Street furniture

The Street furniture type (UrbanFurnitureDatasetDTO) represents the urban amenity elements cataloged by our algorithms: benches, street lamps, trees, bus stops, waste baskets, and many more.

Description

Street furniture data is produced by our algorithms from field imagery and HD satellite imagery. Each element includes:

• The estimated GPS position
• The element's category and subcategory
• A confidence score reflecting the reliability of the identification

Availability by source

Street furniture is available in two distinct layers:

Property	Field imagery	HD satellite imagery	Notes
location	GPS ~1-2m	GPS ~3-5m	Lower accuracy from satellite
category	Complete	Partial	Limited categories from satellite
subcategory	Complete	Partial
fit_score	0-1	0-1	Same scale

Main properties

Identification

Property	Type	Description	Example
id	string	Opaque unique identifier	uf_e6f7a8b9c0d1e2f3
layer_type	string	Document type	urban_furniture
h3_index	string	Parent H3 cell	893b0c6a9a7ffff

Location

Property	Type	Description	Example
location	GeoPoint	Estimated GPS position	{ lat: -21.12, lon: 55.48 }

Classification

Property	Type	Description	Example
category	string	Main category	seating, lighting, vegetation
subcategory	string	Specific type	bench, street_lamp, tree

Confidence score

Property	Type	Description
fit_score	number	Normalized confidence score (0-1)

Confidence score

Only elements with a score >= 0.5 are included in the dataset. The score reflects the reliability of the identification by our algorithms.

Traceability

Property	Type	Description
source_type	string	Source imagery type (street_level: ground-level imagery, satellite_hd: satellite imagery)
captured_at	datetime	Date of the source imagery

Categories

Street furniture is organized into 12 main categories:

Category	Description	Subcategory examples
seating	Seating	bench
waste	Waste	waste_basket, recycling
lighting	Lighting	street_lamp
vegetation	Vegetation	tree, tree_row
barrier	Barriers	bollard, gate, planter
transport	Transport	bus_stop, charging_station
accessibility	Accessibility	crossing, lowered (accessible kerb ramp)
signage	Signage	traffic_signals, stop, give_way
utility	Utilities	fire_hydrant, manhole
service	Services	drinking_water, toilets
security	Security	surveillance (camera), defibrillator
advertising	Advertising	billboard, column (Morris column)

Confidence score

The confidence score (fit_score) is computed by our algorithms and normalized between 0 and 1.

Interpretation

Score	Interpretation
>= 0.75	High reliability, direct use
0.50 - 0.74	Medium reliability, verification recommended

Filtering by score

Only elements with a score >= 0.50 are indexed. Low-reliability elements are excluded to guarantee data quality.

Recommended QGIS style

Street furniture is displayed in amber (circles) by default:

1. Download the style file: urban_furniture.qml
2. In QGIS: right-click the layer -> Properties -> Symbology
3. Style -> Load Style -> select the .qml file

Download and styling guide →

Use cases

Street furniture inventory

Catalog amenities by category:

• Number of public benches
• Lighting coverage
• Urban vegetation

Accessibility analysis

Identify accessibility amenities:

• Pedestrian crossings and accessible kerb ramps
• Tactile paving strips
• Accessible parking spaces

Urban planning

Use the data to:

• Identify under-equipped areas
• Plan the placement of new amenities
• Map public services

Maintenance

Locate amenities to:

• Plan maintenance rounds
• Track the condition of furniture
• Optimize interventions

GeoJSON example

{
  "type": "Feature",
  "properties": {
    "id": "uf_e6f7a8b9c0d1e2f3",
    "layer_type": "urban_furniture",
    "h3_index": "893b0c6a9a7ffff",
    "category": "seating",
    "subcategory": "bench",
    "fit_score": 0.72,
    "source_type": "street_level",
    "captured_at": "2024-06-15T14:30:00Z"
  },
  "geometry": {
    "type": "Point",
    "coordinates": [55.48, -21.12]
  }
}

Relation to other types

Street furniture is linked to the other data types through:

• H3 cell: each element belongs to an H3 cell (via h3_index)
• Location: enables proximity analyses with parking areas

This relationship makes it possible to cross-reference data for combined analyses (e.g. benches near parking areas).