HALOS: Mapping Linear Features on Modern Geospatial Reference Frameworks

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This proposal is to design and develop sophisticated methods for mapping massive linear features on tile maps and discrete global grids. The representation of geographic features (e.g., roads, rivers), outlines of areas (e.g., municipal boundaries, lake banks) or moving objects trajectories (e.g., of humans, vehicles) on paper or digital maps is commonly accomplished using polyline geometries. These geometries usually comprise a massive number of vertices. To facilitate the processing, analysis, or mapping of these geometries at a small scale, the number of these vertices must be reduced. Traditional cartographic methods can be applied to assist the elimination of vertices. However, these methods are semi-automated and involve an intense human supervision with limited applicability in massive production. Recent developments in Geospatial Web and Digital Earth have introduced new frameworks to modelling and mapping voluminous geospatial data. Online map service providers, such as Google Maps or OpenStreetMap, deliver their content in a standardized tile format to meet the demand for high speed dissemination of voluminous data over the web, while Digital Earth has adopted the discrete global grid systems (DGGS) to understand the planet model by offering an analysis-ready-information-grid. The rapidly growing use of these reference frameworks (i.e., tile maps and DGGS) has urged the development of new approaches to an efficient, consistent, and compliant mapping of massive polyline geometries representing geographic features or trajectories. This research plan aims to address this need by building on previously acquired knowledge with multiple benefits to the field, society, the economy, and the environment. Specifically, this research aspires to introduce new approaches to the fundamental problem of line simplification (a.k.a. cartographic generalization or data reduction) in geospatial data handling. These approaches will offer a sophisticated modelling and visualization of massive linear geospatial features on modern geospatial reference frameworks. Industry and government map service providers like Google Maps, OpenStreetMap or Natural Resources Canada will be able to generate massive map products in a faster and more accurate mode. General end-users and data scientists will be able to visualize, process, and analyze geospatial features in a more accurate, interoperable, and consistent manner with a valuable impact in research and practice to diverse fields in Natural Resources and Engineering. The education of HQP in this emerging area is strategically important in preparing the leaders of tomorrow and in retaining Canada's leading position in Geomatics.