Satellite Object Detection: Detect objects in satellite imagery using text prompts

What it does

Satellite Object Detection is a browser-based tool for locating objects in satellite imagery using natural language prompts. Users draw a polygon on a map, enter a text description like "swimming pools" or "oil tanks", and the system scans the selected area to find matches. It works by splitting the region into tiles, running each through a vision-language model, and returning results as GeoJSON data overlaid on the map.

Who it is for

The tool targets professionals in logistics, urban monitoring, and geospatial analysis who need to identify specific assets from satellite data. It removes the need to write complex queries or train custom models—plain English prompts replace technical setup. The demo environment requires no account or credentials, making it accessible for exploration before commitment.

Pricing

The site does not show prices. A free demo version is available with limited map area. A paid full version is mentioned as accessible via a Dashboard, offering global mapping, multi-layer GeoJSON exports, and custom project management.

How it stands out

The product uses open-vocabulary vision-language models instead of pre-trained classifiers, meaning it can detect arbitrary objects described in text rather than only predefined categories. The browser-based interface and real-time tile processing let users see results instantly on a map. Geographic coordinate conversion (WGS84) and GeoJSON output integrate directly with mapping workflows. The demo operates without authentication, lowering friction for trial.

What a founder should check

First, verify the moat: vision-language models are rapidly improving and increasingly commoditized. Check whether the builder's main advantage comes from the interface design, the processing pipeline, data partnerships, or something else—and whether that advantage holds as VLM APIs become cheaper. Second, understand switching costs for the target user base. Professionals using similar tools may have workflows built on other platforms; confirm what lock-in or data export benefits could increase retention. Third, test pricing pressure: satellite imagery analysis tools compete on cost per square kilometer scanned and ease of use. Compare the full platform pricing against alternatives like manual analysis, competing SaaS tools, or self-hosted VLM approaches to understand where this sits on the value curve.

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