Structured
drone images
of real cities,
licensed for
model training

Aerial imagery for the Physical AI teams building world models and spatial intelligence.

The largest structured drone imagery archive of the physical world.

Millions

of images

Hundreds

of cities covered

Accurate, measurable imagery from every season and every angle.

  • Illustrated 3D tile of Miami, FL
    Miami, FL
  • Illustrated 3D tile of Vancouver, BC
    Vancouver, BC
  • Illustrated 3D tile of Chicago, IL
    Chicago, IL
  • Illustrated 3D tile of Halifax, NS
    Halifax, NS
  • Illustrated 3D tile of Phoenix, AZ
    Phoenix, AZ
  • Illustrated 3D tile of Toronto, ON
    Toronto, ON
  • Illustrated 3D tile of Seattle, WA
    Seattle, WA
  • Illustrated 3D tile of Ottawa, ON
    Ottawa, ON
  • Illustrated 3D tile of Calgary, AB
    Calgary, AB
  • Illustrated 3D tile of New York, NY
    New York, NY
  • Illustrated 3D tile of Montréal, QC
    Montréal, QC
  • Illustrated 3D tile of Miami, FL
    Miami, FL
  • Illustrated 3D tile of Vancouver, BC
    Vancouver, BC
  • Illustrated 3D tile of Chicago, IL
    Chicago, IL
  • Illustrated 3D tile of Halifax, NS
    Halifax, NS
  • Illustrated 3D tile of Phoenix, AZ
    Phoenix, AZ
  • Illustrated 3D tile of Toronto, ON
    Toronto, ON
  • Illustrated 3D tile of Seattle, WA
    Seattle, WA
  • Illustrated 3D tile of Ottawa, ON
    Ottawa, ON
  • Illustrated 3D tile of Calgary, AB
    Calgary, AB
  • Illustrated 3D tile of New York, NY
    New York, NY
  • Illustrated 3D tile of Montréal, QC
    Montréal, QC
  • Illustrated 3D tile: Vancouver, BC, Pacific temperate rainforest
    Vancouver, BC
    Pacific temperate rainforest
  • Illustrated 3D tile: Edmonton, AB, Aspen parkland at the boreal edge
    Edmonton, AB
    Aspen parkland at the boreal edge
  • Illustrated 3D tile: Saskatoon, SK, Northern Great Plains
    Saskatoon, SK
    Northern Great Plains
  • Illustrated 3D tile: Phoenix, AZ, Sonoran Desert
    Phoenix, AZ
    Sonoran Desert
  • Illustrated 3D tile: Toronto, ON, Mixedwood plains
    Toronto, ON
    Mixedwood plains
  • Illustrated 3D tile: Houston, TX, Gulf Coast prairies and marshes
    Houston, TX
    Gulf Coast prairies and marshes
  • Illustrated 3D tile: Calgary, AB, Rocky Mountain foothills
    Calgary, AB
    Rocky Mountain foothills
  • Illustrated 3D tile: Highways, Interchanges, ramps and overpasses
    Highways
    Interchanges, ramps and overpasses
  • Illustrated 3D tile: Roads, Intersections, lane markings and crosswalks
    Roads
    Intersections, lane markings and crosswalks
  • Illustrated 3D tile: Residential parcels, Lots, roofs, driveways and yards
    Residential parcels
    Lots, roofs, driveways and yards
  • Illustrated 3D tile: Commercial parcels, Retail, parking and loading areas
    Commercial parcels
    Retail, parking and loading areas
  • Illustrated 3D tile: Tree species, Maple, oak, birch, cherry, spruce, pine and aspen
    Tree species
    Maple, oak, birch, cherry, spruce, pine and aspen
  • Illustrated 3D tile: Vancouver, BC, Pacific temperate rainforest
    Vancouver, BC
    Pacific temperate rainforest
  • Illustrated 3D tile: Edmonton, AB, Aspen parkland at the boreal edge
    Edmonton, AB
    Aspen parkland at the boreal edge
  • Illustrated 3D tile: Saskatoon, SK, Northern Great Plains
    Saskatoon, SK
    Northern Great Plains
  • Illustrated 3D tile: Phoenix, AZ, Sonoran Desert
    Phoenix, AZ
    Sonoran Desert
  • Illustrated 3D tile: Toronto, ON, Mixedwood plains
    Toronto, ON
    Mixedwood plains
  • Illustrated 3D tile: Houston, TX, Gulf Coast prairies and marshes
    Houston, TX
    Gulf Coast prairies and marshes
  • Illustrated 3D tile: Calgary, AB, Rocky Mountain foothills
    Calgary, AB
    Rocky Mountain foothills
  • Illustrated 3D tile: Highways, Interchanges, ramps and overpasses
    Highways
    Interchanges, ramps and overpasses
  • Illustrated 3D tile: Roads, Intersections, lane markings and crosswalks
    Roads
    Intersections, lane markings and crosswalks
  • Illustrated 3D tile: Residential parcels, Lots, roofs, driveways and yards
    Residential parcels
    Lots, roofs, driveways and yards
  • Illustrated 3D tile: Commercial parcels, Retail, parking and loading areas
    Commercial parcels
    Retail, parking and loading areas
  • Illustrated 3D tile: Tree species, Maple, oak, birch, cherry, spruce, pine and aspen
    Tree species
    Maple, oak, birch, cherry, spruce, pine and aspen

