Public and private sector leaders can improve operations to become more sustainable by sourcing raw materials more responsibly and by analyzing and mitigating climate risks to their organizations.
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One-stop Earth observation data that is curated and analysis ready, including 900+ curated geospatial datasets, including near-real-time satellite imagery.
Powerful computation platform at scale
A powerful tool to analyze and visualize Earth data at scale. Parallel processing for speed and scale, with machine learning built in.
50,000 sustainability-focused monthly active users (and growing). Join the rich user community focused on sustainability, social and environmental impact.
Google Cloud integration
Learn how to quickly get Earth Engine running through a Google Cloud project and environment of your choice using our quickstart guides.
Use client libraries to create and manage Compute Engine resources in Go, Python, Java, Node.js, and other languages.
Enable global supply chain transparency and traceability to footprint.
Understand climate risk exposure for operations and investments (for example, flood, wildfire, drought, etc).
Monitoring crop health and productivity, reducing pesticide and fertilizers, and evaluating the effectiveness of sustainable agriculture practices.
Enable sustainable forest management and monitor land cover change and climate events response.
|Data catalog with 900+ analysis-ready, geospatial datasets||Easy access to a wide variety of data including near-real-time satellite imagery and curated scientific datasets that allow you to rapidly test new hypotheses and quickly respond to requests.|
|Deep time series of data||Easy access to a long time series of data that will give you better insight into long-term change and variation. It also enables better predictions and risk assessment.|
|Processing speed||Fast production of results enables timely answers to critical questions, and faster responses to requests, hypothesis testing, and product development.|
|Processing scale||Ability to work with massive volumes of data to produce large-scale results and/or work with deep time series of data.|
|Fully managed service||No need to manage backend infrastructure, so you can focus resources on geospatial expertise instead of backend software development.|
|Code Editor||Web-based IDE coding environment focused on hypothesis testing enables you to test new hypotheses and quickly respond to requests.|
|Interactive applications||Create interactive experiences of analyses with Earth Engine’s library of UI components. Share geospatial tools with non-coders and deliver insights into the hands of decision-makers.|
|Active user ecosystem||Deeply knowledgeable community of users to interact with to help solve problems faster and apply best-in-class methods.|
|Integration with machine learning tools||Build geospatial ML models to derive better geospatial insights and spend less effort building data pipelines.|