Client: Christchurch City Council
Roof runoff is an important source of urban stormwater contamination in cities with the material and condition of roofs both impacting the quality of runoff entering the stormwater network.
Christchurch City Council worked with AI provider Lynker Analytics (Lynker) to develop an automated and explainable Machine Learning model to predict roof material and condition across the city.
Christchurch City residential roof tops. Photo credit: Hannah Currie
Lynker initially used a deep learning model trained on high resolution aerial photography captured in 2020 and 2021 to classify roof material e.g. coloursteel, galvanised, metal tile, non metal and predict their condition e.g., good, average, poor. This model was trained on around 1000 buildings, and its accuracy was measured against field captured data.
Examples of roof materials used in the modelling
An ensemble model was then developed using Explainable AI to combine predictions from both the deep learning model with property level data held by the Council, such as age, roof material at construction date and property type.
A supplementary study was also conducted to determine the proportion of zinc-coated roofs on industrial buildings and break down the sub class for all metal roofed buildings to include Coloursteel and Galvanised.
Example of an area with different roof materials. Photo credit: Hannah Currie
This approach allowed the Council to identify and understand the most informative attributes and data interactions needed to make the best overall prediction for each roof.
Results from the deep learning computer vision model could be weighed against the information maintained on the property by Council. Metal roofs which are generally the main source of contaminants were then assigned a decay index value with a higher value indicative that the roof is in poor condition.
Across the city, around 10% of buildings were found to have a poor condition roof with the best result achieved using an ensemble model which combined the deep learning and explainable models.
The global explanations provided by Explainable AI described the most important contributing factors across all properties alongside the factors impacting local (rooftop scale) explanations.
Deliverables
City-wide data set describing roof type and condition
Technical Memo explaining method and results