Good aerial data for physical AI training is hard to get

A stack of aerial image plates sits beside a tall licence document marked with a question mark, while a lowered barrier arm blocks the road out.?

Data provenance risk

Unclear image rights can stall a launch, or force you to retrain from scratch.

A city block is cut into a full grid of hexagons. A drone, tethered to its support van, captures one hexagon at a time while every other hexagon is still uncollected.

Data collection cost

Collecting the data yourself burns runway before training even starts.

Image prints with missing or mismatched labels lie scattered across a work surface beside sorting bins marked .jpg, .tif and a question mark..jpg.tif?

Data curation and preparation

Your best engineers spend time cleaning imagery instead of training models.

A car's street-level camera cone reaches only the side of a building, never its rooftop landing pad, and a patch of curb rendered in coarse blocky pixels loses the edge detail.H

Imagery diversity, resolution, and accuracy

Top-down airplane and satellite imagery with limited sun angles isn't enough to complement streetview datasets.

Spexi archive data is the solution

Spexi's drone archive data supports model training as one licensed, training-ready dataset of accurate, measurable imagery, with an aerial view of every city block in its coverage.

A drone captures the building's rooftop landing pad straight down and the curb at an angle, both shown in green, alongside the car's existing street-level camera view. A chip reads 2.8 cm per pixel, and the curb's paint lines are crisp.H2.8 cm per pixel

The aerial view your model is missing.

Add the aerial view of every covered city, from multiple angles and altitudes at 2.8 cm or sharper, to the ground-level data you already have.

A floating island cut in the exact shape of the continental United States, with layered soil along its edges, state borders on its green surface, and clusters of glowing green hexagons rising from covered cities from Seattle, San Francisco and Los Angeles to Denver, Houston, Chicago, New York and Miami.

Access the largest standardized drone imagery archive.

License structured drone imagery from hundreds of cities, and put the runway you would spend on a fleet into the model.

Sixteen drone image cards lie in a neat grid, each carrying the same metadata strip. A query panel above sets Spexi Image API parameters: capture type oblique, pitch from -60 to -30, heading from 90 to 180, and a capture date range, inside a bounding box for your area. Green dashed lines run from the query to the five cards that match, which lift off the grid with green outlines while the rest fade. A label reads Training mix, 5 of 16 match.Spexi Image API · Search imagesbboxyour areacapture_typeobliquepitch[-60, -30]heading[90, 180]captured_at[2025-03, 2025-09]Training mix5 of 16 match

Query the training mix. No sorting required.

Every image is captured to the same standard and carries the same metadata, so your team builds a training mix with queries instead of tedious sorting.

The stack of aerial image plates now sits beside a single licence document marked with a check, and the barrier arm is raised so a car drives out on the open road.

One contract clears every image.

One licence covers every image, with rights to train models and deploy them commercially, so your legal team reviews a single contract instead of a pile of sources.

