In: Stilla U, Gamba P, Juergens C, Maktav D (Eds) JURSE 2011 - Joint Urban Remote Sensing Event --- Munich, Germany, April 11-13, 2011
Classification of Residential Building Architectural Typologies using LiDAR Thoreau Rory Tooke1, Michael vanderLaan2,3, Nicholas Coops1, Andreas Christen2 and Ronald Kellett3 1
Forest Resources Management, 2Department of Georgraphy, 3 School of Architecture and Landscape Architecture University of British Columbia Vancouver, Canada Email:
[email protected] features in cities. Specifically, LiDAR has the ability to provide a wealth of building morphological characteristics that can be used as inputs for predicting the age, type, and ultimately the energy demand of buildings.
Abstract— The availability of light detection and ranging (LiDAR) datasets over urban areas has significant potential to facilitate the automatic parameterization of sophisticated building energy models. In this paper we present an approach to building architectural typology classification using LiDAR data and decision tree regression. By integrating a suite of LiDAR derived building morphological characteristics with field training data we accurately classifed (84%, Kappa = 0.76) of the modelled residential building types. Furthermore, our analysis suggests that building characteristics related to height, volume and roof slope provide the most important predictor variables for classifying building typologies in the examined study area.
Using LiDAR data Neidhart and Sester [4] present an approach for predicting the specific heat demand of buildings using volume to inform building type and indicate that additional metrics may improve typology classification. Therefore, the objective of this paper is to extend the approach presented by Neidhart and Sester [4] by reviewing the utility of numerous building structural attributes for the parameterization of building energy consumption models. Our approach examines the application of a decision tree classification technique to classify building architectural typologies using a suite of morphological characteristics generated from LiDAR data.
I. INTRODUCTION Buildings account for up to 40% of the total energy consumption of developed countries. The majority of this demand originates from the residential sector with a substantial portion of energy usage attributable to thermal comforts, with heating, ventilation and air condition (HVAC) and domestic hot water (DHW) systems contributing to 70% of North America’s building energy demand. To better understand the relationships between energy consumption and building form, a number of energy models have been developed [1] that can then aid in designing initiatives to manage energy more efficiently.
II.
STUDY AREA
The study site for the research conducted in this paper covers a 4km2 area in south central Vancouver, Canada. The majority of buildings in this area are single-family residential houses with a density of 12 dwelling units hectare-1. Approximately 37% of dwellings in this area were built before 1965, 38% between 1965 an 1990, and 25% after 1990. With regards to energy consumption, the majority of single-family homes in the area are heated using natural gas furnaces.
Building energy performance has been shown to be dependent upon a range of criteria including geometry, building design, systems efficiency and occupant behaviour [2]. Since different factors determine each of these criteria, modelling energy demand for individual buildings presents a challenging task. Rylatt et al. [3] indicate that many of the energy demand model parameters have a spatial component such as municipal zoning data, census data and building morphological characteristics that can be exploited to facilitate automation using a Geographic Information Systems (GIS) based approach.
III.
DATA AND METHODS
A. LiDAR Airborne LiDAR data was acquired by Terra Remote Sensing using a TRSI Mark II discrete return sensor attached to a fixed wing platform. The sensor was configured to record first and last returns and ground and non-ground hits were classified using in-house classification techniques. The average pulse spacing equaled 1 laser return 0.7m-2. First return, last return and ground return layers were interpolated and gridded to individual 1m spatial resolution raster images using a natural neighbour interpolation algorithm [5]. The area surveyed covers 4km2 over a low-density residential neighbourhood in Vancouver, Canada.
While spatial data has been incorporated into many of the current building energy models, the direct integration of remote sensing data remains a comparatively underutilized technology. While the moderate spatial resolution (30m) of traditional remote sensing technologies has prohibited an effective characterization of the heterogeneous composition of the urban surface, emerging remote sensing technologies including light detection and ranging (LiDAR) now enable accurate and automated methods for classifying many of the resolute
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In: Stilla U, Gamba P, Juergens C, Maktav D (Eds) JURSE 2011 - Joint Urban Remote Sensing Event --- Munich, Germany, April 11-13, 2011
Figure 1. Typical building architectural typologies represented in the study area: a) type 1, b) type 2, and c) type3.
