Major update to JuliaGeo entry point#7
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alex-s-gardner wants to merge 5 commits intoJuliaGeo:masterfrom
Open
Major update to JuliaGeo entry point#7alex-s-gardner wants to merge 5 commits intoJuliaGeo:masterfrom
alex-s-gardner wants to merge 5 commits intoJuliaGeo:masterfrom
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juliohm
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May 28, 2025
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Removed all statements that favor specific packages because this is not a place for personal preferences.
Removed overstatements because they set incorrect expectations and lead to immediate frustration.
Moved packages hosted in individual accounts or third-party organizations to dedicated sections.
Please don't erase packages from the list again.
| currently provides basic functionality to plot all kinds of ways and buildings. | ||
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| ## JuliaGeo | ||
| The [JuliaGeo](https://github.com/JuliaGeo) GitHub organization serves as a focal point for developing and maintaining the next generation of tooling for geospatial analysis. It aims to leverage the intuitive syntax and high-performance of the [Julia language](https://julialang.org/) to provide robust, efficient, and easy-to-use tools for working with geographic data. |
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| The [JuliaGeo](https://github.com/JuliaGeo) GitHub organization serves as a focal point for developing and maintaining the next generation of tooling for geospatial analysis. It aims to leverage the intuitive syntax and high-performance of the [Julia language](https://julialang.org/) to provide robust, efficient, and easy-to-use tools for working with geographic data. | |
| The [JuliaGeo](https://github.com/JuliaGeo) GitHub organization serves as a focal point for developing and maintaining *interfaces* to geospatial file formats and established geospatial libraries written in other programming languages. This webpage lists various resources to help first-time Julia programmers interested in geospatial analysis. |
| The [JuliaGeo](https://github.com/JuliaGeo) GitHub organization serves as a focal point for developing and maintaining the next generation of tooling for geospatial analysis. It aims to leverage the intuitive syntax and high-performance of the [Julia language](https://julialang.org/) to provide robust, efficient, and easy-to-use tools for working with geographic data. | ||
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| ## Get involved | ||
| JuliaGeo fosters a collaborative environment for creating a comprehensive geospatial toolkit within the Julia ecosystem. Communication is mostly done on: |
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| JuliaGeo fosters a collaborative environment for creating a comprehensive geospatial toolkit within the Julia ecosystem. Communication is mostly done on: | |
| Communication is mostly done on: |
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| ## High-level packages | ||
| Most geospatial analysis can be accomplished with these three packages (in combination with extensions as needed): | ||
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| 1. [Rasters.jl](https://rafaqz.github.io/Rasters.jl/dev/) provides a powerful Julia framework for reading, writing, and manipulating rasterized spatial data, such as satellite imagery or climate model outputs. It offers a standardized interface to work with various data formats and in-memory arrays through Raster, RasterStack, and RasterSeries types, simplifying complex geospatial workflows. Rasters.jl provides [significant performance gains](https://github.com/user-attachments/assets/1c6c56ac-4c5a-4096-984d-15bf2783682c) over similar packages in other languages. | ||
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| 2. [GeoDataFrames.jl](https://www.evetion.nl/GeoDataFrames.jl/dev/) enables the handling of geospatial vector data in Julia by integrating geometric operations directly within DataFrame structures, inspired by Python's [GeoPandas](https://geopandas.org/en/stable/). It achieves this by treating a vector of geometries as a column, allowing for intuitive spatial data manipulation and analysis alongside tabular attributes. | ||
