NASA SBIR 2019-II Solicitation

Proposal Summary

 19-2- S5.03-4162
 Bridging the Gap of Applying Machine Learning to Earth Science
 Multi-Resolution Deep Learning for Land Use Applications
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
GeoVisual Technologies, Inc.
9191 Sheridan Boulevard, Suite 300
Westminster, CO 80031
(720) 323-3399

PRINCIPAL INVESTIGATOR (Name, E-mail, Mail Address, City/State/Zip, Phone)
Jeffrey Orrey
1215 Spruce St. Suite 201
Boulder, CO 80302 - 4257
(303) 955-1575

BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Jeffrey Orrey
1215 Spruce St. Suite 201
Boulder, CO 80302 - 4257
(303) 955-1575

Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 6
Technical Abstract (Limit 2000 characters, approximately 200 words)

We propose to develop and commercialize a deep learning-based image classification capability that detects fine-scale and rapidly changing land surface features, using relatively low resolution and low-cost imagery and an architecture that is simple and fast to train. The proposed system promises to substantially improve the study of high frequency land cover dynamics in heterogeneous landscapes by addressing two principal roadblocks to higher spatial resolution and more frequent land cover classification: 1) the high cost of acquiring high resolution multispectral imagery on a frequent basis, and 2) the general complexity of using machine learning techniques to improve classification capabilities. Our innovation involves using time series of multispectral imagery with relatively rich spectral content as a trade-off with spatial resolution, and applying it on a pixel by pixel basis. Our Phase II focus will be on agricultural areas that frequently change on a small scale. Annual vegetable crops are a key set of relevant land cover classes. But our methodology is extensible to other land cover types, such as urban settlements and their change, and other data inputs in addition to imagery, such as time series of weather data.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

Related follow-on opportunities for NASA program infusion include integration with the TOPS-SIMs irrigation management program at the Ecological Forecasting Lab at NASA Ames, and NASA Goddard’s Harvest Consortium led by the University of Maryland to enhance the use of satellite data in decision making related to food security and agriculture, and the Surface Biology and Geology (SBG) Decadal Designated Observable Study.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

Related commercialization opportunities include monitoring and forecasting for industrial agriculture, particularly for fresh vegetable crops, improved cropland classification for USDA’s Cropland Data Layer, and food waste and sustainability applications addressing prioritized actions of the EPA, USDA and FDA.

Duration: 24

Form Generated on 05/04/2020 06:34:25