Abstract
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.
Highlights
“Space matters . . . ” is the condensed opening statement of the European Space Policy [1], highlighting the strategic importance of space infrastructure, referred to as space capacity, with its three sub-systems: satellite-based (i) communication, (ii) navigation, and (iii) Earth observation (EO)
In the two decades since its initial development, geographic object-based image analysis (GEOBIA) has reached across many applications, and is a basis for transferring concepts and ideas
We have reviewed significant GEOBIA contributions to the EO and the wider AI community and summarized a number of technology development opportunities which if implemented, could synergistically support operational big EO data analysis
Summary
“Space matters . . . ” is the condensed opening statement of the European Space Policy [1], highlighting the strategic importance of space infrastructure, referred to as space capacity, with its three sub-systems: satellite-based (i) communication, (ii) navigation, and (iii) Earth observation (EO). When focusing on EO imaging systems, we suggest that ‘space matters’ refers to the importance of geographic space as an underlying principle of the phenomena observed and monitored by EO satellites and related remote sensing (RS) techniques By building on both aspects—space technology and spatial concepts—this article aims to place classic geographic object-based image analysis (GEOBIA) ideas within the viewpoint of big EO data. GIScience provides methods and strategies on how to move from numeric, sub-symbolic raster data to discrete spatial units with symbolic meaning in several scaled representations We suggest that both disciplines, CV and GIScience, need to respect the ultimate benchmark [2] and currently the only measure [3], namely human cognition, be it human/biological vision or the conceptual understanding [4] of our multidimensional world in simplified planimetric image representations.
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