  • Close-up drone image of Winnipeg
    Winnipeg
  • Close-up drone image of Seattle
    Seattle
  • Close-up drone image of Portland
    Portland
  • Close-up drone image of San Francisco
    San Francisco
  • Close-up drone image of Los Angeles
    Los Angeles
  • Close-up drone image of San Diego
    San Diego
  • Close-up drone image of Las Vegas
    Las Vegas
  • Close-up drone image of Phoenix
    Phoenix
  • Close-up drone image of Salt Lake City
    Salt Lake City
  • Close-up drone image of Denver
    Denver
  • Close-up drone image of Dallas
    Dallas
  • Close-up drone image of Houston
    Houston
  • Close-up drone image of Austin
    Austin
  • Close-up drone image of Chicago
    Chicago
  • Close-up drone image of Detroit
    Detroit
  • Close-up drone image of Nashville
    Nashville
  • Close-up drone image of Atlanta
    Atlanta
  • Close-up drone image of Miami
    Miami
  • Close-up drone image of New York
    New York
  • Close-up drone image of Boston
    Boston
  • Close-up drone image of Winnipeg
    Winnipeg
  • Close-up drone image of Seattle
    Seattle
  • Close-up drone image of Portland
    Portland
  • Close-up drone image of San Francisco
    San Francisco
  • Close-up drone image of Los Angeles
    Los Angeles
  • Close-up drone image of San Diego
    San Diego
  • Close-up drone image of Las Vegas
    Las Vegas
  • Close-up drone image of Phoenix
    Phoenix
  • Close-up drone image of Salt Lake City
    Salt Lake City
  • Close-up drone image of Denver
    Denver
  • Close-up drone image of Dallas
    Dallas
  • Close-up drone image of Houston
    Houston
  • Close-up drone image of Austin
    Austin
  • Close-up drone image of Chicago
    Chicago
  • Close-up drone image of Detroit
    Detroit
  • Close-up drone image of Nashville
    Nashville
  • Close-up drone image of Atlanta
    Atlanta
  • Close-up drone image of Miami
    Miami
  • Close-up drone image of New York
    New York
  • Close-up drone image of Boston
    Boston

Aerial imagery is usually licensed for viewing. Training rights are a separate negotiation.

Spexi licenses the archive for training and commercial deployment with one simple contract structure.

Typical aerial imagery licence

  • View in maps and reports
  • Train models on the imagery
  • Deploy trained models commercially
  • Camera pose on every image

Spexi Model Training licence

  • View in maps and reports
  • Train models on the imagery
  • Deploy trained models commercially
  • Camera pose on every image

Every image is a structured record

The Spexi pilot network captures cities in standardized, automated flight plans. Each image enters the archive with its footprint, camera pose and timestamps, making it accurate to place, measurable and easy to filter to the scenes your model needs most.

Spexi Image API · Search images

GET /developer-api/api/v1/image

p
lon,lat[,radiusMeters]
bbox
minLon,minLat,maxLon,maxLat
polygon
WKT, up to 1 km²
capture_type
panorama, orthomosaic, oblique
captured_at
[start, end]
heading
[0, 360), 0 = north
pitch
[-135, 45], -90 = nadir
altitude
metres above ground
limit
1 to 1000, default 100
cursor
next-page token
focused
default true
pixel_bounds
default false

→ GeoJSON features: footprint, camera pose, capture time

The result: query the exact imagery your model needs.

N

Intersections

p
intersection coordinates, 30 m radius each
pitch
-90 (top-down) and -45 (angled)
cursor
page through every match
  • South-facing drone image of San Francisco, Jul 11, 2024 · 10:46
    Vancouver
  • South-facing drone image of San Francisco, Jul 20, 2024 · 10:40
    Tampa
  • South-facing drone image of San Francisco, Aug 23, 2026 · 11:04
    Seattle
  • South-facing drone image of San Francisco, Jul 16, 2024 · 08:27
    Dallas
  • South-facing drone image of San Francisco, Jul 9, 2024 · 07:08
    Los Angeles

Used by leading AI labs

Explore structured dataset

What only the full archive makes possible

  • Generalizable 3D reconstruction

  • Multi-view geometry pre-training

  • Novel-view synthesis at scale

  • City-scale geolocalization

  • Aerial foundation models

  • Robustness across regions

  • Cross-city generalization

  • Long-tail object detection

  • New

    Temporal and seasonal robustness

  • Sim-to-real benchmarking

  • Generalizable 3D reconstruction

  • Multi-view geometry pre-training

  • Novel-view synthesis at scale

  • City-scale geolocalization

  • Aerial foundation models

  • Robustness across regions

  • Cross-city generalization

  • Long-tail object detection

  • New

    Temporal and seasonal robustness

  • Sim-to-real benchmarking

Features and specs

Archive

Images

Millions of images
and growing

Area

Millions of acres
and growing

Capture dates

2024 to today

Geography

Hundreds of U.S.
and Canadian cities.
Expanding globally.

