Predicting building ages from LiDAR data with random forests for building energy modeling

Tooke, Thoreau Rory; Coops, Nicholas C.; Webster, Jessica Wagner

doi:10.1016/j.enbuild.2013.10.004

Cited by 58 publications

(33 citation statements)

References 24 publications

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“…However, their work does not consider the use of neighbourhood metrics, nor DSM data, which has become more widespread in recent years, as we propose in our work. Tooke et al (2014) describe the application of a predictive modelling to LiDAR data in order to infer building ages in Vancouver, Canada, also with the aim of supporting energy modelling applications. Their approach extracted spatial metrics, which are used within a random forests regression model, to predict building age.…”

Section: Related Workmentioning

confidence: 99%

Predicting residential building age from map data

Rosser

Boyd

Long

et al. 2019

Computers, Environment and Urban Systems

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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 modelling and simulation. We describe two major stages to achieving thisa 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 crossvalidated 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.

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Section: Related Workmentioning

confidence: 99%

Predicting residential building age from map data

Rosser

Boyd

Long

et al. 2019

Computers, Environment and Urban Systems

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“…Hence, regression techniques have been paid a greater attention to improve empirical models. Following this philosophy, LiDAR can currently be found for different tasks such as estimation of biomass in forest areas [6] or prediction of building ages [7].…”

Section: Introductionmentioning

confidence: 99%

A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

et al. 2015

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a b s t r a c tLight Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop those models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has emerged as a suitable tool to improve classic stepwise MLR results on LiDAR. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and regression techniques in machine learning (neural networks, support vector machines, nearest neighbour, ensembles such as random forests) with special emphasis on regression trees. The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results confirm that classic MLR is outperformed by machine learning techniques and concretely, our experiments suggest that Support Vector Regression with Gaussian kernels statistically outperforms the rest of the techniques.

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“…[21][22][23][24]. Also, the LiDAR is an effective remote sensing tool for measuring a three-dimensional coordinate by emitting a short laser pulse to the target object and analyzing the reflected light [25][26][27][28].…”

Section: Monitoring Phase Of a Building's Dynamic Energy Performancementioning

confidence: 99%

A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase

et al. 2015

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Predicting building ages from LiDAR data with random forests for building energy modeling

Cited by 58 publications

References 24 publications

Predicting residential building age from map data

Predicting residential building age from map data

A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase

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