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Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?
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GEOBIA Achievements and Spatial Opportunities in the Era of Big Earth Observation Data

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Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg 5020, Austria
2
University of Calgary, Department of Geography, Calgary, AB T2N 1N4, Canada
3
Spatial Services GmbH, Salzburg 5020, Austria
4
Italian Space Agency (ASI), Roma 00133, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 474; https://doi.org/10.3390/ijgi8110474
Received: 18 July 2019 / Revised: 11 October 2019 / Accepted: 21 October 2019 / Published: 24 October 2019
(This article belongs to the Special Issue GEOBIA in a Changing World)
The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena. View Full-Text
Keywords: geographic object-based image analysis (GEOBIA); computer vision; big data analytics; GIScience; spatial autocorrelation; geographic space geographic object-based image analysis (GEOBIA); computer vision; big data analytics; GIScience; spatial autocorrelation; geographic space
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Lang, S.; Hay, G.J.; Baraldi, A.; Tiede, D.; Blaschke, T. GEOBIA Achievements and Spatial Opportunities in the Era of Big Earth Observation Data. ISPRS Int. J. Geo-Inf. 2019, 8, 474.

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