Transportation engineers and urban planners often rely on terrain mapping to manage infrastructure assets. However, traditional manual collection methods can be cost prohibitive depending on the size and location of the area to be surveyed. Remote sensing technologies, such as LiDAR, are a cost-effective alternative to manual collections.
The City of San Diego is the eighth-largest city in the United States and the second largest in California, with a population of 1.3 million. The City’s aggregate tree canopy provides significant contributions to the quality of life for residents and visitors, because trees make a vital and affordable contribution to the sense of community and create pedestrian-friendly neighborhoods. San Diego is committed to increasing the amount of trees in the City's urban forest to help the City meet the goals of the City’s Climate Action Plan
by continuing to expand the City's tree canopy. A healthy forest is essential to increasing tree canopy cover, and technology can bridge the gap between what an expert arborist can observe and record in the field and what an arborist cannot observe in our tree’s canopy.
Transportation and Storm Water Departments seeks a solution that would deliver the following outcomes:
- Develop a data gathering system to capture a specific City asset (trees, road conditions, traffic lights, fire hydrants, bus stops) using computer visioning and/or other methods.
- Ability to perform post processing of data/image to categorize tree genus and health condition using computer vision and machine learning.
- Develop a predictive model to assess and prioritize tree health conditions and identify potential ‘hot spots’ caused by known disease(s) and/or other form(s) of deterioration.
- Ability to accurately and efficiently transfer the data into a GIS based map layer.