2026-07-25T14:48:15Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9388 2024-03-06T10:44:12Z ddc:004 open_access

Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper) Sudmanns, Martin Lang, Stefan Tiede, Dirk Werner, Christian Augustin, Hannah Baraldi, Andrea Big Earth Data Semantic Analysis Data Cube 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Martin Sudmanns and Stefan Lang and Dirk Tiede and Christian Werner and Hannah Augustin and Andrea Baraldi 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.60 urn:nbn:de:0030-drops-93881 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.60 eng https://creativecommons.org/licenses/by/3.0/legalcode