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| 3. [GeometryOps.jl](https://juliageo.org/GeometryOps.jl/dev/) provides a suite of highly efficent geometric operations for vector data (i.e. points, lines, polygons), designed to work seamlessly with any [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) compatible geometry. It aims to unify geometric calculations within the Julia ecosystem by offering pure Julia implementations of common spatial functions crucial for GIS and Earth data workflows. GeometryOps.jl provides [significant performance gains](https://github.com/JuliaGeo/GeometryOps.jl/assets/32143268/0be8672c-c90f-4e1d-81c5-8522317c5e29) over similar packages in other languages. | ||
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| ## Inter-package operability | ||
| [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) serves as a Julia protocol and interface for handling geospatial data. It provides a set of traits based on the Simple Features standard, enabling the parsing, serialization, and usage of various geometries within the Julia ecosystem. | ||
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| [GeoFormatTypes.jl](https://github.com/JuliaGeo/GeoFormatTypes.jl): Defines wrapper types to make it easy to pass and dispatch on geographic formats (like Well Known Text) between packages. | ||
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| [CommonDataModel.jl](https://github.com/JuliaGeo/CommonDataModel.jl): Defines a common data model for NetCDF, GRIB, Zarr, and GeoTIFF datasets. | ||
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| ## Visualization | ||
| See [Makie](https://docs.makie.org/stable/) and [Plots.jl](https://docs.juliaplots.org/stable/) for general purpose plotting. | ||
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| [Tyler.jl](https://github.com/MakieOrg/Tyler.jl) for displaying tiled maps interactively with [Makie](https://docs.makie.org/stable/). | ||
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| [GeoMakie.jl](https://github.com/MakieOrg/GeoMakie.jl) for geographic plotting utilities using [Makie](https://docs.makie.org/stable/). | ||
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| ## Lower-level file I/O - native Julia | ||
| [NCDatasets.jl](https://github.com/JuliaGeo/NCDatasets.jl): For working with NetCDF files, a common format for scientific data, including climate and oceanographic data. (Also NetCDF.jl exists). | ||
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| [GeoJSON.jl](https://github.com/JuliaGeo/GeoJSON.jl): Offers utilities for reading, writing, and manipulating GeoJSON data, a widely used open standard format for encoding geographic data structures. | ||
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| [Shapefile.jl](https://github.com/JuliaGeo/Shapefile.jl): Enables the reading and writing of ESRI Shapefiles, a common vector data format in GIS. | ||
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| [GeoParquet.jl](https://github.com/JuliaGeo/GeoParquet.jl): Facilitates working with geospatial data stored in Parquet files. | ||
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| [LazIO](https://github.com/evetion/LazIO.jl): Enables the reading and writing of Laz files. | ||
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| [STAC.jl](https://github.com/JuliaClimate/STAC.jl): SpatioTemporal Asset Catalogs (STAC) client in Julia | ||
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| [FlatGeobuf.jl](https://github.com/evetion/FlatGeobuf.jl): A native flatgeobuf implementation in Julia | ||
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| ## Lower-level packages - C bindings | ||
| [ArchGDAL.jl](https://yeesian.com/ArchGDAL.jl) A complete solution for working with GDAL in Julia. | ||
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| [GDAL.jl](https://github.com/JuliaGeo/GDAL.jl): A Julia wrapper for the powerful Geospatial Data Abstraction Library (GDAL), enabling the reading and writing of a vast array of raster and vector geospatial data formats. | ||
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| [LibGEOS.jl](https://github.com/JuliaGeo/LibGEOS.jl): A wrapper for the GEOS (Geometry Engine - Open Source) library, offering a wide range of geometry operations. | ||
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| [Proj.jl](https://github.com/JuliaGeo/Proj.jl): A Julia wrapper for the PROJ library, which is a standard for coordinate transformations and cartographic projections. (Note: Proj4.jl was an older version, with Proj.jl being the more current wrapper). | ||
