AI-ready real-world data, captured and delivered on demand

Spexi provides standardized real-world data to support training, validation, and continuous learning across AI systems.

Capture high-resolution data where your systems operate and receive structured datasets designed for direct integration into ML and geospatial pipelines.

200x faster than aircraft
30x better resolution than satellites
10x more cost efficient per unit area
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Real-world data infrastructure for AI systems

Designed for AI systems that rely on accurate, real-world inputs

Generate high-quality training datasets

Detect and analyze real-world changes over time

Maintain up-to-date digital twins

Validate model outputs against current conditions

Aerial view of an urban intersection with yellow crosswalks, multi-story buildings, parked cars, and trees lining the streets.

Static image datasets

Multi-view, ultra-high-resolution data for perception and training systems.Spexi’s core dataset provides multi-perspective data from real environments, improving model performance and scene understanding.

Best for:

  • ML training datasets

  • Perception model development

  • Asset recognition systems

  • Infrastructure analysis

Aerial view of an urban street intersection with yellow crosswalk lines and several parked cars beside multi-story buildings.

Orthomosaic datasets

Structured spatial datasets for mapping and geospatial AI systems. Orthomosaics provide orthorectified, high-precision spatial datasets for large-scale analysis.

Optimized for:

  • GIS model layers

  • Spatial reasoning systems

  • Planning and simulation environments

Specifications:

  • Resolution: ~2.8 cm GSD

  • Coordinate system: WGS84

  • Tile streaming: supported

  • Up to 10cm absolute accuracy when aligned with control data

Aerial view of an urban street intersection with yellow crosswalk lines and several parked cars beside multi-story buildings.

API distribution

Access, query, and retrieve AI-ready real-world datasets on demand through a flexible API designed for machine learning pipelines and geospatial systems.

Spatial querying

Search by point, radius, or bounding box to retrieve data for any location.

Perspective filtering

Filter by camera angle, direction, and altitude to match your model requirements.

Aerial view of suburban houses showing a backyard deck with outdoor furniture and a swimming pool.

All images mode

Retrieve every dataset intersecting your area of interest

‍Returns all available data matching your spatial query, enabling complete coverage and maximum recall.

Best for:

  • High-volume data pipelines

  • Bulk dataset generation

  • Comprehensive analysis workflows

Multi-view mode

Retrieve multiple perspectives of the same location

Returns a representative set of images across viewing angles (top-down + oblique directions), providing richer spatial context.

Best for:

  • Perception model training

  • Scene understanding

  • Property and asset analysis

Focused mode

Retrieve the most relevant dataset for a location

Applies intelligent filtering to return the best image representing your area of interest, reducing noise and improving efficiency.

Best for:

  • End-user applications

  • Map interfaces and pop-ups

  • “Best image” selection workflows

How Spexi works

From capture request to AI-ready dataset, Spexi makes real-world data collection fast, flexible, and operationally repeatable.

Get the real-world data your AI needs

1. Request coverage where it matters

Define the locations, assets, or regions you need captured based on where your models need better visibility, fresher inputs, or updated ground truth.

2. Spexi coordinates collection on demand

Spexi activates its distributed pilot network to capture high-resolution aerial data at the requested place and time without the long refresh cycles of traditional imagery programs.

3. Data is standardized for AI workflows

Captured data is processed into structured, standardized datasets that reduce preprocessing overhead and make downstream use faster and more reliable.

4. API delivery into your workflow

Datasets are integrated directly into your existing ML and geospatial pipeline for training, validation, change detection, mapping, simulation, and digital twin systems.

Using the platform
Aerial view of a large industrial building with a parking lot filled with multiple green delivery trucks beside a river and green grassy area.
Aerial view of an urban area with a highlighted purple zone outlined in yellow, covering residential and commercial buildings, near Charleson Park and Alliance Wellness Clinic.

Imagery-powered AI workflows

High-resolution drone data that fuels the core computer vision tasks your AI models rely on.

Aerial view of a construction site with steel rebar, wooden planks, pipes, and concrete foundations in progress.Aerial view of a construction site with multiple reinforcement cages labeled in pink boxes.

Object detection

Use case: Street mapping, object classification

Aerial view of a residential area showing several long, narrow buildings with parked cars and surrounding green yards with trees and garden sheds.Aerial view of a residential area with five adjacent buildings having red roofs, green cars parked beside them, purple marked parking areas, and purple highlighted trees and bushes.

Segmentation

Use case: Roof damage detection, construction progress, land cover detection

Aerial view of a residential neighborhood with a house under construction surrounded by completed houses and autumn-colored trees.Aerial view of a residential neighborhood featuring houses, a circular cul-de-sac, trees, and green spaces.

Change detection

Use case: Post-disaster analysis, encroachment alerts

Aerial view of a city neighborhood with rows of residential buildings, trees, and a prominent church with a tall steeple in the center.Aerial view of a neighborhood with a central church surrounded by multi-story residential buildings and trees.

Digital twin creation

Use case: Infrastructure & urban modeling, autonomous navigation

Aerial view of a mostly empty parking lot near a large circular arena building.Aerial view of a modern residential complex with multiple mid-rise apartment buildings and a large arena in the background.

Synthetic data generation

Use case: AI model training (detection, mapping, risk assessment)

Get the real-world data your AI needs

Explore API Documentation