Latest in Machine Learning for Tree Inventories.pdf
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The document discusses advancements in using machine learning to enhance tree inventories, detailing a shift from traditional methods to more efficient, technology-based approaches. It outlines specific processes, such as using aerial imagery and Google Street View, to estimate tree counts and monitor their conditions over time. Additionally, it highlights the challenges faced in tree assessments, the importance of maintaining updated inventories, and the benefits of implementing urban forestry programs.
Latest in Machine Learning for Tree Inventories.pdf
- 1. The Latest In Utilizing Machine Learning to Complete Tree Inventories Josh Behounek
- 3. “Solutions Through Innovation and Expertise”
- 4. 1931 Pen & Paper Computer 1992 GIS-based 2002 Machine learning 2022
- 7. Tree Inventory vs Assessment Assessments Google Streets DRG Standard LiDAR
- 11. Buffalo, NY Inventory Update 2001 2014 Difference Sites 124,445 127,080 2,635 Total DBH 871,173” 817,627” -53,546” Average DBH 7” 6” -1” # Species 281 247 -34 # Removals 668 2,707 2,039 # Planting Sites 48,761 44,619 -4,142
- 12. Top 5 Species Still Poor Recommended for Removal 45, 43% 24, 23% 20, 19% 9, 9% 6, 6% MAPLE, NORWAY MAPLE, SILVER LINDEN, LITTLELEAF HORSECHESTNUT, COMMON CHERRY, KWANZAN 162 total trees
- 13. Condition Change Assessment 2001 Inventory 2014 Inventory 5,698 Poor 3 Dead 145 Poor 11 Fair 0 Good 293 Plant 38,199 Fair 298 Dead 3,445 Poor 26,830 Fair 962 Good 1,365 Plant 25,632 Good 259 Dead 717 Poor 8,952 Fair 9,783 Good 1,878 Plant
- 14. 3 Types of Machine Learning & Tree Inventories ✔ Nothing ✔ Inherited ❏ Current
- 15. Machine Learning Process with Google Streets Geo-localization of tree canopy (Step 1) ● Aerial imagery is used to identify where trees are. ● Canopy pixels are extracted and vectorized to define the boundary called the tree canopy zone. Estimating tree count (Step 2) ● Within the tree canopy zone, street view imagery is used to find the trees under street view Estimating distance from observer (Step 3) ● A heat map is generated that defines the distance of each pixel from the observer. ● Using this, the average distance of tree pixels is calculated within the bounding box extracted in step 2 Identifying location of individual trees (Step 4) ● Observer location and field of view is projected in aerial view (the right angle in blue above) ● Using the distance calculated in step 3, individual trees are placed on aerial image map (yellow points). Photo credit - SiteRecon
- 16. 1. No Idea of Number of Trees to be Inventoried
- 17. Scenario 1: No Idea # of Trees – Tucson, AZ
- 22. 2. Inherited
- 23. Inherited - San Diego Results 26% Vacant Sites ~ 436,770 Total Sites
- 28. Utilizing Point Data 1. Number of Trees 2. Location of Trees 3. TreeKeeper Software 4. Tree Equity 5. Pruning Cycles 6. Planting Locations 7. Updating
- 30. Implementing an Urban Forestry Mapping & Monitoring Program
- 31. Advantages Photo credit - greehill
- 32. Implementing Tree Monitoring Program Year 1 Initiate tree monitoring program Perform advanced assessments Install TreeKeeper 9 Year 2 Implement information via TreeKeeper 9 Year 3 Perform tree monitoring data collection Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 5 Perform tree monitoring data collection Perform advanced assessments of flagged trees Perform change analysis Update TreeKeeper 9 Year 4 Implement information via TreeKeeper 9 Photo credit - greehill
- 33. Initial assessment greehill drives streets & parks per contract specs Data Delivery Data is delivered into TreeKeeper 9 with API to greehill software Data extraction Data is processed via machine learning to provide information per data specs. Advanced Assessments Davey provides Level 2 assessments to flagged trees. Outlier Trees Based on results of data, client goals, & budget a certain # of trees are identified for advanced assessments Tree Monitoring Program Operation workflow
- 39. Corridor clearance Human Thermal Comfort
- 40. Machine Learning Advantages ● Objective ● Repeatable ● Efficient ● Precise
- 41. “You do not rise to the level of your goals, you fall to the level of your systems.” Atomic Habits by James Clear
- 42. The Latest In Utilizing Machine Learning to Complete Tree Inventories Josh Behounek 573-673-7530 Josh.Behounek@davey.com