License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.60
URN: urn:nbn:de:0030-drops-93881
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9388/
Go to the corresponding LIPIcs Volume Portal

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

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

pdf-format:
LIPIcs-GISCIENCE-2018-60.pdf (0.3 MB)

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.

BibTeX - Entry

@InProceedings{sudmanns_et_al:LIPIcs:2018:9388,
  author =	{Martin Sudmanns and Stefan Lang and Dirk Tiede and Christian Werner and Hannah Augustin and Andrea Baraldi},
  title =	{{Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{60:1--60:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9388},
  URN =		{urn:nbn:de:0030-drops-93881},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.60},
  annote =	{Keywords: Big Earth Data, Semantic Analysis, Data Cube}
}

Keywords: Big Earth Data, Semantic Analysis, Data Cube
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI