Adding New Models

Coming Soon - This guide will show you how to add new AI models to the Geobase AI library.

Overview

This guide will cover:

Understanding the model registry system
Creating new model implementations
Adding model configurations
Testing new models
Contributing models to the library

What You’ll Learn

Model Registry

How models are registered in src/registry.ts
Understanding the ModelConfig interface
Adding new task types

Model Implementation

Creating model classes that extend base models
Implementing required methods
Handling model initialization and inference

Configuration

Setting up model parameters
Configuring input/output types
Adding model metadata

Testing

Writing unit tests for new models
Integration testing with real data
Performance benchmarking

Current Status

This documentation is under development. For now, you can:

Explore existing models in src/models/ to understand the patterns
Check the registry in src/registry.ts to see how models are configured
Review the checklist in docs/checklist_model_docs.md for documentation requirements

Quick Reference

Model Files Structure

src/models/
├── base_model.ts              # Base model class
├── geoai_models.ts           # GeoAI specific models
├── object_detection.ts       # Object detection model
├── land_cover_classification.ts
├── building_footprint_segmentation.ts
└── [your-new-model].ts       # Your new model

Registry Configuration

{
  task: "your-model-name",
  library: "@geobase-js/geoai",
  description: "Your Model Description",
  ioConfig: {} as YourModelIOConfig,
  geobase_ai_pipeline: async (params, modelId, modelParams) => {
    return YourModel.getInstance(modelId, params, modelParams);
  }
}

Stay Tuned

This guide will be updated with complete documentation, examples, and best practices for adding new models to the Geobase AI library.

Need help now? Check out the Local Testing Guide to understand how to test your changes.

Local Testing Guide Map Providers