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UID:pretalx-foss4g-2022-NAKXRM@talks.staging.osgeo.org
DTSTART;TZID=CET:20220825T101500
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DESCRIPTION:In recent years\, several Python packages (e.g. xarray\, raster
 io) have evolved around more basic software libraries such as netCDF4 or G
 DAL for accessing geospatial data. These packages allow to work with all k
 ind of data formats (e.g. GeoTIFF\, NetCDF\, ZARR) providing the data in a
 rray format (NumPy\, xarray) and constitute a fundamental part of any scie
 ntific analysis or operational task. However\, they do not offer full flex
 ibility when working with Earth Observation (EO) datasets. The multidimens
 ional complexity of EO data (i.e. space\, time\, bands) is often resolved 
 by distributing dimensions across many files and thus not always easy to a
 ccess. An important step forward to streamline EO data access has been the
  Open Data Cube (ODC) toolbox\, which utilizes predefined dataset configur
 ations and file-based indices stored in a database. With this setup\, ODC 
 enables an easy and uniform access to multidimensional geospatial datasets
 . Still\, users are often confronted with a great variety of data formats\
 , and files being distributed over different systems. This can pose a hurd
 le when working with ODC\, especially if one wants to process a new stack 
 of geospatial data\, where the extra overhead of a database can stall swif
 t progress.\n\nIn order to close this gap\, the yeoda (''your earth observ
 ation data access'') Python software package aims to resolve this shortcom
 ing by offering a similar interface as ODC\, but allowing to interact with
  geospatial data on a lower level. It relies on two other Python software 
 packages developed by TU Wien: geospade (definition of geospatial properti
 es of a dataset\, e.g. geometries)\, and veranda (read/write access to a v
 ariety of raster and vector data formats\, e.g. GeoTIFF). This modular set
 up ensures a clear separation of concerns\, specifically between geospatia
 l operations and I/O tasks\, yielding a homogenized interface independent 
 from the actual data format. For example\, geospatial operations based on 
 tiled EO raster datasets can be easily performed across tile or file bound
 aries. Data access is then realised in veranda\, which combines geometric 
 properties with I/O objects listed in a table. On top of geospade and vera
 nda\, yeoda acts as a communication layer between files stored on the file
  system and data objects by adding additional dimensions to the data table
 \, such as common metadata or file name entries. Thus\, one can filter mul
 tiple files by their attributes (e.g. time\, bands\, variable names\, sate
 llite platform) before accessing the data.  \n\nHence\, yeoda guarantees t
 he necessary freedom to apply arbitrary algorithms on manifold data format
 s\, while simultaneously supporting scalability by means of parallelised I
 /O operations. Despite ODC's tremendous value for accessing EO datasets th
 rough large scale operational services\, yeoda introduces a new level of d
 ata interaction making it an indispensable tool for the EO user community.
  When taking a look on recent advancements in interoperable cloud-based pr
 ocessing via the openEO API\, yeoda could be utilized as a slim back-end l
 ibrary to lower the hurdle of sharing new EO datasets and to foster scient
 ific exchange.
DTSTAMP:20260403T205715Z
LOCATION:Modulo 0
SUMMARY:yeoda - providing low-level and easy-to-use access to manifold eart
 h observation datasets - Claudio Navacchi
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/NAKXRM/
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