Earth Engine in BigQuery: A New Geospatial SQL Analytics

BigQuery Earth Engine

With Earth Engine directly integrated into BigQuery, Google Cloud has expanded its geographic analytics capabilities. Incorporating powerful raster analytics into BigQuery, this new solution from Google Cloud Next ‘25 lets SQL users analyse satellite imagery-derived geographical data.

Google Cloud customers prefer BigQueryfor storing and accessing vector data, which represents buildings and boundaries as points, lines, or polygons. Earth Engine in BigQuery is suggested for processing and storing raster data like satellite imagery, which encodes geographic information as a grid of pixels with temperature, height, and land cover values.

“Earth Engine in BigQuery” mixes vector and raster analytics. This integration could improve access to advanced raster analysis and help solve real-world business problems.

Key features driving this integration:

• BigQuery’s new geography function is ST_RegionStats. This program extracts statistics from raster data inside geographic borders, similar to Earth Engine’s reduceRegion function. Use an Earth Engine-accessible raster picture and a geographic region (vector data) to calculate mean, min, max, total, or count for pixels that traverse the geography.
• BigQuery Sharing, formerly Analytics Hub, now offers Earth Engine in BigQuery datasets. This makes it easy to find data and access more datasets, many of which are ready for processing to obtain statistics for a region of interest. These datasets may include risk prediction, elevation, or emissions.
  Raster analytics with this new feature usually has five steps:
• Find vector data representing interest areas in a BigQuery table.
• In BigQuery image assets, Cloud GeoTiff, or BigQuery Sharing, locate a raster dataset that was created using Earth Engine.
• Use ST_RegionStats() with the raster ID, vector geometries, and optional band name to aggregate intersecting data.
• To understand, look at ST_RegionStats() output.
• Use BigQuery Geo Viz to map analysis results.

This integration enables data-driven decision-making in sustainability and geographic application cases:

Climate, physical risk, and disaster response: Using drought, wildfire, and flood data in transportation, infrastructure, and urban design. For instance, using the Wildfire hazard to Communities dataset to assess wildfire risk or the Global River Flood Hazard dataset to estimate flood risk.

Assessing land-use, elevation, and cover for agricultural evaluations and supply chain management. This includes using JRC Global Forest Cover datasets or Forest Data Partnership maps to determine if commodities are grown in non-deforested areas.

Methane emissions monitoring: MethaneSAT L4 Area Sources data can identify methane emission hotspots from minor, distributed sources in oil and gas basins to enhance mitigation efforts.

Custom use cases: Supporting Earth Engine raster dataset imports into BigQuery image assets or Cloud Storage GeoTiffs.

BigQuery Sharing contains ST_RegionStats()’s raster data sources, where the assets.image.href column normally holds the raster ID for each image table. Cloud Storage GeoTIFFs in the US or US-central1 regions can be used with URIs. Earth Engine image asset locations like ‘ee://IMAGE_PATH’ are supported in BigQuery.

ST_RegionStats()’s include option lets users adjust computations by assigning pixel weights (0–1), with 0 representing missing data. Unless otherwise specified, pixels are weighted by geometry position. Raster pixel size, or scale, affects calculation and output. Changing scale (e.g., using options => JSON ‘{“scale”: 1000}’) can reduce query runtime and cost for prototyping, but it may impact results and should not be used for production analysis.

ST_RegionStats() is charged individually under BigQuery Services since Earth Engine calculates. Costs depend on input rows, raster picture quality, input geography size and complexity, crossing pixels, image projection, and formula usage. Earth Engine quotas in BigQuery slot time utilisation can be changed to control expenses.

Currently, ST_RegionStats() queries must be run in the US, us-central1, or us-central2.

This big improvement in Google Cloud’s geospatial analytics provides advanced raster capabilities and improves sustainability and other data-driven decision-making.

Earth Engine in BigQuery: A New Geospatial SQL Analytics

BigQuery Earth Engine

With Earth Engine directly integrated into BigQuery, Google Cloud has expanded its geographic analytics capabilities. Incorporating powerful raster analytics into BigQuery, this new solution from Google Cloud Next ‘25 lets SQL users analyse satellite imagery-derived geographical data.

Google Cloud customers prefer BigQuery for storing and accessing vector data, which represents buildings and boundaries as points, lines, or polygons. Earth Engine in BigQuery is suggested for processing and storing raster data like satellite imagery, which encodes geographic information as a grid of pixels with temperature, height, and land cover values.

“Earth Engine in BigQuery” mixes vector and raster analytics. This integration could improve access to advanced raster analysis and help solve real-world business problems.

