The purpose of this study is to understand the differences between machine learning assisted and traditional digitizing of map features in OpenStreetMap.

After reading this statement, please indicate your consent for us to use your information to contact you to participate in Teh experiment. If you are under 18 years of age, you are not able to give individual consent and are therefore not eligible to participate in this study.

The experiment is designed to compare the results of traditional remote mapping workflows (editing in ID Editor) with emerging AI assisted workflows (editing with RapID). To do this, we will be conducting mapping experiments of two locations (Uganda and US), with two different levels of mappers (beginner and advanced) using the two different remote mapping workflows (RapID and ID). A more in-depth explanation of the experiment can be found here >>. https://docs.google.com/document/d/1o8vmHMCPbu6O-1Ta4a9Yd6aOhX76xRxocxfVCyXpxPU/edit?usp=sharing 

To this end, we are looking to run four mapathons with > 50 people each, as follows:
Beginner Mapathon - Uganda data
Beginner Mapathon - US data
Advanced Mapathon - Uganda data
Advanced Mapathon - US data

Each mapathon will be a total of two hours, with participants being randomly assigned to map building footprints with either ID or RapID prior to beginning their mapping. Data will be gathered on the existence of map features, completeness of mapped features and similarity of map features, when compared with an OSM reference dataset.

For the beginner mapathons we will be using convenience sampling from our networks by generating a public call for participants. For the advanced mapping, we will be looking for support from some of our country offices who have advanced mapping teams, who are experienced with RapID.