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UID:pretalx-foss4g-2022-3MQR9R@talks.osgeo.org
DTSTART;TZID=CET:20220824T113000
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DESCRIPTION:Large geospatial data sets generated by modern remote sensing a
 nd environmental modelling provide new opportunities for analysts\, scient
 ists\, and researchers. However\, the size of these data sets can present 
 challenges due to the computation and resource management required for ana
 lytics and processing. Current solutions for processing such data sets lar
 gely focus on horizontal scaling approaches on\, for example\, distributed
  systems such as the Cloud\, without fully exploiting the opportunities of
 fered by modern computing architecture. Furthermore\, the variety of forma
 ts and types of geospatial data often result in complex processing workflo
 ws composed of multiple tools for reading and writing\, transformation\, p
 rocessing\, and resource management. We present an introduction\, overview
 \, and demonstration of the open-source Geostack framework (gitlab.com/geo
 stack/library). This has been developed to help simplify many common opera
 tions\, provide economy of code and to transparently take advantage of mod
 ern CPU/GPU hardware. We have aimed to provide three main routes to simpli
 fy and accelerate geospatial processing. These are: 1) a unified interface
  to read vector and raster data and interoperate between them\, with no so
 ftware dependencies for common geospatial data formats\, 2) treatment of a
 ll data as objects independent of geospatial transforms\, with transparent
  resource management through an underlying tile-based caching system\, rep
 rojection and interpolation carried out where needed\, 3) extensive use of
  OpenCL to provide computational acceleration and automatic processing vec
 torisation on GPUs and multi-core CPUs as well as user-defined scripts to 
 be executed over these objects. The framework also includes many common ge
 ospatial operations as well as several base geospatial solvers (including 
 moving fronts\, flow networks\, particle modelling) accelerated using Open
 CL. Geostack is a C++ API with Python bindings\, the code examples and dem
 onstrations are presented in Python. The Python bindings are available thr
 ough conda and fully interoperable with common Python libraries including 
 numpy\, gdal\, xarray\, netcdf\, geopandas and sqlite\, allowing users to 
 use as much or as little of the Geostack functionality as required. We pre
 sent demonstrations of several common geospatial tasks with benchmark comp
 arisons to alternate workflows.
DTSTAMP:20260722T131442Z
LOCATION:General online
SUMMARY:Geostack: a high performance geospatial processing\, modelling and 
 analysis framework - James Hilton\, Nikhil Garg
URL:https://talks.osgeo.org/foss4g-2022/talk/3MQR9R/
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