The official source for Apache Sedona news, technical insights, release updates, and best practices in large-scale spatial data management.
SedonaDB 0.4.1 supports raster analysis in SQL and Python on a single machine. This example uses eleven PlanetScope satellite images to estimate the area burned in the Gironde and Landes wildfire of July 2026. SQL selects and aligns the images, NumPy identifies vegetation loss, and raster functions convert the result into polygons and area estimates by municipality.
Many raster analyses need one result for each polygon. Examples include mean elevation by canton, forest cover by district, and flood depth by parcel. A raster stores values in a grid of pixels. A polygon defines an area, also called a zone.
Zonal statistics summarize the raster pixels inside each zone. A spatial join matches data by location. Apache Sedona performs both steps in one query. This example reads 7,006,027 elevation pixels from Amazon S3 and summarizes all 26 Swiss cantons in 56 seconds.
In a flat spatial query the world is only its noon shadow on the ground. Two objects whose shadows touch can be four hundred feet apart, and from a flat predicate the answer is the same for both.
The questions put to spatial engines have stopped being flat. Drones under FAA ceilings, aircraft over towers, apartments above restaurants, mineral deposits below mining leases: each one comes down to whether two volumes meet, and until now the SQL answer was the shadow version. Sedona 1.9.1 adds the missing axis. There is a Box3D type with constructors and accessors, 3D predicates and distances, a 3D extent aggregate, and query plans that account for the third axis.
The Mississippi wanders. Between the two edges of a single Sentinel-2 scene it travels 147.6 km to cover 103 km of ground, and its width swings from 131 m to almost 3 km. Every one of those numbers came out of raw pixels: Apache Sedona's raster operators built the river polygon, and ST_ApproximateMedialAxis drew the line down the middle of it.
Those deep-green grids are California's Central Valley — some of the most intensively farmed land on Earth, not as a camera sees it but as the NDVI vegetation index does: computed, pixel by pixel, by seven lines of NumPy. Sedona ran those seven lines on 484 satellite tiles in parallel, straight out of a public S3 bucket, and wrote the answer back as Cloud Optimized GeoTIFFs. That's the new move in Sedona 1.9.1: a plain Python UDF can take raster columns and return rasters. If the raster function you need isn't built in — your index, your QA rule, your model — write it.
Every raster team has the bucket. Thousands of GeoTIFFs accumulated from surveys, vendors, and pipelines — and no catalog. Which files cover this area? Which CRS are they in? Which ones were never converted to Cloud Optimized GeoTIFF? Until now, answering meant crawling the bucket with one-off scripts — or pulling rasters down just to inspect them.
Anyone can look at a map and see clumps. The interesting questions are the ones eyeballing can't answer: is that clustering statistically real? Exactly where is it significant? And which points genuinely don't belong?
OpenStreetMap is the world's map — every road, café, and coastline, edited by millions of people. It ships as .osm.pbf: a dense, compressed Protocol-Buffers blob of nodes, ways, and relations. Getting that into a cluster usually means a preprocessing detour through osmium or a staging database.
You have the shapes. Somewhere in a petabyte of satellite imagery are exactly the scenes that cover them. The hard part was never the analysis — it was the plumbing in between.
The hardest part of working with big rasters often isn't the analysis. It's just getting the file open.
