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Apache Sedona

Apache Sedona is the most widely deployed open-source distributed spatial engine in the world, with 68M+ downloads. It runs on Apache Spark and Apache Flink and supports spatial SQL, Python, Java, and Scala for processing geometry and raster data at scale. Wherobots was founded by Jia Yu and Mo Sarwat, the researchers who created Apache Sedona at Arizona State University. Any Apache Sedona workload runs on Wherobots with zero code changes.

Try Apache Sedona On Wherobots Cloud
2,000,000 Downloads per month
68,000,000 Number of downloads
2,200 Stars on github
200% Year over year growth

The Open Source Solution for High-Performance Geospatial ETL & Analytics

Apache Sedona extends distributed compute frameworks to add support for spatial data types, spatial indexing, and efficient spatial query processing using Spatial SQL.

With 300 vector and raster Spatial SQL functions and support for multiple distributed compute engines including Apache Spark, Apache Flink, and Snowflake, Apache Sedona brings the most comprehensive support of Spatial SQL to parallel and scalable data processing environments.

Apache Sedona Architecture

Get started

The easiest way to get started is by using the official Apache Sedona Docker image

docker run apache/sedona:latest

Apache Sedona can be run in many different cloud platforms including:

learn more

Leverage resources to learn more about using Apache Sedona to solve your large-scale spatial data problems.

We recap what shipped in Apache Sedona 1.9, including the Cloud Optimized GeoTIFF writer, the new PROJ-based CRS engine, and Bing tile functions. We then preview SedonaDB 0.4 with GPU-accelerated spatial join on ray tracing cores, geography joins on a sphere, the sedona-gdal crate, and raster predicates for tile pruning. The session closes with a sneak peek at Sedona 1.9.1 (geography type in Sedona Spark, Box2D, Box3D, and more raster support), community resources, and Q&A.
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS45RTgxNDRBMzUwRjQ0MDhC

Apache Sedona Community Office Hour: June 2026

Topics include:- Recent Apache Sedona 1.9.0 release with a focus on its raster functionalities- Upcoming SedonaDB release’s new features such as GPU accelerated spatial join
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Apache Sedona Community Office Hour: May 2026

During this office hour, we'll coverthe features of a number of releases across the Apache Sedona ecosystem including- Apache Sedona 1.8.1- SedonaDB 0.2.0- SpatialBench 0.1.000:00 Welcome and agenda00:44 Apache Sedona ecosystem overview and stats01:55 Sedona architecture: Spark, Flink, Snowflake, SedonaDB05:02 Release cadence and recent releases05:56 Sedona 1.9 highlights: Spark 4.1 and new CRS engine07:41 Sedona 1.9: cloud optimized GeoTIFF writer08:21 Sedona 1.9: Bing tile functions and auto-tiling raster data source09:43 Sedona 1.9: new geometry functions, GeoParquet, and GeoPandas API11:48 SedonaDB 0.4 preview: GPU spatial joins, geography type, GDAL raster15:38 SedonaDB 0.4: geography joins with S2 cells and raster-vector interop18:45 Guest talk: geography type coming to Sedona Spark 1.9.119:46 Why geography matters: anti-meridian, geodesic distance, polar regions25:07 New geography ST functions and join predicates27:43 Under the hood: lazy serialization and S2 indexing30:22 How geography spatial joins work in Spark33:16 Benchmark: geography vs. geometry performance39:22 Free Sedona book, community resources, and how to contribute
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Apache Sedona Community Office Hour: February 2026

