How computer vision meets human expertise
After converting the output of the model into a format that mapping volunteers can use, the AI system’s predictions then serve as the basis for the mapping process. The AI does most of the work for the person creating the maps, so she or he only needs to fill in any gaps, double-check the accuracy, and select the appropriate road type. An extensive set of validation tools has been built into RapiD to help users catch and fix data issues in real time, thus improving the quality of the final submissions to OSM.
“The tool strikes a good balance between suggesting machine-generated features and manual mapping,” says Martijn van Exel, a longtime leader in the open mapping community. “It gives mappers the final say in what ends up in the map but helps just enough to both be useful and draw attention to undermapped places.”
The result has been a better way for people with mapping expertise to do their work.
“Automatic data validation checks [allow] me easily to spot necessary changes,” says Dennis Irorere, a research fellow at YouthMappers in Nigeria. “This has gone a long way to not only enable me to map faster but also improve the quality of road map data that I submit after mapping.”
Rendering a suggested road is very quick, he notes, which makes the Map With AI tool easier to use in areas with limited connectivity.
“In my own case, the tool has helped in such a way that the majority of the roads in a selected task are mapped and are mapped faster,” says Samuel Aiyeoribe, associate manager, Technical GIS, at eHealth Africa. “Once the AI predicts a road feature, this can be quickly and easily tagged into appropriate road features accordingly.”
