“We have focused on an incredibly high degree of accuracy in our models, so you get results you just can’t get by hand-labelling, especially with more than a million images worth of labelled data,” said Mike.
The Future of AI Feature Classes
Enhanced feature classes help guide AI detection, with each AI pack considering multiple layers. For example, to label swimming pools the model may include attributes such as above- and below-ground swimming pools, empty pools or pools under construction, spas, hot tubs and paddling pools.
“If you do it right, the machine learning model can actually learn all those nuances,” said Mike. “But it can only do that if the humans [labellers] are consistent with those nuances.”
To see how Nearmap defines feature classes — such as swimming pools — visit our Knowledge Hub.
Consistency and Alignment
Because Nearmap AI is aligned with Nearmap aerial imagery, the AI attributes align perfectly with the vector map, removing many of the challenges associated with getting different geospatial data sets to line up — both spatially, and for a particular point in time.
“A lot of local governments are trying to move from hand-digitised maps, that are really accurate, but are hard work and quickly become out of date,” said Mike.
For local government agencies and councils — like the City of Ryde in Sydney — the ability to see how areas have changed over time, comparing variations in vegetation cover and non-porous spaces (asphalt, concrete), helps inform better planning decisions to predict and manage urban heat islands.
To keep up to date and identify what’s happening in a local area, at scale, Mike says the only way to achieve that is with automation.
AI and Automation
The focus of machine learning is that instead of a human adjusting the algorithm and writing a program, it’s programmed through the data. “My solution is not to fiddle with the algorithm,” said Mike. “The solution is to label more data in that scenario so that the model can learn from it.”
For those who have been working with algorithms for a long time, that is a big shift in thinking.
Emphasising the importance of good data, Mike summarises: “Data at massive scale with machine learning always beats a hand-rolled algorithm. Clean labelled data is the absolute key – and then you can train a model and run that at scale.”
Want to learn more about Nearmap AI? Watch the Nearmap AI Workshop presented by Dr Michael Bewley at Nearmap NAVIG8 2021.
Learn more about accessing deeper insights with Nearmap AI.