The first integrated platform for global scale geospatial analytics.
Analyzing Imagery is hard enough.
simplifies the steps from pixels to insight.
Discover, prepare, and analyze satellite imagery instantly:
- Global catalog of free, publicly available, and analytics-ready imagery
- Visual analysis tools to inspect and compare scenes
Analyze rasters in a DataFrame to enable:
- Geospatial queries
- Horizontally scalable raster processing
- Distributed machine learning via SparkML
Build scalable analytics delivered through an accessible interface:
- Spatiotemporal queries
- Map algebra raster operations
- Spark ML algorithms
Automate analyses to reach a broader audience:
- No-to-low code environment
- Customizable tools
- Replicable models
Focus on insights.
We'll take care of the rest.
Enable your data scientists to unlock insights from EO data using Astraea's technology platform. We offer focused courses intended to get your team up to speed, as well as side-by-side mentoring and customized training sessions to focus on your specific use case.
Are you EO-Curious? Are you looking for a low-risk way to gauge the value of satellite imagery to your organization? Our Discovery and Prototype offerings are designed to determine the technical and business feasibility of your use case, from imagery sourcing to visualization, and build a Proof of Concept for further validation.
Need answers from imagery but don't have the time or team to get it done? Let our experienced team of data scientists and engineers build a bespoke solution to answer your specific question and deliver insights in the language your stakeholders speak. Our Deployment offering can include custom-built dashboards, web applications, and automated image processing designed to integrate your solution into your enterprise workflows.
Astraea brought the power of machine learning and high-resolution geospatial data, saving our team thousands of hours of manual search.
The energy industry is rapidly moving towards renewable resources, especially as environmentally-driven policies are making it affordable. Solar farms are developing faster than can be reliably recorded.
Using publicly available satellite data and advanced computer vision techniques, Astraea's data science team built an interactive web application that allows users to visualize and chart the growth of utility-scale solar farms. In many locations, we were able to identify solar farms not yet recorded in public databases.
Sun Tribe Solar engaged Astraea to generate new business leads by identifying commercial buildings with newly installed thermoplastic polyolefin rooftops, indicating solar opportunities for building owners.
Astraea’s team built a custom solution, powered by artificial intelligence. Using a semi-supervised model, Astraea was able to identify the roof material of over six thousand buildings across 12 counties in Virginia.
Combining machine learning analysis and high-resolution imagery, Astraea was able to save the Sun Tribe Solar team thousands of hours in manual rooftop labeling.
Monitoring changes in forests across large, inaccessible regions over extended time periods is difficult but essential for understanding the impacts forest loss and degradation have on climate change.
Earth-observation data provides a valuable source of information to conduct global, independent estimates of regional and national forest cover and deforestation.
Astraea’s data science team used over 2,000 global sites and public satellite data to build a machine learning model that measured year-over-year forest loss for the state of Mato Grasso, Brazil.