Research Grants to Manage and Model Earth Observation Data

Research Grants to Manage and Model Earth Observation Data

We like to take on tough technical challenges that advance the state-of-the-art and have the potential for positive civic and social impact.  So Azavea is honored and excited to have been awarded two Phase I Small Business Innovation Research (SBIR) grants from the U.S. Department of Energy and from NASA to address these challenges by building two different systems. Funding from the U.S. Department of Energy ($155,000) will enable the development of the Raster Foundry, a re-useable cloud-based platform for high-resolution imagery management and distributed computation. The NASA funding ($125,000) will enable the development of ModelLab to build, store, and execute complex geospatial models.

Advances in small satellite, drone, and sensor technology are revolutionizing the Earth observation industry. Many contemporary human challenges, including climate change, water quality and availability, watershed management, biodiversity loss, urban sprawl, energy needs, and human health, cannot be effectively understood or resolved without the kind of ongoing global monitoring these instruments can provide.  Indeed,  high-resolution raster imagery is being generated in greater amounts and at higher resolutions, providing the potential for real-time monitoring of both global and local change.  But the development of tools for storing and processing the burgeoning volumes of imagery, elevation, and other raster data presents significant technical challenges.  These two projects address these challenges in different ways.

The Raster Foundry will combine advanced user interface design and distributed computing techniques in an affordable, Software-as-a-Service (SaaS) solution that can rapidly process imagery data sets and help users derive the intelligence necessary to support an array of decision-making activities.  An intuitive user workflow will walk them through the process of uploading, mosaicking, merging, re-projecting, displaying, and storing Earth observation data from both satellite and aerial cameras.  They will also have the option of exporting their data for use in other applications.


 photo credits:

ModelLab, the subject of the second grant, will enable the design, storage, and execution of geospatial models and algorithms that use NASA and other Earth observation data sources.  These geospatial models can, for instance, be used for precision agriculture, to simulate flow patterns of tsunamis and floods, to forecast crime or seismic risk, to determine lines-of-sight for cell tower siting, to interpolate among field observations, to compare coastal erosion patterns over time, and to monitor the impact of forest fragmentation, among countless other applications. ModelLab aims to make these geospatial modeling capabilities available not only to trained geospatial professionals, but also to non-technical users in a broad range of public and private sector positions.

A Model Gallery will initially include geospatial models prepared by members of Azavea’s research team.  In particular, it will include modeling “recipes” prepared by Dr. C. Dana Tomlin, originator of Map Algebra.

The rapid increase in both imagery and other geospatial data has increased the complexity of creating responsive and scalable geospatial applications.  We are excited that both projects will be leveraging GeoTrellis, our open source high performance geoprocessing engine.  Based on the Scala language and the Spark framework, GeoTrellis provides the ability to process large and small data sets with low latency by distributing the processing across computing clusters, which is exactly what Raster Foundry and ModelLab will need to do.

The ModelLab project is supported by the NASA Small Business Innovation Research (SBIR) program, award number NNX15CS06P

The Raster Foundry project is supported by the U.S. Department of Energy SBIR program, award number DE-SC0013134