B. Field Training Data During January and February 2010 a field campaign was performed to determine the building type of 264 houses across the area for training and validation of the classification methodology. This classification defines archetypal characteristics to classify residential buildings into typologies each of which facilitates the determination of the presence of common characteristics including construction, system efficiency, type, and energy intensity. Ultimately these characteristics enable the parameterization of numerous building energy models, including a commonly used Canadian model (HOT2000).
buffer, and 3) the removal of structures not intersecting a 25m street buffer. After secondary buildings were removed a polygon simplification algorithm was applied [7] to smooth the pixilation artifacts from the raster building extraction technique. Finally, buildings were separated according to lot parcels by attributing municipal zoning data and splitting contiguous structures on lot boundaries where necessary (Fig. 2). D. Building Morphological Characterization Once building polygons were indentified, a suite of building morphological features was derived to inform the building typology classification. The basic attributes include the area, volume and height of each building and are extracted from the first return LiDAR layer using the building footprints as the bounding areas for analysis. Furthermore, height is summarized by mean, maximum and standard deviation. Using the area measured for the building and the area of each lot parcel a building footprint to lot area ratio is calculated, based on the concept that more recent buildings tend to maximize available space.
Three typologies were developed to cover the building variation observed within the study area. The first typology (type 1), houses typically constructed before 1965, is characterized by low complexity roofs with high slopes, large variations in height, low building footprint to lot area ratios and low volumes with no attached garages (Fig. 1a). The second typology (type 2), houses typically built between 1965 and 1990, is characterized by simple low slopping gable roofs, medium heights typically with two stories slab, maximized building to lot area ratios and large volumes with attached garages or drive-under parking spaces (Fig. 1b). Finally the third typology (type 3), houses typically built after 1990, is characterized by complex medium sloped roofs, maximized heights typically with two levels and a full basement, maximized building footprint to lot area ratios and large volumes (Fig. 1c).
The more sophisticated morphological metrics relate to the shape, complexity and exposure of the buildings. Roof slope is generated from the LiDAR since it has been recognized as important characteristic for differentiating the year in which a building is constructed. The slope of each pixel is generated using the first return LiDAR layer with established algorithms [8]. Roof slope for each building is then categorized into low, moderate and steep by determining the percentage of the roof below 20º, between 20º and 30º and greater than 30º. Once slope is generated, external surface area can be extracted for each building using equation 1.
C. LiDAR Feature Extraction The procedure used to extract buildings from the LiDAR data follows the techniques of Goodwin et al. [6]. This procedure used the second return raster layer to determine the location of vegetated features, and subtracted these features from the first return layer. Height thresholds and connectivity analysis were then applied to extract buildings. After all building structures were extracted from the LiDAR data, a set of post-processing steps were required to: remove non-primary dwellings such as garages and sheds; smooth building edges; and split contiguous buildings into individual units.
(1) where c is the cell size and s is the slope in degrees of the pixel i of n pixels comprising the building. External surface area provides a measure of the building envelope by correcting for the three-dimensional form of a pixel represented in the twodimensional space of a digital elevation model. External surface area is also used to generate a shape factor, referred to as compacity, using equation 2 [9].
To remove non-primary dwellings all buildings were converted to a polygonal layer to which three Boolean operations were applied. These operations included: 1) the removal of structures less than 50m2 in area, 2) the removal of structures less than 75m2 and intersecting a 4m alleyway
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In: Stilla U, Gamba P, Juergens C, Maktav D (Eds) JURSE 2011 - Joint Urban Remote Sensing Event --- Munich, Germany, April 11-13, 2011
IV. RESULTS Table 1 indicates the range in values for morphological characteristics associated with each building type from the training dataset. The variability across building types helps to highlight the necessity of an automated statistical approach to building typology classification. In this paper we apply decision tree regression with all of the predictor variables listed in Table 1 to classify three residential building types. The overall accuracy of the classification is assessed at 84.4% with a Kappa coefficient of 0.76 (a Kappa of 1 indicates a perfect agreement after correcting for the probability of chance [11]). A confusion matrix provides further detail of the sources of error within the model. The most accurately classified building typology is type 2 resulting in a producer’s accuracy of 91.1% followed by type 1 at 85.7% and type 3 at 73.6% (Table 2). Results from the confusion matrix indicate that the lower accuracy of type 3 is associated with referenced buildings of this type committed to type 2 in the classification.