Key features driving this integration:

• BigQuery’s new geography function is ST_RegionStats. This program extracts statistics from raster data inside geographic borders, similar to Earth Engine’s reduceRegion function. Use an Earth Engine-accessible raster picture and a geographic region (vector data) to calculate mean, min, max, total, or count for pixels that traverse the geography.
• BigQuery Sharing, formerly Analytics Hub, now offers Earth Engine in BigQuery datasets. This makes it easy to find data and access more datasets, many of which are ready for processing to obtain statistics for a region of interest. These datasets may include risk prediction, elevation, or emissions.

Raster analytics with this new feature usually has five steps:

• Find vector data representing interest areas in a BigQuery table.
• Find an Earth Engine raster dataset in BigQuery image assets, Cloud GeoTiff, or BigQuery Sharing.
• Use ST_RegionStats() with the raster ID, vector geometries, and optional band name to aggregate intersecting data.
• To understand, look at ST_RegionStats() output.
• Use BigQuery Geo Viz to map analysis results.

This integration enables data-driven decision-making in sustainability and geographic application cases:

Climate, physical risk, and disaster response: Using drought, wildfire, and flood data in transportation, infrastructure, and urban design. For instance, using the Wildfire hazard to Communities dataset to assess wildfire risk or the Global River Flood Hazard dataset to estimate flood risk.

Assessing land-use, elevation, and cover for agricultural evaluations and supply chain management. This includes using JRC Global Forest Cover datasets or Forest Data Partnership maps to determine if commodities are grown in non-deforested areas.

Methane emissions monitoring: MethaneSAT L4 Area Sources data can identify methane emission hotspots from minor, distributed sources in oil and gas basins to enhance mitigation efforts.

Custom use cases: Supporting Earth Engine raster dataset imports into BigQuery image assets or Cloud Storage GeoTiffs.

BigQuery Sharing contains ST_RegionStats()’s raster data sources, where the assets.image.href column normally holds the raster ID for each image table. Cloud Storage GeoTIFFs in the US or US-central1 regions can be used with URIs. Earth Engine image asset locations like ‘ee://IMAGE_PATH’ are supported in BigQuery.

ST_RegionStats()’s include option lets users adjust computations by assigning pixel weights (0–1), with 0 representing missing data. If no weight is given, pixels are weighted by geometry position. Raster pixel size, or scale, affects calculation and output. Changing scale (e.g., using options => JSON ‘{“scale”: 1000}’) can reduce query runtime and cost for prototyping, but it may impact results and should not be used for production analysis.

ST_RegionStats() is charged individually under BigQuery Services since Earth Engine calculates. Costs depend on input rows, raster picture quality, input geography size and complexity, crossing pixels, image projection, and formula usage. Earth Engine quotas in BigQuery slot time utilisation can be changed to control expenses.

Currently, ST_RegionStats() queries must be run in the US, us-central1, or us-central2.

This big improvement in Google Cloud’s geospatial analytics provides advanced raster capabilities and improves sustainability and other data-driven decision-making.

Minggu ini, sejak tanggal 2 sd 4 Maret, kami bermain-main lagi dengan #EarthEngine bersama rekan-rekan mahasiswa. Kali ini ada enam mahasiswa dari Universitas Esa Unggul (@univ_esaunggul) Jakarta, delapan dari Universitas Sultan Ageng Tirtayasa (@untirta_official) tiga dari program MBKM BRIN (satu dari @univ.brawijaya dan dua dari @univ.udayana).Wakt
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Waktu yang singkat, 2,5 hari full, ternyata materi mampu diserap dengan baik oleh rekan2 hebat ini. Terbukti dari dua kali kuis yang hasilnya menggambarkan kemampuan mereka menyerap “ilmu baru” dengan sangat baik. Dan mereka pun mampu untuk bekerja bersama/kelompok! 😊👍👍👍
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Terima kasih rekan2 semua, sudah saling membantu sesama peserta dalam diskusi2 yang menarik. Saya belajar banyak dari kalian semua. Maaf belum hafal akun kalian 😁🙏
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#GEE #RemoteSensing @herlyn_nf07 @helenaaatmsr @itanurlaita @d.ssyf @masllhamm_ @abdlmnan_ @rzkii_a @madrasman25 dkk 😁🙏
https://www.instagram.com/p/CasE2uCvRSv/?utm_medium=tumblr