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| ## Access to geospatial datasets | ||
| [MapTiles.jl](https://github.com/JuliaGeo/MapTiles.jl): For working with tiled web maps. | ||
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| [GADM.j](https://github.com/JuliaGeo/GADM.jl): Provides access to the GADM dataset of global administrative areas. | ||
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| [NaturalEarth.jl](https://github.com/JuliaGeo/NaturalEarth.jl) Interface to the Natural Earth dataset. | ||
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| [GeoDatasets.jl](https://github.com/JuliaGeo/GeoDatasets.jl) Access to common geographic datasets. | ||
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| [SpaceLiDAR.jl](https://github.com/evetion/SpaceLiDAR.jl) Utilities for accessing and working with satellite altimetry data (i.e. ICESat, ICESat-2, GEDI) | ||
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| ## Geospatial analysis packages | ||
| [Geomorphometry.jl](https://deltares.github.io/Geomorphometry.jl/v0.7.0/) Geospatial operations, cost and filtering algorithms as used for elevation rasters. | ||
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| [Geodesy.jl](https://github.com/JuliaGeo/Geodesy.jl): Provides tools for working with points defined in various coordinate systems (e.g., LLA, ECEF, ENU) and performing geodetic calculations. | ||
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| [SortTileRecursiveTree.jl](https://github.com/asinghvi17/SortTileRecursiveTree.jl) An Sort-Tile-Recursive (SRT) tree implementation for GeoInterface compatible geometries. No newline at end of file |
Member
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Suggested change
| ## High-level packages | |
| Most geospatial analysis can be accomplished with these three packages (in combination with extensions as needed): | |
| 1. [Rasters.jl](https://rafaqz.github.io/Rasters.jl/dev/) provides a powerful Julia framework for reading, writing, and manipulating rasterized spatial data, such as satellite imagery or climate model outputs. It offers a standardized interface to work with various data formats and in-memory arrays through Raster, RasterStack, and RasterSeries types, simplifying complex geospatial workflows. Rasters.jl provides [significant performance gains](https://github.com/user-attachments/assets/1c6c56ac-4c5a-4096-984d-15bf2783682c) over similar packages in other languages. | |
| 2. [GeoDataFrames.jl](https://www.evetion.nl/GeoDataFrames.jl/dev/) enables the handling of geospatial vector data in Julia by integrating geometric operations directly within DataFrame structures, inspired by Python's [GeoPandas](https://geopandas.org/en/stable/). It achieves this by treating a vector of geometries as a column, allowing for intuitive spatial data manipulation and analysis alongside tabular attributes. | |
| 3. [GeometryOps.jl](https://juliageo.org/GeometryOps.jl/dev/) provides a suite of highly efficent geometric operations for vector data (i.e. points, lines, polygons), designed to work seamlessly with any [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) compatible geometry. It aims to unify geometric calculations within the Julia ecosystem by offering pure Julia implementations of common spatial functions crucial for GIS and Earth data workflows. GeometryOps.jl provides [significant performance gains](https://github.com/JuliaGeo/GeometryOps.jl/assets/32143268/0be8672c-c90f-4e1d-81c5-8522317c5e29) over similar packages in other languages. | |
| ## Inter-package operability | |
| [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) serves as a Julia protocol and interface for handling geospatial data. It provides a set of traits based on the Simple Features standard, enabling the parsing, serialization, and usage of various geometries within the Julia ecosystem. | |
| [GeoFormatTypes.jl](https://github.com/JuliaGeo/GeoFormatTypes.jl): Defines wrapper types to make it easy to pass and dispatch on geographic formats (like Well Known Text) between packages. | |
| [CommonDataModel.jl](https://github.com/JuliaGeo/CommonDataModel.jl): Defines a common data model for NetCDF, GRIB, Zarr, and GeoTIFF datasets. | |
| ## Visualization | |
| See [Makie](https://docs.makie.org/stable/) and [Plots.jl](https://docs.juliaplots.org/stable/) for general purpose plotting. | |
| [Tyler.jl](https://github.com/MakieOrg/Tyler.jl) for displaying tiled maps interactively with [Makie](https://docs.makie.org/stable/). | |
