GeoProcessor: Advanced Geospatial Data Processing Toolkit

GeoProcessor is a comprehensive Python library for geospatial data processing, analysis, and visualization. It provides a unified interface for working with raster datasets using industry-standard libraries like Rasterio, GDAL, xarray, NumPy, and Pandas, with cloud integration via AWS.

Features

• Raster Processing: Read, write, reproject, clip, and transform raster data
• Multi-dimensional Analysis: Leverage xarray for labeled multi-dimensional analysis
• Cloud Integration: Seamless AWS S3 support for cloud-based workflows
• Visualization: Tools for map creation and data plotting
• PostGIS Integration: Connect to and query spatial databases
• Performance Optimized: Efficient processing of large geospatial datasets
• Well-documented API: Comprehensive documentation with examples

Installation

# Clone the repository
git clone https://github.com/yourusername/geoprocessor.git
cd geoprocessor

# Install dependencies
pip install -r requirements.txt

# For development
pip install -r requirements-dev.txt

Quick Start

import rasterio
import numpy as np
from geoprocessor.raster.processing import calculate_ndvi, to_xarray

# Calculate NDVI from red and near-infrared bands
ndvi = calculate_ndvi('red_band.tif', 'nir_band.tif', 'ndvi_result.tif')

# Convert a raster to an xarray Dataset for advanced analysis
ds = to_xarray('elevation.tif')
print(f"Mean elevation: {ds.data.mean().values}")

# Process data directly from AWS S3
from geoprocessor.aws.s3 import read_raster_from_s3
data, metadata = read_raster_from_s3('my-bucket', 'path/to/raster.tif')

Documentation

Comprehensive documentation is available in the docs/ directory:

• User Guide: Detailed instructions and concepts
• API Reference: Complete API documentation
• Examples: Example workflows and use cases

Jupyter Notebooks

Interactive examples and tutorials are available as Jupyter notebooks in the notebooks/ directory:

• Tutorials: Step-by-step introduction to basic concepts
• Examples: Real-world usage examples
• Utilities: Helper notebooks for data inspection and benchmarking

Running Notebooks in Google Colab

All notebooks are compatible with Google Colab. You can access Rasterio sample datasets directly in Colab using:

# Install dependencies
!pip install rasterio xarray numpy matplotlib geopandas

# Download sample data
!wget https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif -O sample.tif

Hardware Workflow Integration

GeoProcessor is designed for integration with hardware-based geospatial workflows:

• Field data collection systems
• Drone and satellite imagery processing pipelines
• Real-time monitoring systems
• Edge computing devices for onsite processing

Tech Stack

• Python: Core language for all operations
• Rasterio / GDAL: Handling and processing raster datasets
• xarray / NumPy / Pandas: Data manipulation and analysis
• AWS: Cloud-based data storage and processing
• PostGIS: Spatial data management (optional)

Project Structure

The repository is organized as follows:

geoprocessor/                   # Main package directory
├── core/                       # Core functionality
├── raster/                     # Raster-specific modules
├── vector/                     # Vector-specific modules
├── aws/                        # AWS integration
├── postgis/                    # PostGIS integration
└── visualization/              # Visualization tools

notebooks/                      # Jupyter notebooks
├── tutorials/                  # Step-by-step tutorials
├── examples/                   # Example applications
└── utilities/                  # Utility notebooks

data/                           # Sample data
tests/                          # Test suite
docs/                           # Documentation

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The Rasterio and GDAL development teams
• The xarray and NumPy communities
• Contributors to the geospatial Python ecosystem