Advances in small satellite, drone, and sensor technology are revolutionizing the Earth observation industry. As a result, high-resolution raster imagery is being generated in greater amounts and at higher resolutions than ever before, providing the potential for real-time monitoring of both global and local change. Many contemporary human challenges, including climate change, water quality and availability, watershed management, biodiversity loss, urban sprawl, energy needs, disaster recovery, public health, and humanitarian relief efforts, cannot be actively explored or addressed without a strong reliance on remotely sensed data. The increasing availability and falling cost of this data has created the need for advanced software tools that can help both public and private sector organizations to access, analyze, edit, and apply it more effectively in ways that will support their missions.
The Raster Foundry project addresses this need by providing an entirely new type of user experience that makes raster processing capabilities available over the web, not only to GIS analysts and other trained geospatial professionals, but also to non-technical users in organizations ranging from local governments and humanitarian groups to nonprofits and utility firms.
When developing Raster Foundry, Azavea focused on the issues of computer processing time, data storage capacity, and user interface design that have long been a significant barrier to the widespread access and use of remotely sensed data by many decision-makers. At the core of the application is GeoTrellis, Azavea’s open source high performance geoprocessing engine. Based on the Scala language and the Apache Spark project, GeoTrellis provides the ability to rapidly process large geospatial datasets by distributing the processing across multiple computing clusters.
The development of the Raster Foundry project is supported by the Small Business Innovation Research (SBIR) program of the U.S. Department of Energy, Office of Science, Award Number DE-SC0013134. An early prototype, completed in 2015, will be built out over the next two years to support data processing for long-range planning initiatives in communities around the world.