2212767

Sbir Phase I: Smartphone-Based Machine Learning and Computer Vision for Cost-Effective Verification of Forest Carbon Offsets -The broader impact of this Sbir Phase I project is to make the gathering of tree measurements and other field-based nature observations dramatically easier. The project involves building a mobile app that uses computer vision and augmented reality to make measuring trees much easier than using the tape measures of today, in fact it will be as simple as scanning a barcode.

This will result in much more nature imagery and other training data for use with ecological models, which will yield a better understanding of how ecosystems will change in the coming years. One important prediction this project will help with is forest growth which is very useful for generating carbon offsets that can finance the regeneration of large amounts of land. Better ecological models can also help communities prepare for changes in climate, sea level, soil quality and other ecological shifts in the coming decades.

Millions of people will be potentially affected by climate change and its accompanying shifts in ecosystems, so arming communities and governments with better insights about these impacts will be critical. Furthermore, catalyzing many millions of hectares of nature regeneration projects will help mitigate the worst effects of climate change and other ecological challenges.

This project involves the unique combination of computer vision with augmented reality in order to quickly and accurately measure trees. There are multiple computer vision neural networks being developed for the app. One of them uses bark and tree imagery in order to classify the tree species, an important feature needed for ecological models. Another model analyzes surrounding scenery and quickly identifies the closest tree trunk. The app then uses the phone's augmented reality capabilities to gauge the distance and orientation of the phone from the trunk and combines these two to yield a diameter at breast height measurement.

The research plan involves collecting data from specific regions in Panama and Brazil in conjunction with active nature restoration projects and gathering a critical mass of leaf and bark imagery so that species in those areas can be classified accurately. The scope will then increase to additional regions and later, measurements such as birdsong and other biodiversity markers.

Over time the app could become a platform for many such nature observations, and it is expected to evolve to become more game-like to attract large numbers of people having fun experiences together and advancing the cause of nature science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.

Earthshot Labs Pbc

47.084 - NSF Technology, Innovation, and Partnerships

National Science Foundation (NSF)

New York, New York 10013-2461 United States

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