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IBM releases Open Building Insights , Modelling Urban Growth for Africa
At the 29th United Nations Climate Change Conference of the Parties , COP29 , IBM and Sustainable Energy for All , SEforALL announced new , publicly available Artificial Intelligence , AI-powered solutions to inform more sustainable urban development for cities and communities around the world , enabling decision-makers and policymakers to map urbanisation and identify energy and infrastructure needs for communities in developing regions .
“ At IBM , we are proud to launch solutions that harness the power of Artificial Intelligence to have an impact for communities around the world ,” said John Matogo , Corporate Social Responsibility Leader for Africa and the Middle East at IBM .
Open Building Insights , OBI is an interactive online platform running on IBM Cloud . OBI visually consolidates data in a map , providing information related to buildings in countries addressing urban planning challenges , such as building location , height , footprint area , and usage type . This visual consolidation makes AI models ’ output easy to understand for non-technical users , and empowers stakeholders to make informed decisions about sustainable urban development .
OBI ’ s interactive map consolidates models created by the German Aerospace Centre , DLR , which estimates buildings ’ heights , by Open Energy Maps , which provides information about electricity status and consumption , and by IBM .
The brand-new AI model developed by IBM runs on IBM Cloud and was built using the IBM watsonx AI and data platform . It uses building-specific data – including its footprint , number of floors , roof image , location and other map data – to determine whether a building is residential or nonresidential . This categorisation is key to determining the energy needs of a certain urban area .
OBI is available for free to the public , containing information across all of Kenya , and is already being used in the country for energy planning . Based on information from the OBI platform , developed by IBM , Makueni County in Kenya obtained valuable insights to implement measures that are projected to benefit around 1,139,000 citizens by 2030 .
The IBM model and DLR model are also available through OBI for the state of Maharashtra in India .
Modelling Urban Growth , MUG is an opensource AI model designed to predict where cities will grow . The model is trained on , and validates , historical data from satellite images ; geographic data , such as slope and elevation ; demographic data ; and structural data , such as road layout , combining the data into a time series .
MUG helps users to map future urbanisation and associated infrastructure needs , enabling decision makers to prioritise communities and developing regions that need support for issues like electrification and energy services . MUG is an AI Alliance project , and is publicly available and opensource on GitHub .
The model is currently trained on data from Africa , including Nigeria , Benin , Togo , Ghana , Cameroon , Uganda , Kenya , Democratic Republic of the Congo , Tanzania , Rwanda , and Malawi . However , the model is designed to be re-trained by users for any country in the world , using publicly accessible data .
On GitHub , MUG includes an explanatory guidebook on running the code and making predictions using the same or different datasets , which further expands access to developers and decision-makers . p
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