| [GeoMakie.jl](https://github.com/MakieOrg/GeoMakie.jl) for geographic plotting utilities using [Makie](https://docs.makie.org/stable/). | |
| ## Lower-level file I/O - native Julia | |
| [NCDatasets.jl](https://github.com/JuliaGeo/NCDatasets.jl): For working with NetCDF files, a common format for scientific data, including climate and oceanographic data. (Also NetCDF.jl exists). | |
| [GeoJSON.jl](https://github.com/JuliaGeo/GeoJSON.jl): Offers utilities for reading, writing, and manipulating GeoJSON data, a widely used open standard format for encoding geographic data structures. | |
| [Shapefile.jl](https://github.com/JuliaGeo/Shapefile.jl): Enables the reading and writing of ESRI Shapefiles, a common vector data format in GIS. | |
| [GeoParquet.jl](https://github.com/JuliaGeo/GeoParquet.jl): Facilitates working with geospatial data stored in Parquet files. | |
| [LazIO](https://github.com/evetion/LazIO.jl): Enables the reading and writing of Laz files. | |
| [STAC.jl](https://github.com/JuliaClimate/STAC.jl): SpatioTemporal Asset Catalogs (STAC) client in Julia | |
| [FlatGeobuf.jl](https://github.com/evetion/FlatGeobuf.jl): A native flatgeobuf implementation in Julia | |
| ## Lower-level packages - C bindings | |
| [ArchGDAL.jl](https://yeesian.com/ArchGDAL.jl) A complete solution for working with GDAL in Julia. | |
| [GDAL.jl](https://github.com/JuliaGeo/GDAL.jl): A Julia wrapper for the powerful Geospatial Data Abstraction Library (GDAL), enabling the reading and writing of a vast array of raster and vector geospatial data formats. | |
| [LibGEOS.jl](https://github.com/JuliaGeo/LibGEOS.jl): A wrapper for the GEOS (Geometry Engine - Open Source) library, offering a wide range of geometry operations. | |
| [Proj.jl](https://github.com/JuliaGeo/Proj.jl): A Julia wrapper for the PROJ library, which is a standard for coordinate transformations and cartographic projections. (Note: Proj4.jl was an older version, with Proj.jl being the more current wrapper). | |
| ## Access to geospatial datasets | |
| [MapTiles.jl](https://github.com/JuliaGeo/MapTiles.jl): For working with tiled web maps. | |
| [GADM.j](https://github.com/JuliaGeo/GADM.jl): Provides access to the GADM dataset of global administrative areas. | |
| [NaturalEarth.jl](https://github.com/JuliaGeo/NaturalEarth.jl) Interface to the Natural Earth dataset. | |
| [GeoDatasets.jl](https://github.com/JuliaGeo/GeoDatasets.jl) Access to common geographic datasets. | |
| [SpaceLiDAR.jl](https://github.com/evetion/SpaceLiDAR.jl) Utilities for accessing and working with satellite altimetry data (i.e. ICESat, ICESat-2, GEDI) | |
| ## Geospatial analysis packages | |
| [Geomorphometry.jl](https://deltares.github.io/Geomorphometry.jl/v0.7.0/) Geospatial operations, cost and filtering algorithms as used for elevation rasters. | |
| [Geodesy.jl](https://github.com/JuliaGeo/Geodesy.jl): Provides tools for working with points defined in various coordinate systems (e.g., LLA, ECEF, ENU) and performing geodetic calculations. | |
| [SortTileRecursiveTree.jl](https://github.com/asinghvi17/SortTileRecursiveTree.jl) An Sort-Tile-Recursive (SRT) tree implementation for GeoInterface compatible geometries. | |
| ## Interface packages | |
| [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) serves as a Julia protocol and interface for handling geospatial data. It provides a set of traits based on the Simple Features standard, enabling the parsing, serialization, and usage of various geometries within the Julia ecosystem. | |
| [GeoFormatTypes.jl](https://github.com/JuliaGeo/GeoFormatTypes.jl): Defines wrapper types to make it easy to pass and dispatch on geographic formats (like Well Known Text) between packages. | |
| [CommonDataModel.jl](https://github.com/JuliaGeo/CommonDataModel.jl): Defines a common data model for NetCDF, GRIB, Zarr, and GeoTIFF datasets. | |
| ## IO packages | |
| [NCDatasets.jl](https://github.com/JuliaGeo/NCDatasets.jl): For working with NetCDF files, a common format for scientific data, including climate and oceanographic data. (Also NetCDF.jl exists). | |
| [GeoJSON.jl](https://github.com/JuliaGeo/GeoJSON.jl): Offers utilities for reading, writing, and manipulating GeoJSON data, a widely used open standard format for encoding geographic data structures. | |
| [Shapefile.jl](https://github.com/JuliaGeo/Shapefile.jl): Enables the reading and writing of ESRI Shapefiles, a common vector data format in GIS. | |
