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Title
Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper)
AuthorSudmanns, Martin ; Lang, Stefan ; Werner, Dirk ; Augustin, Hannah ; Baraldi, Andrea
Published in
10th International Conference on Geographic Information Science (GIScience 2018) : Winter and Amy Griffin and Monika Sester, Stephan; Griffin, Amy; Sester, Monika, Dagstuhl, 2018
Published2018
LanguageEnglish
SeriesLeibniz International Proceedings in Informatics (LIPIcs) ; 114
Document typeArticle in a collected edition
Keywords (EN)Big Earth Data / Semantic Analysis / Data Cube
ISBN9783959770835
URNurn:nbn:at:at-ubs:3-10005 Persistent Identifier (URN)
DOI10.4230/LIPIcs.GISCIENCE.2018.60 
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 The work is publicly available
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Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper) [0.3 mb]
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Abstract (English)

Abstract data types are a helpful framework to formalise analyses and make them more transparent, reproducible and comprehensible. We are revisiting an approach based on the space, time and theme dimensions of remotely sensed data, and extending it with a more differentiated understanding of space-time representations. In contrast to existing approaches and implementations that consider only fixed spatial units (e.g. pixels), our approach allows investigations of the spatial units' spatio-temporal characteristics, such as the size and shape of their geometry, and their relationships. Five different abstract data types are identified to describe geographical phenomenon, either directly or in combination: coverage, time series, trajectory, composition and evolution.

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