Sample project
Street Scene Analysis
Measuring the physical character of streets at scale using semantic segmentation, object detection and colour analysis on street-level imagery.
- Network analysed
- 310 km of streets
- Sample points
- 6,200 locations
- Images processed
- 24,800 (4 headings)
- Indicators
- 7 per segment
- Duration
- 5 weeks
- Network analysed
- 310 km
- Images processed
- 24,800
- Indicators per segment
- 7
- Validation agreement
- ±3.2 pp
Sampled every 50 metres
Four headings per sample point
Segmentation, detection and colour
Against a 200-point manual sample
Problem
What needed answering
An urban research group wanted to compare the physical quality of streets across a city: how green they are, how enclosed they feel, how much of the frontage is active, how dominated they are by vehicles.
These qualities are normally assessed by sending people out to score streets by hand. That approach produces good judgement on a small sample and becomes unaffordable past a few dozen streets, while also introducing inconsistency between assessors.
The team needed indicators that were consistent across the whole network, reproducible when re-run, and transparent enough that a planning committee could challenge any individual number and see how it was derived.
Data sources
What went in
Street-level imagery
Licensed panoramic imagery sampled at fixed intervals along the network, four headings per point.
Road network geometry
Open mapping data defining the segments along which points were sampled.
Segmentation model
An open, urban-scene segmentation model producing per-pixel class labels.
Detection model
An open object detection model for vehicles, pedestrians, street furniture and frontage features.
Administrative boundaries
For aggregating segment-level indicators to reportable areas.
Method
How it was done
Sample points were placed every 50 m along the network, with four camera headings per point, giving 24,800 images. Sampling by distance rather than by segment prevents long arterial roads from being under-represented relative to short residential streets.
Semantic segmentation produced per-pixel class shares for each image: sky, building, vegetation, road, sidewalk, vehicle, person and street furniture. Class shares were averaged across the four headings to give one profile per point.
The vegetation share became a green view index, which is the established measure of perceived greenery from the street. Building and sky shares together gave an enclosure ratio, a standard proxy for how contained a street feels.
Object detection ran in parallel to count vehicles, pedestrians and frontage features. Detection and segmentation answer different questions: segmentation says how much of the view is vehicle, detection says how many vehicles there are, and a street can score high on one and low on the other.
Colour analysis extracted the dominant palette per image, which distinguished areas with consistent historic materials from those with mixed modern frontage. Every indicator was aggregated to segment and district level, with the underlying per-image values retained so any number could be traced back to the images that produced it.
Processing
The pipeline
Each stage produced an artefact that the next stage consumed, so any result can be traced back to the input that created it.
- 01
Street view image
Image set24,800 licensed images sampled at 50 m intervals along 310 km of network, four headings per sample point.
- 02
Semantic segmentation
Class sharesPer-pixel classification into sky, building, vegetation, road, sidewalk, vehicle, person and furniture, averaged across headings.
- 03
Object detection
Detection tableCounts of vehicles, pedestrians, street furniture and active frontage features, with confidence thresholds applied consistently.
- 04
Colour analysis
Palette profileDominant palette extraction per image, aggregated to characterise material consistency along each street segment.
- 05
Urban environment analysis
Indicator layerSeven indicators per segment, joined to the network and aggregated to district level, delivered as maps and a queryable dataset.
Visualization
The output
- Building31.6%
- Sky24.1%
- Road20.7%
- Vegetation11.6%
- Sidewalk8.3%
- Vehicle2.9%
- Person0.8%
Data
Results table
| Segment | Green view % | Enclosure ratio | Sky % | Vehicle % | Pedestrians / img |
|---|---|---|---|---|---|
| Elm Avenue | 31.4 | 0.84 | 18.2 | 6.1 | 2.4 |
| High Street | 8.2 | 1.62 | 11.4 | 14.8 | 9.1 |
| Canal Road | 22.7 | 0.41 | 29.8 | 9.2 | 0.8 |
| Park Terrace | 38.9 | 0.72 | 21.6 | 3.4 | 3.7 |
| Station Approach | 4.1 | 1.94 | 8.9 | 21.3 | 12.6 |
Findings
The key comparison
- Park Terracehighest green view38.9
- Elm Avenue31.4
- Canal Road22.7
- High Street8.2
- Station Approachlowest4.1
Result
What the analysis showed
Green view index across the network ranged from 4.1% to 38.9%, and the distribution followed district boundaries far more closely than the team expected. Two adjacent districts differed by more than twenty points with no change in street type.
Enclosure ratio and green view were only weakly related. Several streets scored well on greenery while feeling open and exposed, which matters because those two qualities are often treated as one in streetscape policy.
Vehicle pixel share and vehicle counts disagreed on eleven segments. In each case the segment had few but very large vehicles, which segmentation weights heavily and detection does not. Reporting both prevented a misleading conclusion.
Manual scoring of a 200-point validation sample agreed with the automated green view index within 3.2 percentage points on average, which was accepted as sufficient for comparative rather than absolute use.
Deliverables
What was handed over
- Per-image class share table for all 24,800 processed images
- Segment-level indicator layer with seven measures, joined to the road network
- District-level aggregation tables for reporting
- Detection counts with confidence thresholds and model version recorded
- Colour palette profiles per segment
- Interactive map with indicator switching, plus a method note covering models, thresholds and validation
Keep exploring
Where to go next
Related services
- Street View DataStreet-level imagery turned into measurable indicators for every segment of a network.
- GIS AnalysisSpatial analysis and geographic data processing, from overlays to network models.
- Map VisualizationInteractive maps and data visualization built for clarity and fast loading.
- Geospatial DataLocation, road, boundary and land-use datasets, cleaned and projected correctly.
Related reading
- What Is Geospatial Data?Geospatial data is any data with a location attached. What makes it different is that distance, containment and adjacency between records carry meaning.
- How to Build a Geospatial Data PipelineMost spatial pipelines work perfectly once. The design decisions that matter are the ones that keep them working on run twenty.
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