Imagery

Accurate, measurable static images at 2.8 cm or sharper.

Product

Static Images
(top-down, angled)

Resolution

2.8 cm (1.1 in) GSD
or sharper

Multi-view overlap

70%

Image density

Up to 3,050 images per km²

Metadata and delivery

Metadata fields

Footprint polygon, capture type, heading, pitch, roll, altitude above ground, 2D camera position, capture and processing timestamps, tag, open attributes

Query

Filter on any field via metadata file

Delivery

Bulk transfer

File format

JPG

Data products included

Oblique static image: an angled drone photo of a construction site beside an intersection, with a crane, equipment and traffic visible.
Angled
Nadir static image: a straight-down drone photo of the same intersection, showing crosswalks, signal poles and barricades.
Top-down

Static Images

Derivative-ready

Orthomosaic of a road intersection, viewed straight down: lane arrows, crosswalk lines, traffic signal poles, curbs and landscaping are all map-ready and distinct.

Orthomosaic-ready subset

3D model of an intersection, built from drone imagesThe orthomosaic of a road intersection is draped over a floating 3D block of ground, with traffic signal poles and a large tree standing up from the surface. Above it, the oblique and nadir drone photos it was built from hang in the air, with dashed green rays running from each photo down to the model.ObliqueNadir

3D-reconstruction-ready subset

Security, compliance, and data terms

Your data rights

Train models on the archive and deploy them commercially for the term of your licence. Raw images may not be resold.

Exclusivity

Non-exclusive licence.

Chain of custody

Every capture is timestamped, geo-located, and verified. Each image also carries its processing timestamp, camera position, and footprint.

Privacy

Faces and licence plates are not identifiable at the archive’s resolution.

Flight operations

Flown by vetted local pilots following Transport Canada and FAA rules.

Data residency

US customers' data is stored in the US, while Canadian customers' data is stored in Canada.

A drone shines a green capture beam onto a 3D model of a waterfront city.
Image API · Search ImagesGeoJSON Feature{"type": "Feature""geometry": { "type": "Polygon", … }"properties": {"id": "[image_id]""captureType": "oblique""capturedAt": "2026-09-14T15:42:08Z""processedAt": "2026-09-14T19:05:51Z""heading": 135"pitch": -45"roll": 0.4"altitude": 120"cameraPosition": { "type": "Point", … }"distance": 58.2"tag": "[tag]"}"links": [ … ]}Illustrative values · fields from the Search Images response
  1. 1. Captured
  2. 2. Catalogued
  3. 3. Licensed
  4. 4. Transferred

Frequently asked questions

Have a question about your model or your use case? Our team can walk you through the archive.

What does the archive cover?

Millions of acres covering hundreds of cities across the U.S. and Canada, captured from 2024 to today. Contact Sales for the full city list.

What resolution is the imagery?

Every image is 2.8 cm (1.1 in) ground sample distance (GSD) or sharper.

What metadata comes with each image?

Footprint polygon, capture type, heading, pitch, roll, altitude above ground, 2D camera position, capture and processing timestamps, a tag and an open attributes field.

How is the archive delivered?

By bulk transfer.

What can we do with models we train?

Use them and deploy them commercially for the term of your licence.

What happens to our models when the licence term ends?

Your models are yours to keep and use as you see fit.

Can we resell or share the raw images?

No. The licence does not allow resale of the raw images.

Is the licence exclusive?

No, it is non-exclusive.

Are new captures included during the licence?

Your license is scoped to the size of the image archive at the time of purchase. Amendments can be made as new imagery is added to the archive.

How is privacy handled?

Faces and licence plates are not identifiable at the archive’s resolution.

Can defence and non-North American companies license it?

Yes. Consult export control and legal advice as necessary.

What could your team train on millions of accurate, measurable drone images of the physical world?