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UID:pretalx-foss4g-2026-YZ7CAF@talks.osgeo.org
DTSTART;TZID=JST:20260902T133000
DTEND;TZID=JST:20260902T140000
DESCRIPTION:The rapid growth of Earth observation (EO) data\, from satellit
 e missions such as the Copernicus Sentinel missions to Digital Twin Earth 
 frameworks like Destination Earth\, which integrate high-resolution atmosp
 heric and oceanic simulation models with the ever-growing satellite remote
  sensing datasets\, poses significant challenges for data storage\, intero
 perability\, and analysis at the global scale. Traditional gridding approa
 ches\, typically based on regular latitude/longitude grids or projected co
 ordinate systems such as UTM\, suffer from well-known limitations\, in par
 ticular non-uniform cell areas\, singularities at the poles\, and poor sca
 lability for multi-resolution analysis. There is a growing need for a comm
 on\, efficient\, and interoperable data representation that can accommodat
 e heterogeneous sources\, support large-scale analytics\, and integrate na
 turally with cloud-native storage paradigms.\n\nThe Grid4Earth project inv
 estigates how HEALPix (Hierarchical Equal Area isoLatitude Pixelization)\,
  combined with the Zarr storage format\, can serve as a unifying framework
  for Earth observation and Digital Twin data. Although HEALPix takes a fun
 damentally different route to spherical tessellation than the polyhedron-b
 ased constructions that dominate the classical DGGS literature\, it satisf
 ies the desirable DGGS geospatial core criteria: congruent refinement leve
 ls\, strictly equal-area cells\, and unique hierarchical indexing that ena
 bles efficient spatial queries and data indexing. Originating from the dem
 anding computational needs of cosmic microwave background analysis in astr
 ophysics\, HEALPix is now well integrated into the workflows of climate mo
 dellers and astrophysicists and is thus positioned as a prime candidate fo
 r terrestrial geospatial and remote sensing applications. Our approach ext
 ends HEALPix for geospatial applications by generalising the spherical tes
 sellation to an ellipsoidal HEALPix\, ensuring true equal-area properties 
 on the WGS84 ellipsoid. Unlike traditional geographic grids (e.g. Lat/Lon 
 grids\, UTM)\, HEALPix cells have identical areas regardless of latitude\,
  thus eliminating polar distortions and facilitating statistically unbiase
 d global analyses. Combined with the cloud-native\, chunked array data for
 mat Zarr\, HEALPix data can be efficiently stored\, accessed in parallel\,
  and integrated into modern Python data science workflows.\n\nWithin grid4
 earth\, we developed a complementary suite of four composable open-source 
 Python packages that address the full workflow from data ingestion to anal
 ysis: healpix-geo\, healpix-resample\, healpix-plot\, and healpix-analyse.
 \n\nhealpix-geo: Provides the foundational geospatial layer of the grid4ea
 rth ecosystem. While standard HEALPix implementations assume a perfect sph
 ere\, healpix-geo extends the tessellation to the WGS84 ellipsoid\, ensuri
 ng true equal-area properties for real-world Earth observation data. The l
 ibrary supports refinement levels from 0 to 29\, spanning global scale dow
 n to ~1.2 cm resolution\, and provides coordinate conversions\, spatial su
 bset selection through bounding box\, polygon\, and cone coverage queries\
 , and Multi-Order Coverage (MOC) via the zuniq scheme. All operations are 
 fully vectorised and support parallel processing\, enabling memory-efficie
 nt workflows at scale.\n\nhealpix-resample: Tackles the core data conversi
 on problem: regridding arbitrary Lat/Lon or UTM gridded data onto the elli
 psoidal HEALPix grid. It implements a forward modelling approach in which 
 source data points are projected onto their corresponding HEALPix target p
 ixels using precomputed index mappings. This design enables efficient batc
 h processing of large datasets\, including multi-temporal satellite image 
 stacks and model outputs. Flexible aggregation strategies (nearest neighbo
 ur\, averaging\, majority/mode) make it applicable to both continuous fiel
 ds (e.g.\, surface temperature\, reflectance) and categorical data (e.g.\,
  land cover).\n\nhealpix-plot: Provides visualisation capabilities tailore
 d to HEALPix data. Building on established Python visualisation libraries\
 , it offers functions for projecting HEALPix maps onto standard cartograph
 ic projections (Mollweide\, orthographic\, and regional)\, overlaying anci
 llary geographic information\, and producing publication-quality figures. 
 Particular attention has been paid to handling multi-resolution data and s
 upporting interactive exploration in Jupyter notebook environments.\n\nhea
 lpix-analyse: Completes the ecosystem with diagnostic and analytical tools
 \, including\, but not limited to computation of power spectra via spheric
 al harmonic decomposition\,  spatial convolution with custom kernels on th
 e sphere\, and standard statistical diagnostics (mean\, variance\, histogr
 ams\, spatial correlation). These operations benefit from the isolatitude 
 property of HEALPix and decades of optimisation in astrophysical data proc
 essing\, which makes spherical harmonic transforms in HEALPix exact and fa
 st. These capabilities are valuable for model evaluation\, intercomparison
  between EO products and Digital Twin outputs\, and characterisation of sp
 atial variability at global scale.\nThe HEALPix+Zarr approach builds on ex
 isting adoption. HEALPix is already being used in the Destination Earth's 
 Climate Digital Twin\, and Zarr underpins the EOPF format for Sentinel dat
 a re-engineering\, thus providing a validated foundation for the Grid4Eart
 h toolchain.\n\nAll four packages are released as open-source software und
 er permissive licences and are publicly available on GitHub\, PyPI\, and c
 onda-forge. The project follows open development practices\, with document
 ed APIs\, unit tests\, and example notebooks. Contributions from the commu
 nity are actively encouraged\, and the packages are designed to integrate 
 seamlessly with the broader Python geospatial ecosystem (Xarray\, NumPy\, 
 Matplotlib\, and PyProj). In parallel\, the Grid4Earth team is working tog
 ether with the OGC DGGS Standards Working Group to advance recognition of 
 ellipsoidal HEALPix as a conformant DGGS\, and with the CF Conventions com
 munity to establish standardised metadata conventions for HEALPix-indexed 
 data in netCDF and Zarr\, ensuring that the toolchain aligns with evolving
  community standards for interoperability.\n\nIn conclusion\, the Grid4Ear
 th Python suite provides a complete\, open-source toolchain for converting
 \, storing\, visualising\, and analysing Earth observation and Digital Twi
 n data in the ellipsoidal HEALPix+Zarr format. By combining geodetically a
 ccurate indexing\, efficient regridding\, rich visualisation\, and spheric
 al analytics\, the ecosystem lowers the barrier to adopting HEALPix as a c
 ommon representation for global-scale geospatial workflows. We believe tha
 t this approach has strong potential to improve interoperability across th
 e Earth system science community.
DTSTAMP:20260717T234858Z
LOCATION:Cosmos1
SUMMARY:Grid4Earth: An open-source Python ecosystem for geospatial data int
 egration on the ellipsoidal HEALPix DGGS - Alexander Kmoch\, Tina Odaka
URL:https://talks.osgeo.org/foss4g-2026/talk/YZ7CAF/
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