | XYZ tile server (HTTP) with an authentication token |
| Resolution | 0.15 to 0.30 m/pixel depending on the area and sources |
| Compatible with | QGIS, ArcGIS, MapLibre, Leaflet, OpenLayers, any XYZ client |
| Use | Recent high-resolution basemap of your territory |
Why it matters:
- You get a recent basemap of your territory at an exceptional resolution
- You can overlay our data AND yours on top of this imagery in QGIS or any other GIS
- You visualize the terrain directly to validate or complete the vector data
- It is a pre-survey tool: identify areas of interest on the image before going out in the field
Which layer should you use?
| Need | Recommended layer | Why |
|---|---|---|
| Regulatory inventory (LOM law, accessible) | Street-level | GPS accuracy ~1-2m, complete attributes |
| Fast territorial diagnostic | Satellite | Exhaustive coverage of the territory |
| Areas not covered by street-level imagery | Satellite | Satellite covers hard-to-reach areas |
| Validation before a field audit | HD imagery + satellite layers | Visual pre-survey before going out |
| Complete combined view | Both overlaid | Combine street-level accuracy and satellite coverage |
The satellite layers are a pre-survey tool: they identify objects of interest across your territory. For regulatory use, we recommend validating satellite elements in the field. The street-level layers, thanks to their superior accuracy, require less validation.
The data types
H3 cells
Data aggregated by hexagonal zone using Uber's H3 system.
| Geometry | Primary use |
|---|---|
Polygon | Heatmaps, comparative analysis, KPIs |
Roads
Roads from OpenStreetMap, enriched with parking metadata.
| Geometry | Primary use |
|---|---|
LineString | Linear analysis, planning |
Road segments
Individual road segments with detailed parking density.
| Geometry | Primary use |
|---|---|
LineString | Fine-grained analysis, graduated visualization |
Parking areas
Parking spaces mapped individually.
| Geometry | Primary use |
|---|---|
Point | Precise mapping, navigation |
Parking lots
Parking lots identified through HD satellite imagery (satellite only).
| Geometry | Primary use |
|---|---|
Polygon | Off-street inventory, soil sealing |
Road signs
Road signs cataloged, in particular those related to parking.
| Geometry | Primary use |
|---|---|
Point | Regulations, constraints |
Road markings
Road markings cataloged (lines, symbols, text).
| Geometry | Primary use |
|---|---|
Point | Field analysis, validation |
Street furniture
Street furniture identified by our algorithms.
| Geometry | Primary use |
|---|---|
Point | Inventory, accessibility |
Common format
All our datasets share common characteristics:
| Property | Value |
|---|---|
| Projection | WGS84 (EPSG:4326) |
| Formats | GeoPackage (recommended), GeoJSON |
| Spatial indexing | H3 resolution 9 |
| Language | French |
Common fields
Every record includes identification and metadata fields:
| Field | Type | Description |
|---|---|---|
id | string | Unique identifier |
layer_type | string | Document type (e.g. parking_area, road) |
h3_index | string | Parent H3 cell |
h3_resolution | integer | H3 resolution (always 9) |
source_type | string | Source imagery type (street_level: street-level imagery, satellite_hd: HD satellite imagery) |
captured_at | datetime | Date of the source imagery |
Quality score
Every record includes a confidence score (fit_score) between 0 and 1 reflecting the reliability of the data:
| Score | Interpretation | Recommendation |
|---|---|---|
| ≥ 0.8 | High confidence | Direct use |
| 0.5 - 0.8 | Medium confidence | Verification recommended |
| < 0.5 | Low confidence | Verification required |
Available layers
The data types correspond to the 14 layers available in the GeoPackage and GeoJSON files. Source-dependent layers come in -street (street-level imagery) and -satellite (HD satellite imagery) variants:
| Layer | layer_type | TypeScript type |
|---|---|---|
parking_areas_street | parking_area | ParkingAreaDatasetDTO |
parking_areas_satellite | parking_area | ParkingAreaDatasetDTO |
parking_lots_satellite | parking_lot | ParkingLotDatasetDTO |
roads | road | RoadDatasetDTO |
road_segments_street | road_segment | RoadSegmentDatasetDTO |
road_segments_satellite | road_segment | RoadSegmentDatasetDTO |
h3_cells_street | h3_cell | H3CellDatasetDTO |
h3_cells_satellite | h3_cell | H3CellDatasetDTO |
urban_furniture_street | urban_furniture | UrbanFurnitureDatasetDTO |
urban_furniture_satellite | urban_furniture | UrbanFurnitureDatasetDTO |
road_markings_street | road_marking | RoadMarkingDatasetDTO |
road_markings_satellite | road_marking | RoadMarkingDatasetDTO |
traffic_signs_street | traffic_sign | TrafficSignDatasetDTO |
traffic_signs_satellite | traffic_sign | TrafficSignDatasetDTO |