Several entities are offering open data access and many open-source geospatial solutions are available on coding platforms (see "Additional Resources" for examples).
For example, Google Earth Engine provides access to a variety of datasets including Landsat, Sentinel-2, MODIS, and more. This platform can be used for large-scale geospatial analysis and machine learning applications.
Google and the Food and Agriculture Organization (FAO) of the United Nations have launched Earth Map, a free, web-based Big Data tool that provides users worldwide with access to multidimensional maps and statistics on climate, environmental, and agricultural trends. Built on Google Earth Engine and FAO’s data, Earth Map enables users—even those without technical expertise—to analyse satellite imagery and geospatial data in near real-time.
Prerequisites:
• Capability to process EO data
• Procurement pathway
• Partner support as needed
• Data policy (open by default preferred)
• Cybersecurity, data protection, and continuity planning
Cost:
• Ballpark: USD 10k–100k/yr
• Includes: data storage, IT/cloud infrastructure, data analytics
Timeframe:
• Overall: 6–18 mo
• Phase: scoping & provider engagement (2–4 mo), integration & customization (3–6 mo), training & adoption (6–8 mo)
Pros:
• Cost Effectiveness - Improves allocation of financial resources, maximizing impact while minimizing waste
• Leveraging Open Resources - Provides comprehensive coverage useful for monitoring large-scale phenomena like climate change
• Fomenting Development - Fosters innovation by providing resources without financial constraints
Considerations:
• Limited customization - free data may not always meet user needs in terms of resolution, frequency or type of data
• Dependency - Reliant on external providers Security - Not suitable for uses requiring high security standards
Additional Resources:
https://newspaceeconomy.ca/2023/05/03/a-guide-to-free-earth-observation-imagery-sources/