How to host GIS imagery on a cloud platform?
- 12 hours ago
- 4 min read
Geospatial data volumes are increasing at a rate never seen before. All types of organizations that use satellite images, drones, LiDAR, and raster data need scalable infrastructure to store, manage, and deliver GIS data. Many of the on-premises GIS servers used today are limited in performance and scalability due to high operational costs.
Cloud-based solutions are designed to solve all the above problems because they provide benefits such as elastic storage, global accessibility, high availability of storage, and fast image delivery.
This guidance document provides instructions on how to store GIS images in a cloud solution, the type of architecture to use (along with examples), what tools to use, and tips for optimizing GIS images at an enterprise level.
Why Host GIS Imagery in the Cloud?
Some advantages of using cloud-based GIS imagery as opposed to using traditional infrastructures are:
Scaleable
Cloud companies can add or reduce your storage and computing capabilities as needed in real time.
Faster Image delivery
Utilization of CDNs and globally distributed storage helps to improve tile serving speeds.
Reduced Infrastructure Costs
Utilizing a cloud solution removes the need for expensive server machines and adds no maintenance costs or overhead.
High Availability
Cloud solution providers have built in redundancy and have disaster recovery as part of their services.
Easier Team Collaboration
Teams have secure access to imagery datasets from anywhere.
Types of GIS Imagery You Can Host
Drone orthomosaics
Raster datasets
LiDAR-determined rasters
DEM's (Digital Elevation Model)
Thermal images
Multispectral images
Time-series geospatial images
Each type of image requires a specific storage and processing strategy.
Best Cloud Platforms for GIS Imagery Hosting
Amazon Web Services (AWS)
Some popular tools offered by the AWS platform include:
Amazon S3
EC2
Lambda
Cloudfront
AWS Open Data Registry
It is ideal for GIS systems that can be deployed at the enterprise level.
Microsoft Azure
Some of the tools available from the Azure platform include:
Azure Blob Storage
Azure Maps
Azure Kubernetes Service (AKS)
It is well-suited for organizations that primarily use Microsoft technologies (Microsoft stack).
Some of the advanced geospatial and AI-powered capabilities offered by GeoWGS84.ai include:
AI-based Geospatial Analytics
Drone Mapping Data Processing
Orthomosaic and 3D Mapping Solutions
Satellite and UAV Data Integration
Cloud-Based GIS Workflows
It is best suited for organizations seeking AI-driven geospatial intelligence, drone mapping automation, and scalable spatial data solutions.
Google Cloud Platform (GCP)
Some of the services offered by the GCP platform include:
Google Cloud Storage
Earth Engine Integrations
BigQuery GIS
It is best suited for workloads that rely heavily on analytics.
Best Practices for Cloud GIS Imagery Hosting
Use Cloud Optimized GeoTIFF (COG)
COGs have become the new craze in scalable raster hosting.
Use Tile Caching
Don’t render the image every time someone calls for it.
Use Compression
Reducing bandwidth costs:
LZW
DEFLATE
JPEG
Enable Metadata Management
Store metadata separately for search and indexing.
Monitor Usage and Costs
Use cloud monitoring tools to measure:
Bandwidth
Storage growth
API requests
Rendering performance
Security Concerns
Security Considerations
GIS imagery may contain sensitive infrastructure or proprietary data.
Essential Security Measures:
Role-Based Access Control (RBAC)
API Authentication
Only HTTPS Access
Private buckets
Audit Logs
Cloud-Based GIS Technology To Consider
Cloud-Based GIS technologies have become more common in Modern Geospatial Systems:
Spatiotemporal Asset Catalogs (STAC)
Cloud Optimized Geotiffs (COG)
Kubernetes
Serverless GIS
Raster Analytics Pipelines
Artificial Intelligence (AI) Geospatial Analysis
By Implementing Cloud-Based GIS infrastructures in COG format and/or STAC format and leveraging AI-based Spatial Intelligence capabilities, organizations can streamline their Geospatial Workflows.
Major Challenges When Storing GIS Imagery
Large File Sizes
Highly Detailed Satellite Imagery can reach Terabytes of Data easily.
Possible solution:
Using Tiling, Compression, and Lifecycle Policies.
Slow Rendering
Massive Raster Files can overwhelm server resources.
Possible solution:
Using Pre-Generated Tiles and Caching.
High Cloud Costs
GIS can consume a lot of Bandwidth Adversely Affects Cloud Storage.
Possible solution:
Optimizing Storage Classes and Cloud CDNs.
Future of Cloud GIS Infrastructure
Cloud GIS infrastructures are rapidly developing with:
AI-based Image Analysis
Real Time Streaming of Satellite Imagery
Edge Computing for Geospatial Data Processing
Digital Twin Applications
Advanced 3D Geospatial Visualizations
Organizations adopting Cloud-based (COG or STAC) GIS infrastructures gain a Competitive Advantage for Scale, Analytics, and Operational Efficiency.
All modern organizations invested in geospatial operations must host their GIS imagery on a cloud infrastructure. Using a cloud infrastructure provides scalable storage, rapid delivery of imagery, global accessibility, and an overall reduction in cost for GIS operations.
Using technologies such as Cloud Optimized GeoTIFF, tile caching, object storage, and CDN acceleration, organizations can create high-performing geospatial infrastructures that will allow them to support large volume workloads or enterprise-level workloads.
If you wish to have great geospatial AI capabilities and a scalable GIS infrastructure, look at GeoWGS84.ai to host images, enable analytics, and facilitate improved workflow with respect to spatial intelligence.
For more information or any questions regarding GIS imagery, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
(A GeoWGS84 Corp Company)