Skip to content

Models

fAIr's models are published in an open catalog. A base model is a pretrained model that anyone can run. An advanced user can fine-tune a base model on their own area to produce a local model, which is versioned and published back to the collection. All models and their metadata live in a STAC catalog.

The catalog of contributed models is at hotosm.github.io/fAIr-models, and models can be run at ai.hotosm.org.

Base models

The models available in fAIr today and those planned, with their current status. The catalog is the source of truth for each model's details.

# Model Feature Task type Contributor Status
1 Buildings (RAMP, DINO, YOLO) Buildings Segmentation HOT Available
2 Swimming pools Swimming pools Object detection HOT Available
3 Parking spaces Parking spaces Object detection HOT Available
4 Solid waste Solid waste Classification HeiGIT Available
5 Trees Trees Object detection Omdena In development
6 Seagrass Seagrass Segmentation Zindi In development
7 Highway segmentation Roads Segmentation Zindi In development
8 Road surface (paved / unpaved) Roads Classification HeiGIT In development
9 Road surface damage Roads Classification Community call In development
10 Water storage tanks Water tanks Object detection Community call In development
11 Shipping container detection Ports Object detection Community call In development
12 Solar panels Solar panels Object detection Omdena Planned
13 Bridges Bridges Object detection Omdena Planned
14 Land use and land cover Land cover Segmentation Omdena Planned
15 Building damage assessment Buildings Classification HOT Planned
16 Tent detection Tents Object detection HOT Planned
17 Water bodies Water Segmentation HOT Exploring
18 Roof type Buildings Classification HOT Exploring
19 River segmentation Rivers Segmentation HOT Exploring
20 Flood building damage Buildings Classification HOT Exploring

Buildings, swimming pools, parking spaces and solid waste are available today; the Buildings row covers RAMP, DINO and YOLO. Progress across the model pipeline is tracked on the Milestones page.

Open geo-AI challenges

Several models are being built through two open geo-AI challenges launching in 2026, which open model development to a wider community:

  • Omdena (around 50 participants): trees, solar panels, bridges, and land use and land cover.
  • Zindi (100+ participants expected): seagrass and highway segmentation, with a third model in planning.

Contributions also come from HeiGIT (solid waste, road surface) alongside HOT's own models.

Need a different feature? Ask the team in #fair-coord on Slack, or contribute a model (see below).

Contributing a model

New geo-AI models are contributed through a pull request to the fAIr-models catalog. After review and approval, a model is registered into fAIr and becomes available to every kind of user. The registration flow is described in Register a base model, and the architecture in ML pipeline.

What kind of models

The collection is made of base models: models pre-trained on a specific feature, such as buildings, roads, or flood damage, so they can be reused across locations. Contributions can be classical machine learning models (for example random forest or k-means applied to geodata) or models built on foundation models.

Models coupled to a specific task work best. For example, a DINOv3 trained for buildings, roads, or damage fits well and can be run on a mapper's area directly, which is the form the collection is built around.

Existing open-source geo-AI models can also be integrated through an open call; see Vision.

If you build geo-AI models and are interested in contributing, apply through the open call. fAIr is open to expansion in the following categories:

  • Residential areas
  • Critical public infrastructure
  • WASH infrastructure
  • Livelihood infrastructure
  • Transportation infrastructure
  • Essential utilities
  • Emergency and public service facilities

Other humanitarian features beyond this list are also welcome.