The amount of information collected about the Earth has become extremely large. With this information comes the demand for integration, processing, visualization and distribution of this data so that it can be leveraged to solve real-world problems. To address this issue, a carefully designed information structure is needed that stores all of the information about the Earth in a convenient format such that it can be easily used to solve a wide variety of problems. The idea which we explore is to create a Discrete Global Grid System (DGGS) using a Disdyakis Triacontahedron (DT) as the initial polyhedron. We have adapted a simple, closed-form, equal-area projection to reduce distortion and speed up queries. We have derived an efficient, closed-form inverse for this projection that can be used in important DGGS queries. The resulting construction is indexed using an atlas of connectivity maps. Using some simple modular arithmetic, we can then address point to cell, neighbourhood and hierarchical queries on the grid, allowing for these queries to be performed in constant time. We have evaluated the angular distortion created by our DGGS by comparing it to a traditional icosahedron DGGS using a similar projection. We demonstrate that our grid reduces angular distortion while allowing for real-time rendering of data across the globe.
Geospatial data analysis often requires the computing of a distance transform for a given vector feature. For instance, in wildfire management, it is helpful to find the distance of all points in an area from the wildfire’s boundary. Computing a distance transform on traditional Geographic Information Systems (GIS) is usually adopted from image processing methods, albeit prone to distortion resulting from flat maps. Discrete Global Grid Systems (DGGS) are relatively new low-distortion globe-based GIS that discretize the Earth into highly regular cells using multiresolution grids. In this paper, we introduce an efficient distance transform algorithm for DGGS. Our novel algorithm heavily exploits the hierarchy of a DGGS and its mathematical properties and applies to many different DGGSs. We evaluate our method by comparing its speed and distortion with the distance transform methods used in traditional GIS and general 3D meshes. We demonstrate that our method is efficient and has minimal distortion.
Analysis of aerial images provided by satellites enables continuous monitoring and is a central component of many applications, including precision farming. Nonetheless, this analysis is often impeded by the presence of clouds and cloud shadows, which obscure the underlying region of interest and introduce incorrect values that bias analysis. In this paper, we outline a method for cloud shadow detection, and demonstrate our method using Canadian farmland data obtained from the Sentinel-2 satellite. Our approach builds on other object-based cloud and cloud shadow detection methods that generate preliminary shadow candidate masks which are refined by matching clouds to their respective shadows. We improve on these components by using ray-casting and inverse texture mapping methods to quickly identify cloud shadows, allowing for the immediate removal of false positives during image processing. Leveraging our ray-casting-based approach, we further improve our results by implementing a probability analysis based on the cloud probability layer provided by the Sentinel-2 satellite to account for missed shadow pixels. An evaluation of our method using the average producer (82.82%) and user accuracy (75.55%) both show a marked improvement over the performance of other object-based methods. Methodologically, our work demonstrates how incorporating probability analysis as a post-processing step can improve the generation of shadow masks.
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