Building occupancy type classification and uncertainty estimation using machine learning and open data
Federal and local agencies have identified a need to create building databases to help ensure that critical infrastructure and residential buildings are accounted for in disaster preparedness and to aid the decision-making processes in subsequent recovery efforts. To respond effectively, we need to...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225000020/type/journal_article |
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| author | Tom Narock J. Michael Johnson Justin Singh-Mohudpur Arash Modaresi Rad |
| author_facet | Tom Narock J. Michael Johnson Justin Singh-Mohudpur Arash Modaresi Rad |
| author_sort | Tom Narock |
| collection | DOAJ |
| description | Federal and local agencies have identified a need to create building databases to help ensure that critical infrastructure and residential buildings are accounted for in disaster preparedness and to aid the decision-making processes in subsequent recovery efforts. To respond effectively, we need to understand the built environment—where people live, work, and the critical infrastructure they rely on. Yet, a major discrepancy exists in the way data about buildings are collected across the United SStates There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale. We demonstrate how existing open-source datasets can be spatially integrated and subsequently used as training for machine learning (ML) models to predict building occupancy type, a major component needed for disaster preparedness and decision -making. Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. The 100-year flood in North Carolina is provided as a practical application in disaster preparedness. |
| format | Article |
| id | doaj-art-06da83cc618c4ccb9c88c65855b71f6f |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-06da83cc618c4ccb9c88c65855b71f6f2025-02-10T11:56:33ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.2Building occupancy type classification and uncertainty estimation using machine learning and open dataTom Narock0https://orcid.org/0000-0002-9785-4496J. Michael Johnson1Justin Singh-Mohudpur2Arash Modaresi Rad3Center for Natural, Computer, and Data Sciences, Goucher College, Baltimore, MD, USALynker, Boulder, CO, USALynker, Boulder, CO, USASchool of Computing, Boise State University, Boise, ID, USAFederal and local agencies have identified a need to create building databases to help ensure that critical infrastructure and residential buildings are accounted for in disaster preparedness and to aid the decision-making processes in subsequent recovery efforts. To respond effectively, we need to understand the built environment—where people live, work, and the critical infrastructure they rely on. Yet, a major discrepancy exists in the way data about buildings are collected across the United SStates There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale. We demonstrate how existing open-source datasets can be spatially integrated and subsequently used as training for machine learning (ML) models to predict building occupancy type, a major component needed for disaster preparedness and decision -making. Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. The 100-year flood in North Carolina is provided as a practical application in disaster preparedness.https://www.cambridge.org/core/product/identifier/S2634460225000020/type/journal_articleBayesian neural networkbuilding type classificationflood riskmachine learningopen data |
| spellingShingle | Tom Narock J. Michael Johnson Justin Singh-Mohudpur Arash Modaresi Rad Building occupancy type classification and uncertainty estimation using machine learning and open data Environmental Data Science Bayesian neural network building type classification flood risk machine learning open data |
| title | Building occupancy type classification and uncertainty estimation using machine learning and open data |
| title_full | Building occupancy type classification and uncertainty estimation using machine learning and open data |
| title_fullStr | Building occupancy type classification and uncertainty estimation using machine learning and open data |
| title_full_unstemmed | Building occupancy type classification and uncertainty estimation using machine learning and open data |
| title_short | Building occupancy type classification and uncertainty estimation using machine learning and open data |
| title_sort | building occupancy type classification and uncertainty estimation using machine learning and open data |
| topic | Bayesian neural network building type classification flood risk machine learning open data |
| url | https://www.cambridge.org/core/product/identifier/S2634460225000020/type/journal_article |
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