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Kartta Labs: Unrendering Historical Maps

Yao-Yi Chiang
Tim Waters
Feng Han
Kisalaya Prasad
Raimondas Kiveris
Proceedings of the 3nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. ACM, 2019. (2019)


This paper introduces the Kartta Labs, an ongoing open-source and open-data project aiming for organizing the world’s historical maps and making them universally accessible and useful. Kartta Labs’ framework is designed as a composition of multiple modules. Each module includes a crowdsourcing component and an intelligent, machine-assisted component to automate the process. The framework takes images of historical maps, registers them in space and time, generates a vector version of the map content, and allows the users to query for the vector content and recreate the historical maps in various cartographic styles. We refer to this process as unrendering. The resulting machine-readable map data support a variety of scientific studies and applications that require long-term, detailed geographic information in the past and open up opportunities in other areas such as entertainment. The paper also presents the preliminary results from one automated module to geolocalize a guven a historical map.