Geospatial in Cloud: GCP

GCP Geospatial Services Map

GCP's geospatial capabilities are spread across six services. Understanding which one to use for what is the first decision you need to make.

Most geospatial teams on GCP use three of these: BigQuery GIS for analysis, GCS for storage, and Cloud Run for serving. Earth Engine is the specialist tool you bring in when satellite imagery is involved.

BigQuery GIS Deep Dive

BigQuery GIS operates on a native GEOGRAPHY type. No extensions, no plugins, no PostGIS-style setup. Spatial functions work directly on petabyte-scale tables with the same SQL your analysts already write.

A spatial join on 1 billion points against 50K polygons takes 8-12 seconds. Try that on PostGIS.

SELECT
    p.name,
    z.flood_risk_level,
    ST_AREA(z.geometry) / 1e6 as zone_area_km2
FROM `project.dataset.properties` p
JOIN `project.dataset.flood_zones` z
ON ST_CONTAINS(z.geometry, p.geometry)
WHERE p.country = 'DE'

Standard SQL. No spatial extensions to install, no index creation required. The syntax is identical to what your data team already writes - it just happens to include spatial predicates.

A distance-based query against the full OpenStreetMap dataset (1.5TB, pre-loaded in BigQuery's public datasets):

SELECT
    osm.name,
    osm.amenity,
    ST_DISTANCE(
      osm.geometry,
      ST_GEOGPOINT(13.4, 52.5)
    ) as distance_m
FROM `bigquery-public-data.geo_openstreetmap.planet_features` osm
WHERE osm.amenity = 'hospital'
AND ST_DWITHIN(
  osm.geometry,
  ST_GEOGPOINT(13.4, 52.5),
  10000  -- 10km radius
)
ORDER BY distance_m
LIMIT 10

For a deep dive on the file formats that power this, see our guide on cloud-native geospatial formats (GeoParquet, COG, STAC). GeoParquet and COG work natively with BigQuery and GCS.

Earth Engine: Honest Assessment

Earth Engine is extraordinary for a specific set of problems. It is also wildly overused for problems it was never designed to solve. Here is where it genuinely has no competition, and where it creates more problems than it solves.

Where Earth Engine Is Unmatched

Where Earth Engine Is Overkill or Wrong

Cloud Run for Processing

Cloud Run is the underrated workhorse of GCP geospatial. Deploy a FastAPI + GDAL container with zero infrastructure management. It auto-scales from 0 to N instances based on request volume, and you pay only for actual request time.

Perfect for tile servers, geocoding APIs, on-demand raster processing, and any geospatial microservice that needs to scale without cluster management.

from fastapi import FastAPI
import geopandas as gpd
from shapely.geometry import box

app = FastAPI()

@app.post("/spatial-query")
async def spatial_query(bbox: dict):
    gdf = gpd.read_parquet(
        "gs://my-bucket/parcels.parquet"
    )
    area = box(
        bbox["minx"], bbox["miny"],
        bbox["maxx"], bbox["maxy"]
    )
    result = gdf[gdf.intersects(area)]
    return {
        "count": len(result),
        "parcels": result.to_json()
    }

Deployment is a single command:

gcloud run deploy geo-api \
    --source . \
    --region europe-west1 \
    --memory 2Gi \
    --timeout 300

No Kubernetes manifests. No Docker Compose files. No load balancer configuration. Cloud Run handles HTTPS, autoscaling, and zero-downtime deployments automatically. For teams that want to deploy geospatial APIs without becoming infrastructure engineers, this is the fastest path.

Public Datasets: GCP's Hidden Weapon

This is the advantage nobody talks about. GCP hosts the largest collection of pre-loaded, pre-indexed, query-ready geospatial datasets on any cloud platform. On AWS or Azure, you need to download, convert, and load them yourself. On GCP, it is one SQL JOIN away.

The practical impact: enriching your proprietary data with OSM points of interest, census demographics, or weather patterns is a JOIN operation, not an ETL pipeline. No data engineering required. No storage costs for the public data. You pay only for the query compute ($5/TB scanned).

Real Benchmarks

These numbers come from production workloads, not synthetic tests. Each operation was measured with standard GCP pricing in europe-west1.

Cost Analysis

A side-by-side comparison for a mid-sized geospatial team (10-30 users, 1TB vector data, regular spatial analysis workloads).

When NOT to Use GCP for Geospatial

We run geospatial workloads on GCP daily. We still tell clients not to use it in these scenarios:

Reference Architecture

A production GCP geospatial stack for a team running mixed vector and raster workloads. This is the architecture we deploy for clients who are already on GCP.

Data Lake:
  GCS bucket (COG + GeoParquet)
  <- source data, versioned

Analytics:
  BigQuery GIS
  <- spatial SQL, joins, aggregations

  Earth Engine
  <- satellite imagery, temporal analysis
  <- raster specialisation ONLY

Processing:
  Cloud Run (FastAPI + GDAL)
  <- APIs, on-demand processing

  Dataflow (Apache Beam)
  <- streaming, real-time ingestion

ML:
  Vertex AI
  <- geospatial classification, predictions

The key principle: each service does one thing well. BigQuery for SQL analysis, Earth Engine for raster, Cloud Run for APIs, GCS for storage. Resist the temptation to route everything through Earth Engine or build everything as BigQuery stored procedures.

For teams comparing this with other platforms, Part 1 covers Databricks (stronger for lakehouse architectures) and Part 2 covers AWS (more flexible, DIY approach).