Topics covered:• Apache Sedona 1.8.1 • SedonaDB 0.2.0 • SpatialBench 0.1.00:00 Introduction 3:57 What Is SedonaDB? 6:59 Apache Sedona Community Growth 9:50 Sedona Flink SQL Module for Enhanced Flink Integration 11:40 STAC Authentication Support in Python & Scala APIs 13:57 ST_ApproximateMedialAxis & ST_StraightSkeleton: Why They Matter 19:24 SpatialBench Overview: Components & Results 22:19 Sedona Performance Benchmarks: Databricks Serverless SQL vs. Sedona 28:03 SedonaDB Demo Spatial Query Benchmarking on Databricks with SpatialBench: https://sedona.apache.org/latest/blog/2026/01/08/spatial-query-benchmarking-databricks/
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Apache Sedona Community Office Hour: January 2026

Topics covered:- Highlights from the new Apache Sedona 1.8 release- A sneak peek at what’s next in Apache Sedona 1.8.1- Latest status report on ST_Transform for better accuracy- ST_Transform in SedonaDB explanation and live demoCheck out the Cloud Native Geospatial Analytics with Apache Sedona: https://bit.ly/3T9DqGa
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Apache Sedona Community Office Hour: November 2025

We’re celebrating Apache Sedona 1.8.0 release and the launch of SedonaDB & SpatialBench with a special Apache Sedona Community Office Hour!Here’s what we’ll cover: - What’s new in Apache Sedona 1.8.0 - The launch of SedonaDB with examples & demos - A deep dive into SpatialBench and its designCheck out SedonaDB here: https://sedona.apache.org/sedonadb/latest/
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Apache Sedona Community Office Hour: October 2025

In this session, we'll cover: - New Area of Interest API in ST_Transform- GeoPandas API progress- Sneak peek at 1.8.0 release- Geography type support update- New Well-Known Locations library
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS45ODRDNTg0QjA4NkFBNkQy

Apache Sedona Community Office Hour: September 2025

Features of Sedona 1.8.0 covered:- GeoPandas API for Sedona- Running Sedona on PyFlink- Comprehensive Spark 4.0 support- Geography type integrationDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS5EMEEwRUY5M0RDRTU3NDJC

Apache Sedona Community Office Hour: July 2025

During this office hour, we covered the upcoming Apache Sedona 1.8.0 release:- GeoPandas API on Sedona- Sedona on PyFlink- Spark 4.0 support- Sedona vectorized Python UDF- GeoParquet, and Parquet Geo types0:00 Introduction2:11 Overview of Sedona System Architecture6:45 What’s new in 1.7.1 release10:26 OpenStreetMap PBF Reader12:44 Sneak Peak: Sedona 1.7.2 Release 13:35 Looking Ahead: Sedona 1.8.0 Release16:56 Why We’re Support Java 1121:11 Example of PyFlink Support22:38 Overview of GeoParquet24:30 What is Apache Iceberg?27:12 Working With Iceberg Geo Types33:49 Working With Parquet Geo Types35:44 Final Thoughts & Q&ADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
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Apache Sedona Community Office Hour: June 2025

For this month's office hour, we covered:- Broadcast join support for distributed KNN Join- STAC catalog & OpenStreetMap (OSM) PBF reader- Preview of 1.8.0: GeoPandas API, PyFlink support & Spark 4.0 compatibility- Live demo: Using Sedona with Overture Maps dataDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS5GNjNDRDREMDQxOThCMDQ2

Apache Sedona Community Office Hour: May 2025

This month, we covered Sedona 1.7.1 release: - SQL interface for GeoStats (ST_DBSCAN, ST_GLocal, ST_LocalOutlierFactor) - Broadcast join support for distributed KNN Join - STAC catalog & OpenStreetMap (OSM) PBF reader - New ST functions like ST_RemoveRepeatedPoints0:00 Introduction2:27 Sedona System Architecture8:21 What’s New in Sedona 1.7.1 Release10:50 GeoSTATS12:25 Broadcast kNN Join14:59 OpenStreetMap PBF Reader17:16 GeoParquet with Balanced Partitioning20:09 GeoArrow Support in Sedona DataFrame21:31 Faster Rasterization24:14 STAC Catalog reader - Feng Zhang29:07 STAC Item Schema30:57 DF/SQL Filter Pushdown34:38 Example: Using STAC Reader with Your Notebook42:33 Q&ADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
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Apache Sedona Community Office Hour: April 2025