| [GeoParquet.jl](https://github.com/JuliaGeo/GeoParquet.jl): Facilitates working with geospatial data stored in Parquet files. | |
| ## Geometry packages | |
| [GeometryOps.jl](https://juliageo.org/GeometryOps.jl/dev/) provides a suite of highly efficent geometric operations for vector data (i.e. points, lines, polygons), designed to work seamlessly with any [GeoInterface.jl](https://juliageo.org/GeoInterface.jl/dev/) compatible geometry. It aims to unify geometric calculations within the Julia ecosystem by offering pure Julia implementations of common spatial functions crucial for GIS and Earth data workflows. GeometryOps.jl provides [significant performance gains](https://github.com/JuliaGeo/GeometryOps.jl/assets/32143268/0be8672c-c90f-4e1d-81c5-8522317c5e29) over similar packages in other languages. | |
| ## C bindings | |
| [GDAL.jl](https://github.com/JuliaGeo/GDAL.jl): A Julia wrapper for the powerful Geospatial Data Abstraction Library (GDAL), enabling the reading and writing of a vast array of raster and vector geospatial data formats. | |
| [LibGEOS.jl](https://github.com/JuliaGeo/LibGEOS.jl): A wrapper for the GEOS (Geometry Engine - Open Source) library, offering a wide range of geometry operations. | |
| [Proj.jl](https://github.com/JuliaGeo/Proj.jl): A Julia wrapper for the PROJ library, which is a standard for coordinate transformations and cartographic projections. (Note: Proj4.jl was an older version, with Proj.jl being the more current wrapper). | |
| ## Datasets | |
| [MapTiles.jl](https://github.com/JuliaGeo/MapTiles.jl): For working with tiled web maps. | |
| [GADM.j](https://github.com/JuliaGeo/GADM.jl): Provides access to the GADM dataset of global administrative areas. | |
| [NaturalEarth.jl](https://github.com/JuliaGeo/NaturalEarth.jl) Interface to the Natural Earth dataset. | |
| [GeoDatasets.jl](https://github.com/JuliaGeo/GeoDatasets.jl) Access to common geographic datasets. | |
| ## Inactive packages | |
| The following packages are not actively developed: | |
| [Geodesy.jl](https://github.com/JuliaGeo/Geodesy.jl): Provides tools for working with points defined in various coordinate systems (e.g., LLA, ECEF, ENU) and performing geodetic calculations. | |
| ## Third-party packages | |
| The [JuliaEarth](https://github.com/JuliaEarth) and [JuliaClimate](https://github.com/JuliaClimate) GitHub organizations develop and maintain alternative packages for geospatial work. Additional packages hosted in individual accounts are also listed in this section. | |
| ### JuliaEarth | |
| [GeoStats.jl](https://github.com/JuliaEarth/GeoStats.jl) is an extensible framework for geospatial data science and geostatistical modeling fully written in Julia. It is comprised of several modules for advanced geometric processing, state-of-the-art geostatistical algorithms and sophisticated visualization of geospatial data. | |
| [GeoIO.jl](https://github.com/JuliaEarth/GeoIO.jl) provides a high-level file-format-agnostic method to load and save geospatial data compatible with the GeoStats.jl framework. | |
| [GeoArtifacts.jl](https://github.com/JuliaEarth/GeoArtifacts.jl) collects geospatial artifacts (e.g. datasets) for geospatial data science. | |
| [CoordRefSystems.jl](https://github.com/JuliaEarth/CoordRefSystems.jl) provides unitful coordinate reference systems through a sophisticated set of type conversion rules. It supports various datums and EPSG/ESRI codes and is actively developed. | |
| ### JuliaClimate | |
| [ClimateTools.jl](https://github.com/JuliaClimate/ClimateTools.jl) is a collection of commonly-used tools in Climate science. Basics of climate field analysis are covered, with some forays into exploratory techniques associated with climate scenarios design. The package is aimed to ease the typical steps of analysis climate models outputs and gridded datasets (support for weather stations is a work-in-progress). | |
| [CDSAPI.jl](https://github.com/JuliaClimate/CDSAPI.jl) is a Julia API to the Climate Data Store (a.k.a. CDS). | |
| [INMET.jl](https://github.com/JuliaClimate/INMET.jl) is a Julia API to access data from the Instituto Nacional de Metereologia (INMET). | |
| [STAC.jl](https://github.com/JuliaClimate/STAC.jl): SpatioTemporal Asset Catalogs (STAC) client in Julia. | |
| ### Other third-party packages | |
| The packages below are hosted in individual GitHub accounts or in GitHub organizations of specific institutions: | |
