2021, Jose Delpiano, PhD, University of the Andes, Co-PIs Dr. Mauricio Ponce, Dr. Jose Saavedra, Dr. Matias Recabarren
There are over 100 landscape and tree fatalities In America every year. To name one, New York City had to award $1.6 million due to a citizen being killed by a falling tree branch in 2003. Hazard tree identification and assessment is key to prevention of accidents related to urban trees. Individual arborists are faced with the responsibility for areas with a large number of trees and are very often unable to identify and assess all the hazard trees. We believe some of the most relevant defects in hazard trees can be detected in an automated manner with artificial intelligence (AI) tools. We will determine which of them can be detected from photographs of tree parts and street-level photographs, taken with mobile devices. To train the AI models we will design, a new set of tree images will be collected. A few images are required to assess one tree: even for an expert arborist, a global photograph is not sufficient and pictures of tree parts like leaves, bark, and flowers may be needed. We will test our AI models in real urban environments.
Funding Duration: 2 years
Grant Program: John Z. Duling Grant
Grant Title: Computer Vision for Hazard Tree Identification and Assessment
Researcher: Jose Delpiano, PhD
Peer Reviewed Publications from Grant:
General Audience/Trade Publications:
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