Kartta Labs

Abstract

This paper introduces the Kartta Labs project, an ongoing opensource and open-data project aiming at 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 has a crowdsourcing implementation and an artificial intelligence based implementation. 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 can support a variety of scientific studies and applications that require long-term, detailed geographic information in the past, while opening up opportunities in other areas such as entertainment. This paper also presents the preliminary results from one automated module to geolocalize a given historical map.

An ontology‐based semantic description model of ubiquitous map images

“…However, the map interpretation is still a difficult task. In recent years, the focus of map interpretation has primarily been on georectification to historical maps (Tavakkol et al, 2019), as well as recognizing specific map features (Chiang et al, 2020) and their semantic information (Wang et al, 2023). Gui et al (2023) suggested that users' intention of map image retrieval can be analyzed from a three-tiered structure of "intention, sub-intention, dimension component", and constructed a map retrieval intention dimension tree.…”

“…However, the map interpretation is still a difficult task. In recent years, the focus of map interpretation has primarily been on georectification to historical maps (Tavakkol et al., 2019), as well as recognizing specific map features (Chiang et al., 2020) and their semantic information (Wang et al., 2023). Gui et al.…”

“…Consequently, they differ significantly in symbol design, color schemes, map layout, and other map elements. They impose great challenges for machine‐centered tasks such as semantic retrieval (Luft & Schiewe, 2021; Walter et al., 2013), content description and analysis (Degbelo, 2021a; Fish, 2020; Scheider et al., 2014), and information extraction (Tavakkol et al., 2019; Uhl et al., 2018). The main reason lies in the semantic gap between the simple physical form and the complex connotation of UMIs, which impedes their in‐depth applications.…”

“…Map archives (e.g., online historical map collection) have gained a lot of attention in recent years which has been proven to be an asset in historical study and societal applications. For example, Kartta Labs, a project led by Google, is an open sourced and open‐data project aiming at organizing the world's historical maps and making them universally accessible and useful (Tavakkol et al., 2019). Although the O‐SDM is not developed for a specific map archive, it is general enough to facilitate building tools to decode information from image‐based map archives.…”