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. Topics covered:What’s new in Sedona 1.7.0- KNN Join- GeoStats- DataFrame readers (shapefiles, GeoPackage). Will potentially invite Kristin to give a presentation about the Shapefile readerFeatures in upcoming new 1.7.1 release- GeoStats SQL interface- STAC reader- Iceberg Geo reader / writer in Sedona- Parquet Geo reader / writer in SedonaCommunity development- Improvement on docs- Enable GitHub discussion- Encourage to use GitHub issues instead of JIRADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS5DQUNERDQ2NkIzRUQxNTY1

Apache Sedona Community Office Hour – March 2025

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we cover:* What’s new in Sedona 1.7.0: KNN Join, GeoStats, DataFrame readers * Features in the upcoming new 1.7.1 release: GeoStats SQL interface and bug fixes* Community development: Documentation improvements and GitHub discussionDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS41MzJCQjBCNDIyRkJDN0VD

Apache Sedona Community Office Hours – February 2025

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we cover:* How to utilize the KNN Join* Deep dive into GeoStats* More about DataFrame-based readersDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS4xMkVGQjNCMUM1N0RFNEUx

Apache Sedona Community Office Hours – December 2024

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we cover:* Sedona 1.7.0 release* KNN Join* GeoStats: scalable spatial statistics toolbox* DataFrame-based readersDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS4wOTA3OTZBNzVEMTUzOTMy

Apache Sedona Community Office Hours – November 2024

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we cover:* Sedona 1.6.1 release* KNN Join* Native file readers and writers* Sedona 1.7.0 release timelineDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS41MjE1MkI0OTQ2QzJGNzNG

Apache Sedona Community Office Hours – September 2024

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we covered:* The timeline for the next release 1.6.1* GeoParquet 1.1, including attribute support* New ST functionsDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS4wMTcyMDhGQUE4NTIzM0Y5

Apache Sedona Community Office Hour – August 2024

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. This month we covered:* Getting started with the Apache Sedona Docker image* An overview of the Apache Sedona 1.6.0 release* Upcoming features in the the 1.6.1 release* A call for help with Sedona documentationDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS4yODlGNEE0NkRGMEEzMEQy

Apache Sedona Community Office Hour – June 2024

Each month we host the community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans.It’s an excellent opportunity for the community to engage and contribute to Apache Sedona’s development.This time we'll be covering:* What's new in Apache Sedona 1.5.2* What's new in Apache Sedona 1.6.0* Apache SIS integration* Creating Sedona User Defined Functions (UDFs) with Shapely, Rasterio, and NumPy
YouTube Video UExmSmtjMXBQUjlMR2NYR2pkSFRpdG5CZUQtSFdtSnFLcS41NkI0NEY2RDEwNTU3Q0M2

Apache Sedona Office Hours – May 2024

Connect with Apache Sedona across platforms and networks.

Github

The Apache Sedona GitHub repository is the home of Sedona’s open-source code

visit our github
Follow Apache Sedona On X

Keep up to date with important announcements and links to new technical content

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Connect On LinkedIn

Find new events and announcements from the community on LinkedIn

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Apache Sedona YouTube Videos

Watch tutorials, livestreams, webinar recordings, and more on YouTube

VISIT OUR YOUTUBE
dockerhub

The official Apache Sedona Docker image is the easiest way to get started with Apache Sedona

VISIT OUR DOCKERHUB

Review the latest on Apache Sedona across docs, blogs, and release notes.

Apache Sedona Documentation

Find tutorials and reference docs in the official Apache Sedona documentation

Check sedona Documentation
Apache Sedona On The Wherobots Blog

Many of the posts on the Wherobots blog are technical examples using Apache Sedona

Check our Blog
release notes

Keep up to date with new features in each release in Apache Sedona’s release notes

Check our Release notes

Get Involved

Join the Apache Sedona community and be a part of the modern geospatial analytics ecosystem. There are many ways to be involved.

