You know your city. You know what is missing on its map. We support you to fill the gaps using AI assisted mapping on drone imagery, with all results published as open data.
Selected projects receive between $3,000 and $5,000 in funding, scaled to proposal scope, plus full technical support from HOT. The AI side is on us. You define the feature, run the mapathon, and validate the output.
02
The pipeline
Step 01
Define the feature
Pick anything visible from drone imagery. Open spaces, wastebins, rooftops, solar panels, informal structures, street furniture, anything you can detect and want mapped.
Step 02
Feasibility study
Confirm enough samples exist (around 1000+ is a good target) and decide on imagery: fly your own drone or use existing drone imagery for the area.
Step 03
Run a Locate mapathon
Set up a Locate project on MapSwipe and mobilise volunteers to label your feature. We help you scope and run the session.
MapSwipe / Locate
Step 04
Use the model output
HOT supports you to train a fAIr model on your samples and shares the inference output. If results are weak, you can iterate with the team and collect more data.
Model training runs on HOT infrastructure
Step 05
Publish openly
Validate the model outputs and publish them as open mapping data. OpenStreetMap is the preferred destination. Where features need conflation (rooftops, for example), HDX or another public platform is accepted.
OSM preferred
03
What you get, what we expect
Offer
What HOT offers.
$3,000 to $5,000 in project funding, scaled to proposal scope. Covers mapathon costs, validation effort, and team time.
AI support. We help you train the object detection model with fAIr on your data and share the inference outputs.
Technical guidance on MapSwipe Locate setup, sample sizing, validation, and OSM contribution.
Possible drone or expert support.Subject to availability. We may be able to send a team member or connect you with mentors.
Expect
What we expect.
A clear feature target visible in drone imagery, with around 1000+ expected samples (smaller cases considered).
Imagery access. Fly your own drone (or seek support from HOT Team) or use existing drone imagery covering the project area.
A local team or community able to run a Locate mapathon and validate the results.
Willingness to iterate with the HOT team if the model needs more data to perform well.
Open data commitment. All outputs published openly. Mapping features go to OpenStreetMap where feasible, or to HDX or another public platform when conflation or other constraints apply.
04
Final deliverables
Deliverable 01
Completed MapSwipe project
The Locate project results from your mapathon, finalised and contributed publicly through MapSwipe.
Deliverable 02
Open mapping data
Validated features published as open data. OpenStreetMap is the preferred destination. Where conflation or other constraints make OSM contribution unfeasible (rooftops, for example), HDX or another open public platform is accepted.