Geospatial Toolset Pro

Geospatial Toolset Pro turns your VS Code environment into a high-performance interactive geospatial development studio and Machine Learning workbench. Designed specifically for researchers and GIS developers, it bridges the gap between text-based geographic data formats, live mapping layers, and powerful background scikit-learn ML pipelines.

🚀 Core Features

1. Interactive Multi-Format Canvas & Map Rendering

• Instantly preview spatial file structures (GeoJSON, WKT, and KML) directly within an integrated webview map.
• Toggle fluidly between OpenStreetMap Standard View and Google High-Resolution Satellite Hybrid Imagery layers to analyze your study regions.

2. Live Text-to-Map Synchronization

• Keep your text editor and preview canvas open side-by-side.
• As you edit and save coordinate values or geometry properties in your JSON/WKT files, the map shifts and re-renders spatial alterations dynamically in real-time.

3. Native Spatial Drawing & Reverse Injection

• Create new geospatial data geometries directly on the basemap canvas using built-in interactive vector tools (Polygons, Rectangles, Points).
• Generate on-the-fly syntax snippets (such as Google Earth Engine API code blocks) containing your custom drawn coordinates and inject them directly back into your open editor cursor line.

4. Local LULC Machine Learning Workbench

• Train and deploy standard classification algorithms (Random Forest, Support Vector Machines (SVM), and Gradient Boosting / XGBoost Variants) straight from your workspace.
• Runs lightweight, background Python data processing engines to sample spectral features and predict classification boundaries.
• Renders highly dense, interactive thematic point grids (Water, Vegetation, Built-up) over the base map canvas for immediate validation.

🛠️ System Requirements & Setup

To use the local Machine Learning LULC engine, ensure you have a Python 3 environment active on your machine alongside the core data science libraries.

Run the following command in your terminal to install the underlying engine dependencies:

pip install scikit-learn rasterio geopandas numpy