Climate , energy and cities
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Using machine learning , the heat reduction app helps users identify design inefficiencies that can cause urban overheating . Images : Daniel Yu .
Using machine learning to curb waste and urban heat for cleaner , smarter cities
The problem
Waste and urban overheating are two of today ’ s most pressing global issues . In 2018-19 , Australia generated an estimated 27 million tons of construction and demolition waste , 44 % of the national total waste . Australian cities are experiencing unprecedented levels of heat due to human activity . This has an adverse effect on health , energy and the economy .
Our solution
More on this story
Computational design and digital technologies , under the framework of digital sustainability , can enable construction and urban planning focused on the UN Sustainable Development Goals , says Associate Professor M . Hank Haeusler from UNSW ’ s School of Built Environment . By leveraging machine learning , architects and planners can optimise designs for environmentally responsible construction and safer , more resilient cities .
M . Hank Haeusler is a researcher , designer and entrepreneur who specialises in computational design and digital technologies , including AI and machine learning , digital and robotic fabrication , and smart cities . He works extensively with industry partners and is the director of the ARC Centre for Next-Gen Architectural Manufacturing .