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Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper)

Authors Martin Sudmanns , Stefan Lang , Dirk Tiede , Christian Werner, Hannah Augustin, Andrea Baraldi



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Author Details

Martin Sudmanns
  • University of Salzburg, Department of Geoinformatics - Z_GIS, Schillerstraße 30, Salzburg, Austria
Stefan Lang
  • University of Salzburg, Department of Geoinformatics - Z_GIS, Schillerstraße 30, Salzburg, Austria
Dirk Tiede
  • University of Salzburg, Department of Geoinformatics - Z_GIS, Schillerstraße 30, Salzburg, Austria
Christian Werner
  • University of Salzburg, Department of Geoinformatics - Z_GIS, Schillerstraße 30, Salzburg, Austria
Hannah Augustin
  • University of Salzburg, Department of Geoinformatics - Z_GIS, Schillerstraße 30, Salzburg, Austria
Andrea Baraldi
  • Italian Space Agency (ASI), Rome, Italy.

Cite AsGet BibTex

Martin Sudmanns, Stefan Lang, Dirk Tiede, Christian Werner, Hannah Augustin, and Andrea Baraldi. Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 60:1-60:7, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.60

Abstract

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.

Subject Classification

ACM Subject Classification
  • Information systems → Search interfaces
Keywords
  • Big Earth Data
  • Semantic Analysis
  • Data Cube

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References

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