| [Rasters.jl](https://rafaqz.github.io/Rasters.jl/dev/) provides a powerful Julia framework for reading, writing, and manipulating rasterized spatial data, such as satellite imagery or climate model outputs. It offers a standardized interface to work with various data formats and in-memory arrays through Raster, RasterStack, and RasterSeries types, simplifying complex geospatial workflows. Rasters.jl provides [significant performance gains](https://github.com/user-attachments/assets/1c6c56ac-4c5a-4096-984d-15bf2783682c) over similar packages in other languages. | |
| [GeoDataFrames.jl](https://www.evetion.nl/GeoDataFrames.jl/dev/) enables the handling of geospatial vector data in Julia by integrating geometric operations directly within DataFrame structures, inspired by Python's [GeoPandas](https://geopandas.org/en/stable/). It achieves this by treating a vector of geometries as a column, allowing for intuitive spatial data manipulation and analysis alongside tabular attributes. | |
| [ArchGDAL.jl](https://yeesian.com/ArchGDAL.jl) A complete solution for working with GDAL in Julia. | |
| [LazIO](https://github.com/evetion/LazIO.jl): Enables the reading and writing of Laz files. | |
| [FlatGeobuf.jl](https://github.com/evetion/FlatGeobuf.jl): A native flatgeobuf implementation in Julia | |
| [SpaceLiDAR.jl](https://github.com/evetion/SpaceLiDAR.jl) Utilities for accessing and working with satellite altimetry data (i.e. ICESat, ICESat-2, GEDI) | |
| [Geomorphometry.jl](https://deltares.github.io/Geomorphometry.jl/v0.7.0/) Geospatial operations, cost and filtering algorithms as used for elevation rasters. | |
| [SortTileRecursiveTree.jl](https://github.com/asinghvi17/SortTileRecursiveTree.jl) An Sort-Tile-Recursive (SRT) tree implementation for GeoInterface compatible geometries. | |
| ## Visualization | |
| Visualization is performed with general purpose plotting packages, see [Makie](https://docs.makie.org/stable/) and [Plots.jl](https://docs.juliaplots.org/stable). The packages below are maintained in the [MakieOrg](https://github.com/MakieOrg) GitHub organization: | |
| [Tyler.jl](https://github.com/MakieOrg/Tyler.jl) for displaying tiled maps interactively with [Makie](https://docs.makie.org/stable/). | |
| [GeoMakie.jl](https://github.com/MakieOrg/GeoMakie.jl) for geographic plotting utilities using [Makie](https://docs.makie.org/stable/). |
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Thanks for the inputs @juliohm. You feedback has ignited some good conversations on what we want JuliaGeo to be and what our shared vision is. Things have evolved greatly over the last 5 years and JuliaGeo is moving well beyond a repository for geo I/O and dataset access towards a full service geo analysis suite that is complimentary to JuliaEarth and that needs to be reflected in the entry page. There are good reasons that several of our contributors, like yourself, maintain affiliated packages on their own repositories and we need a better way of capturing that. I'm going to table this PR until we can get that sorted, with an understanding that the status qua is not working as well as it could.
Member
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You feedback has ignited some good conversations on what we want JuliaGeo to be and what our shared vision is
Keep in mind that you cannot speak for JuliaGeo. Other people here have been around since the beginning of Julia, and I joined this organization around the same time (~10 years ago). Your suggestions in this PR completely disregard alternative views, and your attempt to push your own view into what JuliaGeo is or is not, is not ok.
There are good reasons that several of our contributors, like yourself, maintain affiliated packages on their own repositories
Please do not include me in this list. All packages I maintain are hosted across multiple GitHub organizations (JuliaEarth, JuliaClimate, JuliaML, JuliaGeometry, ...) and are not hosted in my own personal account.
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Points taken... we can bring these changes to a vote with the organization before anything is merged... this way we can make sure it's the majorities view.
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I had a go at updating the JuliaGeo webpage... the goal here is to provide an easier to navigate entry point for new users. I've tired to simplify the package landscape where possible to make it less intimidating for new users to get started.