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Geometry and geospatial data on the web
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Anna Wagner, Mathias Bonduel, Jeroen Werbrouck, Kris McGlinn
GML literals are represented in XML, with different tags to represent the semantics of a geometry, e.g. a Polygon will consist of multiple tags, with metadata tags providing some string-based “description”, an identifier which can be global, or application domain based, to the interior and exterior of the polygon, represented as a “LinearRing” with an x and y position (pos) defined. All points belonging to a geometric object should also share the same coordinate reference system. GML supports the use of any Coordinate Reference System (CRS). While typical CAD tools use a Cartesian coordinate system, in the case of geospatial data, a CRS must take into account the curvature of the Earth. A typical geospatial CRS is the World Geodetic System 1984 (WGS 84 – EPSG:4326), which provides a good approximation at all locations on the Earth, as the most commonly used CRS for spatial data on the Web. In some use cases, more accuracy is required than is available with WGS 84, and so other coordinate systems are preferred. Each country generally has its own CRS, using different projections, e.g. the transverse Mercator map projection.
Conventional top-view LiDAR topographic data
Published in Vorawit Meesuk, Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling, 2017
In the consecutive LiDAR strips, when the correspondences between of two or more strips were required, they must be carefully defined in the same georeferencing system, e.g. world geodetic system 1984 (EPSG 4326: WGS 84). In this respect, the ground control points (GCPs) obtained from the stationary GPS station. By using these GCPs as geo-referencing locations, the overlapping LiDAR strips can then be re-adjusted precisely in the same georeferencing location. Therefore, the registered composition of different strips can finally be matched in the same scale, alignment, and geolocation (Bellocchio et al., 2013).
Reprojecting geographic data
Published in Robin Lovelace, Jakub Nowosad, Jannes Muenchow, Geocomputation with R, 2019
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
It is possible to use a EPSG code in a proj4string definition with ”+init=epsg:MY_NUMBER”. For example, one can use the ”+init=epsg:4326” definition to set CRS to WGS84 (EPSG code of 4326). The PROJ library automatically adds the rest of the parameters and converts them into ”+init=epsg:4326+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0”.
Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective
Published in International Journal of Digital Earth, 2020
Johannes H. Uhl, Hamidreza Zoraghein, Stefan Leyk, Deborah Balk, Christina Corbane, Vasileios Syrris, Aneta J. Florczyk
Agreement assessment is conducted by using pixel-based map comparison techniques. The reference data is converted into binary raster layers indicating the presence or absence of built-up areas (Figure 2(c)). The abstract class ‘built-up area’ applied to the GHSL (Pesaresi et al. 2013) in practice translates to any grid cell that overlaps with a built-up and roofed structure. In compliance with this definition, we converted the rasterized, integrated reference data (2 m spatial resolution) to a raster surface compatible to GHS_S1 (originally in WGS84 Web Mercator projection, EPSG:3857, reprojected to Albers equal area conic projection for the contiguous U.S., EPSG:102003 using nearest-neighbor resampling). This compatibility includes spatial compatibility (i.e. spatial resolution of approximately 20 m, identical grid cell position and orientation), temporal compatibility (i.e. using the same temporal reference year), and semantic compatibility (i.e. using the same definition of the abstract built-up land class). In the same way, the other GHSL datasets are reprojected from their native projections into EPSG:102003 and upsampled to the GHS_S1 resolution using nearest-neighbor resampling to ensure compatibility between all layers given the fine resolution of the GHS_S1 data (Figure 2(d–f)).
Spatial relationship of the saturated hydraulic conductivity and rock fragments on the soil surface in an Andean microwatershed
Published in ISH Journal of Hydraulic Engineering, 2023
Julian Leal Villamil, Aquiles E. Darghan Contreras, Deyanira Lobo Luján, Edgar A. Avila Pedraza
In this research, the original variable Ks were initially modeled, but in the series of models obtained, the residuals showed an approximately exponential behavior, so the natural logarithm operator was applied to the natural response to finally use the Ln(Ks). Once the model was adjusted, the predicted values were extracted and simultaneously mapped in the regions that generated the polygons, the observed and predicted values of Ks, and the respective magnitudes of the explanatory variables. All the results were obtained by using the ‘errorsarlm’ function of the R spatialreg library (Bivand et al. 2021) and they were mapped using the WGS84 projection system (EPSG 4326) in the program ArcGIS 10.8 (ESRI (Environmental Systems Research Institute) 2019).
Reconstruction of incomplete public transportation check-out records by heuristic approaching
Published in Cogent Engineering, 2023
You-Sun Shin, Jay Hoon Jung, YoungMin Kwon
We chose a quad-tree as the data structure of the stop map for fast target searching in the 2-Dimensional spaces. Original location data is encoded in WGS 84 (EPSG:4326) world geodetic system which is the reference coordinate system of the Global Positioning System (GPS). Coordinates are represented in longitude and latitude in this system, but they require a complex formula as below, Haversine, to measure distances between two points because locations are mapped on an Earth ellipsoid (Wikipedia 20 October 2021).