Cartographic Metadata for Improving Accessibility and Facilitating Knowledge Extraction and Validation in Planetary Mapping Based on Remote-Sensing Observations

Abstract

The field of planetary mapping and cartography builds almost exclusively on remote-sensing data and can be defined by three distinct concepts: systematic imaging as performed through spacecraft surveying, reference mapping as performed through the compilation of reference maps, i.e., regional to global image and topographic maps, and thematic mapping, which aims at abstracting and contextualizing spatial information to generate complex thematic maps, such as geologic or geomorphologic maps. While thematic mapping represents the highest form of abstraction of information that is provided through systematic mapping, thematic mapping also provides scientific reasoning in support of systematic mapping and exploration through spatially contextualized knowledge. For the development of knowledge, it is paramount to manage and exploit the value of thematic maps as research products, and to design a reliable and transparent development process from the beginning of the mapping phase as there is almost no validation for thematic maps. A key element in accomplishing these objectives is well-designed structures and metadata which are maintained within spatial data infrastructures (SDI) and shared as a coordinated process in research data management through data models. In this contribution, we focus on the need to transfer planetary thematic maps into findable, accessible, interoperable, reusable (FAIR), as well as transparent research data assets to facilitate improved knowledge extraction and also to compensate for limitations caused by the lack of conventional validation options. We review the current status of planetary thematic mapping, and we discuss the principles and roles of mappers and publishers in the process of creating and stewarding digital planetary maps and associated data products. We then present and discuss a set of recommendations that are closely tied to the FAIR concepts in research data management to accomplish such tasks.