<!-- markdownlint-disable --> <p align="center"> <!-- github-banner-start --> <img src="https://raw.githubusercontent.com/hotosm/fair/develop/docs/assets/hot_logo.png" alt="HOTOSM Logo" width="25%" height="auto" /> <!-- github-banner-end --> </p> <div align="center"> <h1>fAIr: AI-assisted mapping</h1> <a href="https://github.com/hotosm/fair/releases"> <img src="https://img.shields.io/github/v/release/hotosm/fair?logo=github" alt="Release Version" /> </a> </div> </br> <!-- prettier-ignore-start --> <div align="center">

| CI/CD | | | | :--- | :--- | :--- | | Tech Stack | | | | Code Style | | | | Community | | | | Submodules | | | | Other Info | | |

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fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) that aims to improve the efficiency and accuracy of mapping efforts for humanitarian and development purposes. The service uses AI models, specifically computer vision techniques, to detect objects such as buildings, roads, waterways, and trees from satellite and UAV imagery.

The name fAIr is derived from the following terms:

• f: for freedom and free and open-source software
• AI: for Artificial Intelligence
• r: for resilience and our responsibility for our communities and the role we play within humanitarian mapping

Features

For mappers/project managers leading mapping projects

• Map object detection using open-source ML models, open imagery and open community-led training and validation data
• Use of open-source satellite and UAV imagery shared on HOT's OpenAerialMap (OAM) to detect map features
• Trainimg of base ML models using localised datasets for the creation of localised AI models that better fit the local context
• Constant feedback loop to eliminate model biases and ensure models are relevant to local communities

For model developers

• Sharing open ML models and making them available to the open mapping community

Unlike other GeoAI data platforms, fAIr is a free and open-source AI service that allows open mapping community members to access open AI models shared by model developer on the platform, and create and train their own AI models for mapping in their region of interest and/or humanitarian need. The goal of fAIr is to provide access to AI-assisted mapping across mobile and in-browser editors, using open ML models to ensure that the models are relevant to the places where communities aim to generate map data.

To eliminate model biases, fAIr is built to work with the local communities and receive constant feedback on the models, which will result in the progressive intelligence of computer vision models. Whenever an mapper uses the AI models for assisted mapping, fAIr can take those corrections as feedback to enhance the AI model’s accuracy.

The ML models suggest detected features to be added to OpenStreetMap (OSM) or other open datalayers.

Product Roadmap (Users' Roadmap) 2026

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| Status | Feature | Detailed Description | Release | |:--:| :-- | :-- | :-- | |🔄| AI Community Contribution | AI Developer can contribute their GeoAI models to fAIr, estiamted Jun-Jul 2026| v## |📅| Start Mapping | End users can select any GeoAI community model and any imagery to map specific features supported by the community models, e.g. solap panels, different rooftops martial...etc. estiamed Jun-Jul 2026 | v## |📅| Integrate Tasking Manager | Users can select specific TM project (with licensed imagery) and select validated taks to be used in fAIr to create building detection GeoAI models, estimated Jul-Aug 2026 | v## |📅| fAIrSwipe integration to create training dataset | Model creators would be able to seek community support of MapSwipe to create training data using MapSwipe projects, estiamted May 206 | v## |📅| fAIrSwipe integration to AI predcitions | users would be able to seek community support to validate the AI prediction producing a human validated data for different features such as solar panels, seagrass, road ...etc., estiamted Aug-Sept 206 | v## |📅| Private/Public Datasets/Models | Users can create provate and public datasets/model, estimated Sept 2026| v## |📅| Public Predcitions Request List | Users can publish publically their prediction request relsults dataset in a public list, estimated Aug 2026 | v## |📅| Clone model | Users can select a GeoAI model and replicated with its dataset to be able to develop them further, estiamted Oct 2026 | v## |📅| Community discussion | Other fAIr users can comment and give feedback on public models, estiamed Nov 2026 | v##

Product Roadmap (Users' Roadmap) 2025

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| Status | Feature | Detailed Description | Release | |:--:| :-- | :-- | :-- | |✅| Adopting YOLOv8 model | Improvements to the prediction algorithm | v2.0.1+ |✅| New UI/UX | redesign to enhance the user experience | v2.0.10+ |✅| fAIr evaluation | detailed research with Masaryk University & Missing Maps Czechia and Slovakia, welcome to join the efforts, here is the final report | no release |✅| Handling User Profile | Enable users to log in easily and have insights in their user activity, their own models/datasets and submitted trainings | v2.1.0 |✅| Notifications features | Training status change would trigger a notification on the web/email to let user know training is finished successfully or with a failure | v2.1.3 |✅| Replicable Models | Enable users to run a pre-trained model on new imagery/on a different area of their choice and using different satellite imagery | v2.2.0 |✅| Offline AI Prediction | Enable users to submit requests for prediction using any pre-trained model and any imagery and process it in the background and provide the results back to user. | v2.2.3 |✅| Post Processing Enhancement | Users would get enhanced geometry features (points/polygons) based on the need of the mapping process | v2.2.4 |✅| fAIrSwipe | Enable users to validate fAIR generated features and push them into OSM by integrating fAIr with MapSwipe, more details | v2.2.15

|👀| You can follow here the details and scope of each of the above features. and you can see and follow the Figma design progress for current in development 🔄 features

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A higher level roadmap for 2025 can be found on Github.

General Workflow of fAIr

1. First We expect there should be a fully mapped and validated task in project Area where model will be trained on
2. fAIr uses OSM features as labels which are fetched from [Raw Data API] (https://github.com/hotosm/raw-data-api) and Tiles from OpenAerialMap (https://map.openaerialmap.org/)
3. Once data is ready fAIr supports creation of local model with the input area provided , Publishes model for that area which can be implemented on the rest of the similar area
4. Feedback is important aspect , If mappers is not satisfied with the prediction that fAIr is making they can submit their feedback and community manager can apply feedback to model so that model