SBIR Phase I: Automated Census of Street Trees from Public Imagery

Period of Performance: 02/01/2017 - 01/31/2018


Phase 1 SBIR

Recipient Firm

AEye Labs, Inc.
166 S Parkwood Ave Array
Pasadena, CA 91107
Firm POC, Principal Investigator


The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is that it will lead to more numerous and healthier city trees throughout the US. It will impact climate, energy, air and water quality and livability. This will be achieved through more effective and efficient management of the urban forest by municipalities, and through more and better organized citizen participation. Trees are a valuable asset for a community. Their benefits include: a reduction in energy use; improvement in air and water quality; increased carbon capture and storage; increased property values; and an improvement in individual and community well-being. To ensure maximum benefit and minimum cost, street trees need to be managed efficiently. In order to do this, municipalities need up-to-date inventories of trees. This Small Business Innovation Research (SBIR) Phase I project intends to automatically generate inventories of street trees from aerial and street-view imagery using cutting-edge computer vision and machine learning techniques. The inventory contains each tree's GPS location, its species, trunk diameter and an estimate of its health. The inventory is updated every time a new aerial or street-view image becomes available. Both tree detection and tree species classification are unprecedented applications based on combining the learning capabilities of deep convolutional networks, 3D geometry, and large annotated datasets that are collected by a combination of experts and crowdsourcing. This innovation makes it possible to maintain an always up-to-date, accurate, complete, US-wide street tree inventory at a fraction of the current cost.