Drone aerial view of urban tree canopy

Urban Tree Inventories

Two ways to inventory your urban forest.

We understand that every municipality must meet deadlines and stay within budget. That is why we are proposing an innovative approach to tree inventory — combining geospatial AI and modern methodology to deliver accurate results faster and at lower cost:

Urban forestry field worker scanning trees with tablet

Side-by-side comparison

Which approach is right for you?

New

NEW – Smart Tree Inventory:

A fast, affordable, field-free alternative using GeoAI and existing satellite/street-view data. An innovative method ideal for budget-conscious projects or as a preliminary assessment.

Traditional Field Inventory:

Time consuming on-ground survey with arborists collecting detailed health and condition data.

Both approaches deliver a complete tree census with rich attribute data. The choice depends on your timeline, budget, and accuracy requirements. Smart Tree Inventory is ideal for municipalities seeking rapid, cost-effective results and the flexibility to update without fieldwork. Traditional Field Inventory is best when maximum accuracy and detailed health assessments are non-negotiable.

Smart Tree Inventory

New
Advantages
  • More affordable
  • Fast turnaround
  • Available year-round — no seasonal constraints
  • Easy to update (external data collection)
Disadvantages
  • Less accurate than field surveys

Traditional Field Inventory

Advantages
  • High data accuracy
Disadvantages
  • Slow to complete
  • Expensive
  • Season dependent
  • Difficult to update

Pricing

Urban Tree Inventories – Pricing

Our Smart Tree Inventory service delivers a complete, field-free census of every street-visible tree in your municipality. Using satellite imagery, Street View data, and proprietary GeoAI, we generate a rich attribute set for each tree—without ever setting foot on site.

The result: accurate, year-round data at a fraction of the cost of traditional ground surveys.

Choose your tier – based on municipality population

Small
Starting from
$7,000
Population
1,000 – 29,999
Delivery Timeline
1 week
Medium
Starting from
$13,000
Population
30,000 – 99,999
Delivery Timeline
2 weeks
Large
Contact us
Population
100,000+
Delivery Timeline
Custom timeline

All tiers include the same rich attribute set, dashboard, webmap, and flexible data exports. Pricing scales with the number of trees processed – not with fieldwork hours – so you get maximum value without seasonal restrictions or on-site delays.

Final pricing is customized to your city's needs and may vary based on inventory size, Google Maps coverage in kilometres, and integration of custom data sources.

Pricing is charged yearly. Annual upgrades can incorporate new Google Street View data, high-resolution satellite imagery, and LiDAR coverage, keeping your inventory current without a full re-survey.

Still not sure about your requirements? Contact us and we'll help you find the right approach. Contact us

What's included in every inventory

A live dashboard with key metrics and health summaries

An interactive webmap to explore, filter, and query every tree

Final GIS data delivered in your preferred format (Shapefile, GeoJSON, File Geodatabase, etc.)

A complete data dictionary with the following attributes per tree:

Data dictionary

AttributeDescription
IDUnique tree identifier
ACS (m)Average crown spread (meters)
Area Canopy (m²)Canopy area in square meters
GenusBotanical genus
Common nameCommon tree name
Comment on genusHow the genus was identified
Height (m)Tree height in meters
AddressCivic address (geocoded)
Latitude / LongitudeCoordinates in degrees (WGS84)
Phytosanitary classificationHealth status: Good / Average / Poor
Phytosanitary descriptionDetailed health observation
Street image dateDate of source street imagery
Near StreetPublic (True) or private (False) tree
DBH (cm)Diameter at breast height
Tree imageGoogle Street View link + still image

Ready to see your urban forest from above?

Contact us for a custom quote, to discuss your specific needs, or to request a sample dataset from a city similar to yours.

See it in action — Farnham case study