Learn how to use our new Model Context Protocol (MCP) server to streamline your spatial data workflows. This allows you to interact with your spatial catalogs and databases directly through an AI agent.In this walkthrough, we cover:- Catalog Exploration: Watch the MCP server navigate hierarchies to find specific datasets (like NYC Taxi data) across professional and custom catalogs.- Data Introspection: Perform live queries against massive datasets to get instant insights, such as counting 185 million taxi pickups in seconds.- Agentic Q&A: See how the server handles complex natural language requests, like finding "burger joints near the Space Needle," by inferring locations and schemas automatically.- Notebook Generation: Learn how to use the MCP server to write and validate Python/Jupyter notebooks for heavy-duty spatial joins and large-scale data processing in Wherobots Cloud.Try the MCP server: https://docs.wherobots.com/develop/mcp/mcp-server-installation0:00 – Introduction to the Wherobots MCP Server0:25 – Catalog Exploration: Searching for spatial datasets1:40 – Data Exploration: Running live queries against the database3:11 – Agentic Q&A: Natural language spatial searches (Space Needle Example)4:40 – Generating Jupyter Notebooks for large-scale processing6:45 – Security & Permissions: Understanding the auto-approval step7:55 – Model Choice: Why Claude 4.5 Sonnet is recommended9:10 – Running the generated notebook in Whereabouts Cloud10:40 – Final thoughts and how to get started
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS5GNjNDRDREMDQxOThCMDQ2

Wherobots MCP Server Demo

🚀 Get started and sign up at https://wherobots.comIn this video, we introduce you to the Wherobots platform, kicking off a series of tutorials designed to familiarize you with its powerful tools and capabilities. Whether you're exploring this video on YouTube or embedded within our notebook environment, this guide ensures you’re set up for success.What You'll Learn:- An overview of the Wherobots Cloud platform.- How to navigate the series of notebooks designed for onboarding.- Essential steps to get started and maximize the platform’s potential.Who Is This For?- New users looking to learn the basics of Whereabouts Cloud.- Professionals exploring geospatial tools for scalable data integration and analysis.- Developers and analysts eager to harness the power of modern geospatial technologies.💡 Next Steps:If you’re watching on YouTube, visit the platform to dive into the interactive notebooks.Already on the platform? Awesome! Follow along to enhance your learning experience.📌 Stay tuned for upcoming videos in this series as we delve deeper into the advanced functionalities of Whereabouts Cloud.Don't forget to like, subscribe, and share if you found this video helpful!Sign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS41MjE1MkI0OTQ2QzJGNzNG

Geospatial Python: Raster and vector data loading in Apache Sedona [Tutorial]

Are you ready to unlock the full potential of your geospatial data? This webinar is designed for professionals and enthusiasts who want to take their geospatial analytics to the next level. Whether you’re just starting out or already working with spatial data, you’ll learn how to leverage the powerful tools and workflows in Wherobots Cloud to analyze, visualize, and interpret geospatial datasets. From setting up your account to mastering advanced analytics, this session will give you actionable insights and hands-on experience.Sign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS4wOTA3OTZBNzVEMTUzOTMy

Wherobots 101: Mastering Scalable Geospatial Data Processing

Follow along for an in-depth webinar on efficiently loading, managing, and analyzing raster and vector data in Wherobots' hosted environment. Whether you're working with massive geospatial datasets or looking for optimized workflows to write and query GeoParquet and Cloud-Optimized GeoTIFFs (COGs), this session will equip you with the tools and techniques to scale your geospatial analysis.In this session, we will walk through two different tutorials:1) For the first tutorial, you can use the community edition to follow along. If you haven’t already, you can create your account here: https://bit.ly/3Q9PcPe 2) For the second tutorial, you will need a Wherobots Pro tier account, which you can sign up through the AWS Marketplace with $400 in free credits: https://aws.amazon.com/marketplace/pp/prodview-ndy62v6hhwrne?sr=0-1&ref_=beagle&applicationId=AWSMPContessaSign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS4xMkVGQjNCMUM1N0RFNEUx

