Smart City • Computer Vision • Geospatial AI

[REDACTED] Municipal Government

Urban Tree & Infrastructure Risk Intelligence

Client:UK Municipal Government (Northern England)
Timeline:6 months (2024)
Team:AI Engineering, GIS & Data Engineering
YOLOv8Vision TransformerPlanet APIMapboxPostGISMLOps
System Architecture

The Challenge

A UK city council responsible for 180,000 publicly-owned trees relied on manual ground inspection cycles every 3-5 years. High-risk trees (diseased or structurally compromised) were missed between cycles, leading to £2.1M in liability claims from property damage in just 18 months.

  • Manual inspections were too infrequent to catch rapid structural decline
  • High financial liability from property damage and injury claims
  • Inability to prioritize inspections based on infrastructure proximity
  • Lack of a centralized geospatial risk-ranked dashboard for tree officers

The Solution

Developed an AI-powered continuous monitoring system that identifies at-risk trees from aerial imagery and prioritizes them based on proximity to critical infrastructure.

  • Aerial Tree Health Detection using YOLOv8 for detection and ViT for classification
  • Geospatial Risk Scoring Engine combining CV health scores with GIS infrastructure data
  • Continuous Satellite Monitoring via Planet API for monthly health change detection
  • Field Inspector Mobile App for ground-truth validation and offline data recording
  • Council Web Dashboard (React + Mapbox) for risk-ranked maintenance scheduling

Lexer System's Approach

1

Multi-Model CV Pipeline

Deployed YOLOv8 for tree crown detection and a Vision Transformer (ViT) classifier to identify specific health markers like crown dieback and structural lean.

2

Infrastructure Risk Overlay

Integrated the council's GIS data (power lines, roads, buildings) to calculate a weighted risk score based on the "consequence of failure."

3

Satellite Health Tracking

Utilized Planet API to ingest monthly satellite imagery, allowing the AI to detect seasonal anomalies and storm damage in near real-time.

4

Inspector Feedback Loop

Designed a mobile app that allows ground inspectors to correct AI findings, which then automatically retrains the models via an MLOps pipeline.

Results & Impact

4,200
At-Risk Trees Found

Vs. 340 found in the previous manual cycle

94%
Inspection Accuracy

Precision in identifying high-priority risks

+8 Months
Lead Time

Increased window for proactive maintenance

£800K
Liability Reduction

Projected reduction in claims in first 12 months

Technical Highlights

ViT Health Classification

Using Vision Transformers to capture subtle texture changes in foliage that signal disease before structural failure occurs.

PostGIS Spatial Analytics

Complex spatial joins between detected tree boundaries and municipal infrastructure assets for real-time risk weighting.

Continuous Monitoring

Moving from a "snapshot" inspection model to a "continuous subscription" model using satellite and drone fusion.

Lessons Learned

  • Geospatial context (what the tree could hit) is as important as the tree's biological health for risk assessment
  • Field inspector adoption is higher when the AI is presented as a "prioritization tool" rather than a replacement
  • Satellite monitoring provides the necessary frequency that drone surveys cannot reach at scale

Next Steps

  • Expand to detect invasive species and urban heat island mitigation impacts
  • Integrate automated tree planting recommendations based on soil and shade data
  • Link with public incident reporting apps for crowdsourced risk signal detection

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