Hi, I’ve been working on a Satellite Analysis Portal that leverages Open-Vocabulary models (VLM) for zero-shot object detection. It bridges the gap between raw pixels and actionable vector data using natural language prompts.

Technical Architecture/Capabilities:

* Zero-Shot Inference: No retraining new model for every project. Describe the feature (e.g., "Circular storage tanks", "Irrigation pivots") and the model performs inference.
* GIS-Lite Workspace: Built for analysts who need speed. Draw AOIs (polygons/rectangles), upload your own GeoJSON/KML/Shapefile boundaries, and manage analysis layers in-browser.
* Spatial Statistics: Automatically generates Density Heatmaps and calculates metrics like Spatial Entropy for detected clusters.
* Exports: Full support for GeoJSON and CSV to pipe into your existing QGIS/ArcGIS workflows.
* API: REST interface for high-throughput batch processing.

Demo & Workspace: I’ve put together a functional demo that allows for real-time inference on high-res imagery:   [Analysis Workspace Demo](https://www.useful-ai-tools.com/tools/satellite-analysis-demo/)

I’m working on adding various satellite options and enabling comparisons of the same location over time.  
I’m looking for feedback Looking for Feedback:

1. does it seem useful?
2. Is the detection quality good? 
3. What features might I be missing?

I built a Satellite Analysis tool with Open-Vocabulary AI that Detects anything on Earth by just describing it (Interactive Demo).