Awal tahun 2022 ini kedatangan tetamu istimewa, para penderma ilmu dari berbagai penjuru tanah air. Ada yg dari Udayana @univ.udayana Bali, dari UPI @upiofficial Bandung, dari Untirta @untirta_official Serang. Mereka akan berbagi ilmu dalam Kerja Praktik dan Tugas Akhir tahapan strata-1.
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Semoga menjadi keasyikan tersendiri dalam mendalami ilmu spasial bersama. Karena tidak hanya praktik tetapi juga banyak tantangan lainnya yang akan mereka temui. Semangat yaaa 😊👌
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#PenginderaanJauh #EarthEngine
https://www.instagram.com/p/CYbte5VviXE/?utm_medium=tumblr

Dalam acara berbagi rasanya kurang seru tanpa adanya kuis. Bukan sekedar rame tetapi sekaligus mengakrabkan suasana dan menyerap hal baru dengan menyenangkan.
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Demikian juga saat bersama rekan2 dari DKP Sulawesi Barat @dkp_sulbar . Keriuhan diakhiri pemberian bonus bagi pemenang kuis berupa buku @hotchocolate.thenovel . Semoga buku segera diterima dan bisa menjadi teman dalam rangkaian nataru. 📕☕
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Selamat untuk @rusman_uchok dan @sanrayuli . Juga selamat untuk tim yang sangat bersemangat mengeksplor Google Earth Engine untuk aplikasi kelautan dan perikanan, termasuk mangrove juga ✌😁👌
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#DKPSulbar #Mamuju #HotChocolateTheNovel #GEE #EarthEngine
https://www.instagram.com/p/CXyYL3iPdEb/?utm_medium=tumblr

Magnetmotor eines US-Erfinders vor der Markteinführung

Der “Dasgehtanders” Blog machte in einem Artikel vom 22. Oktober 2019 auf eine sich anbahnende Energieerzeugungsrevolution in den USA aufmerksam, welche vom Mainstream bis dato “unentdeckt” blieb bzw. ignoriert wird. Es ist eine grandiose Ingenieursentwicklung des US-Erfinders Dannis Danzik, die dort wie folgt beschrieben wird:

“Earth Engine, so nennt Dennis Danzik den Motor, der ausschließlich…

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Magnetmotor eines US-Erfinders vor der Markteinführung

Der “Dasgehtanders” Blog machte in einem Artikel vom 22. Oktober 2019 auf eine sich anbahnende Energieerzeugungsrevolution in den USA aufmerksam, welche vom Mainstream bis dato “unentdeckt”  blieb bzw. ignoriert wird. Es ist eine grandiose Ingenieursentwicklung des US-Erfinders Dannis Danzik, die dort wie folgt beschrieben wird:

“Earth Engine, so nennt Dennis Danzik den Motor, der ausschließlich…

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Know how #EarthEngine’s living map helps local communities fight deforestation
Earth Engine creates a living map of forest loss
In 2005, a Google engineer named Rebecca Moore got a notice about a logging plan near her home in the Santa Cruz Mountains. The mailing included a grainy black-and-white map that did nothing to show what was at stake with the plan. Dissatisfied, she decided to create a new map using details of the plan overlaid on the 3-D satellite imagery in Google Earth. Moore’s visualization illuminated what was really at stake: where exactly the 1,000 acres of logging would occur, the threats to water and old-growth redwoods, even the narrow mountain roads where logging trucks would be navigating blind curves near kids walking to school.
The map activated the community, and its close inspection of the plan ultimately led the California Department of Forestry to deem the proposal ineligible. That local success was one of the key steps that Moore and the Google Earth team took on their way to meeting a much larger challenge: If the impact to one area could be so quickly and powerfully visualized, could we monitor forest changes in high resolution across the entire globe?
to read more>> click this link:http://technoworldbeyondfuture.blogspot.in/2016/12/know-how-earthengines-living-map-helps.html
Source:Google

How to Show Changes and More on Maps

Showing students how landforms change has been a challenge for some time. Let’s face it, there is only so much we can do with maps and pictures. Now, with Earth Engine by Google, this has never been easier.
(more…)

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Apply to attend the workshop by November 1st!

I participated to the Google Earth Engine workshop that was organized in  the Google San Francisco downtown office on december 9th 2013.

I joined the Google Earth Engine beta testing team discovering the very interesting capabilities of the workspace/programming environment (namely the Playground) that give access to raster based analyses using DEMs and several remote sensing data that are available on the google servers for on-the-fly geospatial analyses. I hope to have time soon to use this tool effectively for research applications.

For more info visit the official page at https://earthengine.google.org/

I had the chance to meet some Googlers and enter the main office in Mountain View to visit the Googleplex. Some pictures are available at my Facebook page following this link.

Google has composited satellite images from the past 28 years to make a massive, zoomable timelapse image.

More info here, or check out the website here.