What methods can be developed to reduce carbon emissions in urban environments? This is what a recent study published in Automation in Construction hopes to address as an international team of researchers investigated how machine learning could be used to develop methods for reducing carbon emissions in urban environments. This study has the potential to help researchers, climate scientists, legislators, and the public better understand tools that can be used to mitigate climate change and how they are implemented.
For the study, the researchers introduced a new AI-based software tool called EcoSphere, which is designed to evaluate carbon emissions and the financial burden it has on urban environments. To demonstrate its capabilities, the researchers used EcoSphere to evaluate the carbon footprint and financial burdens from a myriad of building construction methods and policies. Users can run simulations based on a single building or a collection of buildings to estimate financial costs and emissions for both the short and long term.
“EcoSphere uses machine learning not just to process these large datasets and imagery — but to understand it,” said Dr. Matthew Sisk, who is the Co-Director of Civic-Geospatial Analysis and Learning Lab and Associate Professor of the Practice (GIS and Data Science) at the University of Notre Dame and a co-author on the study. “By combining computer vision, geospatial analysis and large language models, we can generate detailed carbon profiles in real-time for entire cities, making sustainable urban planning faster, smarter and more accessible.”
The researchers aspire to use EcoSphere to improve carbon emission estimates and the financial burden they cause to cities and communities.
How will EcoSphere help reduce carbon emissions in urban environments in the coming years and decades? Only time will tell, and this is why we science!
As always, keep doing science & keep looking up!
Sources: Automation in Construction, EurekAlert!