Blacktown Council faced a dual challenge of improving road safety and mitigating summer heat waves by planting trees. The Council aimed to understand the impact of tree plantings on road safety and road surface temperature while identifying suitable tree planting locations.
The Council sought to assess road safety and implement tree planting while considering potential conflicts between the two goals. The project focused on a 3km stretch of Palmyra Rd and involved three components: IRAP road safety assessment, tree location suitability analysis, and road shading analysis.
The existing IRAP star rating for road safety was assessed in three scenarios: existing conditions, growth of recently planted trees, and growth of proposed trees. Mobile LiDAR data from Tomtom captured road features. Assumptions were made for missing data. The new trees minimally impacted the star rating, emphasizing the potential for nuanced assessment in the future.
Anditi’s coding methodology primarily uses three data sources for coding:
1. Mobile Lidar-derived point cloud where machine learning and AI algorithms are used to automatically detect features such as line markings, medians, poles, and tree trunks.
2. Panorama images that are acquired in addition during lidar capture are used to manually code features using Anditi’s IRAP coding tool. Panoramas are captured every few meters and when combined provide a video of the road.
3. Aerial imagery used in GIS to digitise features such as pedestrian crossing points and property access points
The suitability analysis aimed to identify locations for additional tree plantings without negatively affecting the IRAP star rating. Nature strip dimensions dictated tree species. GIS layers determined potential tree locations and distances from the road. Proposed trees were analyzed against existing roadside severity objects. Constraints were applied to ensure safe planting.
Shading analysis considered existing shading, recently planted trees, and proposed trees. The patented algorithm by Anditi generated shading polygons and calculated shading for different dates and times. Artificial trees were inserted into LiDAR data. Results showcased increasing shading with new and proposed trees. Notably, future projects could benefit from higher point density aerial LiDAR data and improved tree shape modelling.
IRAP star ratings showed promising safety levels, with the potential for minor impacts due to new tree growth. Tree location analysis identified 867 potential planting spots. Shading analysis depicted significant increases in shading with new and proposed trees, with potential for future scaling and solar irradiance analysis.
Improvements could include accurate species-specific digital trees and analysis of shading throughout growth stages. Scaling could use aerial LiDAR data to prioritise planting areas with low shading.
The integrated methodology of IRAP assessment, tree location suitability, and shading analysis addressed the dual challenges of road safety enhancement and heatwave mitigation through tree planting. The results highlight the potential to balance these objectives effectively, paving the way for future urban planning initiatives.
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