An AI-driven geolocation tool that identifies where photos were taken using visual analysis instead of metadata.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install geoinfer-image-geolocation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install geoinfer-image-geolocation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GeoInfer Image Geolocation?
GeoInfer Image Geolocation is a specialized tool for AI agents that enables precise location identification of images through deep visual analysis. By examining architecture, terrain, vegetation, and signage, it bypasses the need for GPS coordinates or EXIF metadata, making it a powerful addition to your collection of Openclaw Skills. This skill is particularly useful for workflows involving investigative journalism, security auditing, and digital forensics where metadata has been stripped or is unavailable.
Built for professional-grade analysis, the skill leverages advanced computer vision models to predict locations down to the city level. It transforms visual cues into actionable geographic data, allowing developers and researchers to verify the origin of any image file including JPEG, PNG, and WebP formats.
GeoInfer Image Geolocation Use Cases
- Conducting Open Source Intelligence (OSINT) investigations to verify the location of social media imagery.
- Performing digital forensics on images where metadata or GPS tags have been removed or altered.
- Enhancing investigative journalism by cross-referencing visual landmarks with geographic data.
- Automating security workflows that require the verification of physical site locations from captured photos.
How GeoInfer Image Geolocation Works
- The user or AI agent identifies a local image file and selects an appropriate model (e.g., Global, Property, or Car).
- The system processes the image using visual cues like architecture and vegetation rather than metadata.
- A request is sent to the GeoInfer API using the GEOINFER_API_KEY for authentication.
- The skill returns a JSON array of top location predictions, including precise coordinates and confidence scores for each candidate.
GeoInfer Image Geolocation Setup
To integrate this capability into your Openclaw Skills workflow, follow these steps:
- Obtain your API key from the GeoInfer API dashboard.
- Set your environment variable:
export GEOINFER_API_KEY="geo_your_key_here"
- Use the provided scripts to predict locations:
bash scripts/predict.sh /path/to/image.jpg global_v0_1 5
GeoInfer Image Geolocation Data Schema & Taxonomy
The skill manages data through structured JSON outputs and utilizes a credit-based system for different model types. Data organization follows this structure:
| Attribute | Description |
|---|---|
| Model ID | The specific engine used (global_v0_1, car, property, accuracy). |
| Top_N | The number of prediction results returned (range 1-15). |
| Coordinates | Latitude and longitude pairs for each predicted location. |
| Confidence | A score indicating the AI's certainty for each result. |
| Credit Balance | Current remaining API credits as returned by credits.sh. |
GeoInfer Image Geolocation Advanced Features
- Context-aware models such as Car or Property for specialized geolocation scenarios.
- Batch processing support for large-scale image analysis in investigation pipelines.
- Real-time credit monitoring to manage API consumption during automated tasks.
- Multi-prediction comparisons (top_n) to facilitate high-confidence confidence interval analysis.
SKILL.md
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