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Releases: hotosm/fAIr

Early Dev Release fAIr 0.1.0 - Alpha

08 Apr 06:57
@kshitijrajsharma kshitijrajsharma
v0.1.0-alpha
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Early Dev Release fAIr 0.1.0 - Alpha Pre-release
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Release Notes for fAIr Dev Release 0.1.0 - Alpha

Introduction

Welcome to the dev alpha release of fAIr, an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) & Friends . This document provides an overview of the features included in this release, along with instructions for installing and using the software.

Features

The dev alpha release of fAIr includes the following features:

  • Project datasets creation: Users can create their own project datasets by uploading satellite and UAV imagery through OpenAerialMap. These datasets can be used for training models to detect buildings

  • Training labels creation: Users can create multiple training labels for their project datasets. These labels help the AI models learn to detect objects with greater accuracy.

  • Local model training: Users can train their own AI models using the project datasets and training labels they have created. The trained models can be used to make predictions

  • Prediction visualization: Users can view the predictions made by their trained models on imagery. This feature helps users evaluate the accuracy of their models and identify areas for improvement.

Installation

To install fAIr, follow these steps:

  1. Download the latest version of the software from the fAIr GitHub repository.

  2. Install the required dependencies, as listed and follow dev installation instruction

  3. Start Both Backend and Frontend server

Usage

To use fAIr, follow these steps:

  • Visit Learn Page of fAIr Frontend

Important Notes

  • This is a dev alpha release, which means that not all features are fully functional and some may be experimental.

  • The software may not work correctly in all circumstances and may break as we are still experimenting with it and most of it is still an idea.

  • Use this software at your own risk and do not use it for any mapping projects.

Conclusion

We hope that this dev alpha release of fAIr will be useful for those interested in exploring the potential of AI-assisted mapping for humanitarian purposes. We welcome feedback and suggestions on how to improve the software for future releases.

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