Turning Aerial Imagery Into a Searchable World

Vexcel isn't a household name — but you've almost certainly used their data. This aerial imaging company flies low-elevation aircraft across roughly 45 countries, capturing imagery at 7.5cm resolution from five different angles (straight down plus four oblique views), building one of the richest geospatial datasets on Earth.

In this episode, Daniel talks with Steve Lombardi, VP of Product at Vexcel, about what happens after the pixels are captured. They dig into object detection and "elements" (pre-extracted features like roof condition, solar panels, and pools), then go deep on Vexcel's newest capability: vector embeddings — essentially a searchable fingerprint for every 100-meter chunk of the planet.

Steve explains how customers can search the visible world with a text phrase, an uploaded image, or by simply drawing a box on the map — and get back matching locations anywhere on Earth. They cover how oblique imagery adds context to searches (like finding buildings that "look like a palace"), how customers refine results with a simple thumbs up/down feedback loop, and a fascinating new use case: exposing embeddings as a QGIS tile layer so you can build a personalized, concept-driven heat map — like a custom risk map — without ever touching a database.

Topics covered:

• What makes Vexcel's aerial imagery different from satellite imagery
• How photogrammetric data enables precise 3D measurement and object detection
• Object detection vs. vector embeddings — when to use which
• Custom elements: letting customers define their own objects to detect
• Searching aerial imagery by text, image, or drawn area
• Refining search results with a lightweight classifier ("thumb up / thumb down")
• Change detection over time (the Austin, Texas example)
• Bringing embeddings into QGIS as a personalized, concept-based tile layer
• Where aerial imagery and geospatial AI are headed next