Predicting residential building age from map data
The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/54994/ |
| _version_ | 1848799095659954176 |
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| author | Rosser, Julian F. Boyd, Doreen S. Long, G. Zakhary, Sameh Mao, Y. Robinson, D. |
| author_facet | Rosser, Julian F. Boyd, Doreen S. Long, G. Zakhary, Sameh Mao, Y. Robinson, D. |
| author_sort | Rosser, Julian F. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points. |
| first_indexed | 2025-11-14T20:30:13Z |
| format | Article |
| id | nottingham-54994 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:30:13Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-549942020-03-13T04:30:12Z https://eprints.nottingham.ac.uk/54994/ Predicting residential building age from map data Rosser, Julian F. Boyd, Doreen S. Long, G. Zakhary, Sameh Mao, Y. Robinson, D. The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points. Elsevier 2018-09-13 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/54994/1/Estimating_building_age_submission_Aug2018_accepted_pre-typesetting.pdf Rosser, Julian F., Boyd, Doreen S., Long, G., Zakhary, Sameh, Mao, Y. and Robinson, D. (2018) Predicting residential building age from map data. Computers, Environment and Urban Systems . ISSN 0198-9715 https://www.sciencedirect.com/science/article/pii/S0198971518300851?via%3Dihub doi:10.1016/j.compenvurbsys.2018.08.004 doi:10.1016/j.compenvurbsys.2018.08.004 |
| spellingShingle | Rosser, Julian F. Boyd, Doreen S. Long, G. Zakhary, Sameh Mao, Y. Robinson, D. Predicting residential building age from map data |
| title | Predicting residential building age from map data |
| title_full | Predicting residential building age from map data |
| title_fullStr | Predicting residential building age from map data |
| title_full_unstemmed | Predicting residential building age from map data |
| title_short | Predicting residential building age from map data |
| title_sort | predicting residential building age from map data |
| url | https://eprints.nottingham.ac.uk/54994/ https://eprints.nottingham.ac.uk/54994/ https://eprints.nottingham.ac.uk/54994/ |