Metadata-Version: 2.4
Name: geomind-ai
Version: 1.1.1
Summary: AI agent for geospatial analysis with Sentinel-2 satellite imagery
Author: Harsh Shinde, Rajat Shinde
License-Expression: MIT
Project-URL: Homepage, https://harshshinde0.github.io/GeoMind
Project-URL: Repository, https://github.com/HarshShinde0/GeoMind
Project-URL: Documentation, https://github.com/HarshShinde0/GeoMind#readme
Project-URL: Issues, https://github.com/HarshShinde0/GeoMind/issues
Keywords: geospatial,satellite-imagery,sentinel-2,ai-agent,remote-sensing,earth-observation
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: openai>=1.0.0
Requires-Dist: pystac-client>=0.8.0
Requires-Dist: pystac>=1.10.0
Requires-Dist: xarray>=2024.1.0
Requires-Dist: zarr>=2.18.0
Requires-Dist: dask>=2024.1.0
Requires-Dist: geopy>=2.4.0
Requires-Dist: fsspec>=2024.1.0
Requires-Dist: aiohttp>=3.9.0
Requires-Dist: requests>=2.31.0
Requires-Dist: s3fs>=2024.1.0
Requires-Dist: matplotlib>=3.8.0
Requires-Dist: numpy>=1.26.0
Requires-Dist: python-dotenv>=1.0.0
Dynamic: license-file

###  Install Dependencies

```bash
pip install -r requirements.txt
```

###  Run the Agent

```bash
# Interactive mode
geomind

# Single query
geomind --query "Find recent imagery of Paris"

geomind --query "Create an RGB true-color composite visualization from the December 25th image"

geomind --query "Create an NDVI from the December 25th image"
```

## Example Queries

```
💬 "Create an RGB composite for the most recent image of London"

💬 "Calculate NDVI for Central Park, New York"

💬 "What images are available for Tokyo with less than 10% cloud cover?"
```

## Approach

### Traditional Approach
```
Full Scene Download → Local Storage → Process → Result
     ~720 MB            Disk I/O      Slow      
```

### GeoMind Approach (Zarr + fsspec)
```
HTTP Range Request → Stream Chunks → Process in Memory → Result
     ~1-5 MB           No disk          Fast           
```