Wherobots 102: Reading and Processing Cloud Native Geospatial Data

In this session, you'll learn how to seamlessly integrate Python and Wherobots to perform advanced spatial joins and analyses on geospatial data.Webinar Highlights:- Learn how to join datasets using spatial predicates like ST_Intersects to combine facilities with administrative boundaries—ensuring only relevant spatial relationships are processed.- See how to leverage the powerful ST_AKNN function to efficiently find the k-nearest neighbors for each facility. We’ll explore a Pythonic approach to identify the closest centroids to your facilities, ensuring a highly optimized query without the overhead of expensive cross joins.- Understand how to apply spatial filters to extract a targeted subset of your data. We'll walk through filtering facilities that fall within a specified geographic boundary.- Explore strategies like repartitioning by geohash to improve performance and reduce data shuffling during spatial joins, making your spatial queries scalable for large datasets.- Finally, learn how to visualize your spatial join results interactively using SedonaKepler and SedonaPyDeck, turning complex geospatial data into actionable insights with rich, interactive maps.Timestamps00:00 Introduction 03:01 Introduction to Wherobots, Apache Sedona and product architecture08:16 Performance benchmarks: Wherobots vs. Sedona, BigQuery and Snowflake09:55 Create account and get started with Wherobots | Start a notebook, setting runtime, adding Python libraries, cloud storage, and spatial catalog18:40 Start demo | Connect to runtime and notebook, importing functions from PySpark20:48 Load data | Polygons from Overture Maps and points from Foursquare Open Places Dataset23:49 Standard Spatial Join (Pythonic Approach)27:32 Spatial aggregate | Efficiently count points in each polygon 29:13 K Nearest Neighbor Join 37:22 Advanced optimization techniques | Cluster data using Geohash 47:28 Visualize Spatial Join results with SedonaKepler and SedonaPyDeck48:11 Summary and Q&ASign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS41MzJCQjBCNDIyRkJDN0VD

Spatial Joins at Scale: Unlocking Advanced Geospatial Analytics with Wherobots

Zonal statistics—like calculating average elevation or temperature over custom geometries—is one of the most powerful ways to combine raster and vector data. But when it comes to scaling up beyond a few polygons, traditional GIS tools often hit a wall.In this live session, we’ll show you how Wherobots makes large-scale zonal stats simple, fast, and scalable using RS_ZonalStats, a powerful function built for modern spatial SQL. You’ll see how easy it is to load vector features (like buildings or land parcels) and join them with cloud-hosted raster layers to compute meaningful insights—without slowdowns.We’ll walk through:- How to prepare and load both raster and vector data- Using RS_ZonalStats to compute min, max, mean, and more- A real-world demo with Overture Maps and elevation data from AWS- Tips for optimizing and scaling your raster analysis workflowsWhether you’re analyzing terrain, land cover, or climate data, this is your shortcut to making raster analysis work at any scale.0:00 Introduction4:38 Overview of RS_ZonalStatsAll and ST_Isochrones7:48 Set up10:17 Spatial join between raster and polygon dataset13:15 Accessing and preparing raster data 18:59 Load isoochrone polygons from Iceberg 21:16 Compute zonal statistics26:05 Results and efficiency28:41 Further exploration and Q&ASign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe
YouTube Video UExmSmtjMXBQUjlMRkY1ci1FZ0xQS3VyVzcwZERYbzRKeS45NDk1REZENzhEMzU5MDQz

Scaling Zonal Statistics: Fast Raster + Vector Joins with Wherobots

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Apache Sedona Issue Tracker

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