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Integrating STARE with relational databases

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<p>When working with ungridded remote sensing data, such as swath surface reflectance like Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09 or Visible Infrared Imaging Radiometer Suite (VIIRS) VNP09, extracting targeted information of interest from a collection of granules can be a challenging exercise. Given a region of interest (ROI), it is tedious both to determine the subset of granules that intersect the ROI, as well as identifying, within the granules, the individual instantaneous field of views (IFOVs) contained by the ROI.</p><p>The SpatioTemporal Adaptive-Resolution Encoding (STARE) is an indexing scheme that recursively divides the Earth's surface into quadtree hierarchies, allowing triangular elements ("trixels") of varying sizes (resolutions) to be identified with unique index values. STARE is also a software library that operates on STARE indices. It can efficiently determine the spatial relationship between two trixels, by evaluating their index values, if the trixels share a common path in the STARE tree structure. By representing geographical regions as the sets of trixels with adaptive resolutions that tesselating them, STARE provides an elegant method to determine geospatial coincidence of arbitrarily shaped geographic regions, with accuracy up to ~7-8 cm in length. </p><p>In this presentation, we introduce <em>STARELite</em>, a SQLite STARE extension and its use for cataloguing volumes of remote sensing granules that researchers often possess in their local storage. In this application, STARELite is used to determine subsets of granules intersecting arbitrary ROIs. Further, STARELite can be used for the inverse search problem: Determining all spatially coincident granules of an individual granule. STARELite leverages other components of the STARE ecosystem; namely <em>STARE sidecars</em>, which hold the trixel index values of each iFOV and a set of trixels representing the cover of each granule; <em>STAREMaster</em>, which is used to generate STARE sidecar files; and <em>STARPandas</em>, a Python Pandas extension used to bootstrap STARELite databases.</p><p>Given the limitations of SQLite, STARELite is to be understood as a proof of concept for the integration of STARE into relational databases in general. </p>
Title: Integrating STARE with relational databases
Description:
<p>When working with ungridded remote sensing data, such as swath surface reflectance like Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09 or Visible Infrared Imaging Radiometer Suite (VIIRS) VNP09, extracting targeted information of interest from a collection of granules can be a challenging exercise.
Given a region of interest (ROI), it is tedious both to determine the subset of granules that intersect the ROI, as well as identifying, within the granules, the individual instantaneous field of views (IFOVs) contained by the ROI.
</p><p>The SpatioTemporal Adaptive-Resolution Encoding (STARE) is an indexing scheme that recursively divides the Earth's surface into quadtree hierarchies, allowing triangular elements ("trixels") of varying sizes (resolutions) to be identified with unique index values.
STARE is also a software library that operates on STARE indices.
It can efficiently determine the spatial relationship between two trixels, by evaluating their index values, if the trixels share a common path in the STARE tree structure.
By representing geographical regions as the sets of trixels with adaptive resolutions that tesselating them, STARE provides an elegant method to determine geospatial coincidence of arbitrarily shaped geographic regions, with accuracy up to ~7-8 cm in length.
 </p><p>In this presentation, we introduce <em>STARELite</em>, a SQLite STARE extension and its use for cataloguing volumes of remote sensing granules that researchers often possess in their local storage.
In this application, STARELite is used to determine subsets of granules intersecting arbitrary ROIs.
Further, STARELite can be used for the inverse search problem: Determining all spatially coincident granules of an individual granule.
STARELite leverages other components of the STARE ecosystem; namely <em>STARE sidecars</em>, which hold the trixel index values of each iFOV and a set of trixels representing the cover of each granule; <em>STAREMaster</em>, which is used to generate STARE sidecar files; and <em>STARPandas</em>, a Python Pandas extension used to bootstrap STARELite databases.
</p><p>Given the limitations of SQLite, STARELite is to be understood as a proof of concept for the integration of STARE into relational databases in general.
 </p>.

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