Guest Editors
Dr. Tomaž Podobnikar, Faculty of Information Studies in Novo Mesto, SLOVENIA
Geospatial Information Expert – consultant, civil servant, Scientific Councillor, lecturer and supervisor (PhD, MSc, BSc), recently a Sustainable Development Officer with 25+ years of experience in the areas of geospatial, environmental, natural and social sciences/fields, Earth observation, hazard and risk management, project/programmes evaluation, etc. at 13 academic, governmental, NGO, IGO (UN) and consulting organizations in 9 countries. Simplifier and constructor of new algorithms. Able to build every aspect of digital, from the front end to the back end and everything in between. Career highlights include strong leadership (head of the dept., chief GIS). Fieldwork in ecological, archaeological, anthropological and geomorphological mapping, geodetic survey and humanitarian in resource-limited settings. Designed and delivered courses in GIS/RS, cartography, spatial data and information quality, and digital terrain modelling. Documented innovative methods and technical solutions: (1)spatial data integration/conflation with semantic enrichment to reduce the cost of digital elevation model (DEM) up to 25-times, realized for the National DEM and implemented into the EU and Google Earth models; (2)geomorphometry based index for feature detection and recognition, visualization and photography enhancement, used in the Esri World Topographic Map; (3)methodology to pre-process any geospatial data with special tools to make them ‘analytics ready’, realized for the ESCWA Spatial Data Infrastructure and will be realized with ESA. Awarded with over 25 competitive grants including Skolkovo Innovation Center and Fulbright.
Summary
The special issue addresses the complex area of data quality as part of data management in the field of geospatial information. It seeks to examine the approaches required to achieve data quality that meets user expectations. Exploring geospatial data quality encompasses a comprehensive investigation into the pivotal knowledge gaps within GIS, remote sensing, cartography, geodesy, and other allied geospatial domains.
The first line of inquiry revolves around the applicability of well-known error models to geospatial data. Do the traditional classifications and measures of random, systematic, and gross errors encompass all the critical facets of geospatial data uncertainty? Are better concepts needed to more robustly represent geospatial data quality? This question leads to a broader discussion of theory versus practice, how the concepts should be incorporated into the spatial ETL(Extract Transform Load) or spatial data infrastructure (SDI) procedures, or the metadata for the archived data in the geospatial context. The special issue also challenges spatial data quality standards, such as those outlined in ISO 19157.
There is also the question of whether the concepts of the data quality model and associated models for different geospatial domains, such as GIS vs. cartography, and static vs. dynamic geospatial data management are enough congruent. How does the concept of vector or raster data quality apply across different levels of detail, spatial and temporal scales, and how do we ensure the integration and harmonization of data across multiple sources? The pursuit of continuous spatial data quality improvement based on the statistical, visual, and empirical methods are critical components of this exploration.
The special issue welcomes original research articles and encourages perspectives and technical notes that contribute to the discourse on data quality in the geospatial domain.
Keywords
Geospatial; GIS; Cartography; Remote sensing; Digital elevation model; Vector data Raster data; Temporal data; Geospatial data quality; Quality data model; Uncertainty; Accuracy; Error; Statistical methods; Empirical methods; Visual methods; Spatial data standards; Metadata
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