文章基本信息

标题：Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper)
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作者：Martin Sudmanns ; Stefan Lang ; Dirk Tiede 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2018
卷号：114
页码：1-7
DOI：10.4230/LIPIcs.GISCIENCE.2018.60
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：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.
关键词：Big Earth Data; Semantic Analysis; Data Cube