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A Semantic View on Planetary Mapping—Investigating Limitations and Knowledge Modeling through Contextualization and Composition.

Authors :
van Gasselt, Stephan
Nass, Andrea
Source :
Remote Sensing. Mar2023, Vol. 15 Issue 6, p1616. 22p.
Publication Year :
2023

Abstract

The concept of planetary mapping constitutes different activities within different contexts. Much like the field of cartography, it is an amalgamation of science, techniques, and artistic disciplines. It has undergone considerable changes over the last decades to cope with increasing demands related to data management, analysis, and visualization. Planetary mapping employs abstraction, which involves simplifications and generalizations. It aims to produce accessible visualization of planetary surfaces to gain insights and knowledge. Here, we show that different manifestations of this concept are interdependent and we discuss how different mapping concepts relate to each other semantically. We reason that knowledge gain can only be achieved through thematic mapping. The reasoning for systematic mapping and exploration is an intellectual product of thematic mapping. In order to highlight these relationships, we (a) develop in-depth definitions for different types of planetary mapping, (b) discuss data and knowledge flow across different mapping concepts, and (c) highlight systemic limitations related to data that we acquire and attempt to abstract through models. We finally develop a semantic proto-model that focuses on the transformation of information and knowledge between mapping domains. We furthermore argue that due to compositionality, map products suffer not only from abstraction but also from limitations related to uncertainties during data processing. We conclude that a complete database is needed for mapping in order to establish contextualization and extract knowledge. That knowledge is needed for reasoning for planning and operational decision making. This work furthermore aims to motivate future community-based discussions on functional semantic models and ontologies for the future development of knowledge extraction from thematic maps. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
6
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
162815048
Full Text :
https://doi.org/10.3390/rs15061616