(2) where v is the volume of the building. Compacity defines the amount of exposed building per unit volume and has been used by Salat [9] and Ratti et al. [2] to assess potential heat loss. The higher the compacity value the more a building is exposed to the outside temperatures, effectively reducing energy efficiency. An important parameter for estimating energy consumption, we include compacity in the analysis to determine if it is also relevant for defining building types. E. Decision Trees Decision trees offer quick processing requiring minimal computational resources, an ability to process data measured at different scales and no assumptions are made concerning the frequency distribution within the data [10]. The basic process of a decision tree construction functions by repeatedly dividing a set of training data into increasingly distinct subsets based on tests to one or more of the measured values associated with the data. Once a set of hierarchically structured rules (branches) is produced they can be applied to a larger dataset to separate the data into relevant classes.
TABLE I.
RANGE IN VALUES FOR BUILDING MORPHOLOGICAL CHARACTERISTICS
Building Architectural Typology
Morphological Characteristic Area (m2) Volume (m3) Mean height (m) Max height (m) Height standard deviation Building-lot area ratio Low roof slope (%) Moderate roof slope (%) Steep roof slope (%) External surface area (m2) Compacity
The analysis in this study uses the building architectural typologies collected in the field as the target variable for the decision tree analysis and the LiDAR derived building morphological characteristics as continuous predictor variables. Single trees for each hierarchical level of building type are developed in DTREG using a 10 V-fold cross validation technique that has been demonstrated to produce accurate results without requiring an additional independent dataset to assess the accuracy of the model [10].
Type 1
Type 2
Type 3
56-195 333-1122 3.1-6.3 4.7-11.7 0.5-2.3 0.09-0.52 3-83 1-60 4-67 145-516 2.6-6.2
71-228 446-1429 4.3-7.2 5.2-8.2 0.2-2.4 0.17-0.56 14-94 0-56 1-46 128-603 2.2-4.9
67-188 354-1123 3.7-8.0 5.2-9.4 0.6-2.5 0.18-0.52 11-82 9-62 1-47 243-502 2.4-5.3
Results from the decision tree also provide an indication of the importance of the contribution that each variable provides to the model. Importance scores are produced by assessing how the predictor variables were used as primary or surrogate splitters and are scaled to a value of 100. In this analysis the most important variable for generating the decision tree model is the mean building height (100) followed by percent of roof slope between 20º and 30º (moderate slope) (98.7) then building volume (32.9) and finally maximum building height (30.8). TABLE II.
CONFUSION MATRIX FOR DECISION TREE CLASSIFICATION RESULTS
Classification data Reference data Type 1 Type 2 Type 3 User’s Accuracy
Type 1
Type 2
Type 3
78 5 7 86.6%
7 92 12 82.9%
6 4 53 84.1%
Producer’s Accuracy 85.7% 91.1% 73.6% 84.4% ( Kˆ = 0.76)
V. DISCUSSION Examining the results of the confusion matrix and the importance of the predictor variables highlights important considerations from this analysis. From an initial suite of 13 building morphological characteristics, only four metrics are
Figure 2. Extracted building footprints a) using the techniques of Goodwin et al. [6] and b) after post-processing to: clean pixilation artifacts, remove nonprimary buildings, and split contiguous structures into individual units.
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In: Stilla U, Gamba P, Juergens C, Maktav D (Eds) JURSE 2011 - Joint Urban Remote Sensing Event --- Munich, Germany, April 11-13, 2011
used to produce the final decision tree model. These four metrics relate to three simple structural attributes of height, volume and roof slope. The simplicity of these metrics is important to consider, as it suggests that computational time and resources need not be spent on generating more advanced metrics such as surface area and compacity for the classification of building architectural typologies. Nonetheless, these more complex metrics of building structure have been demonstrated to provide effective parameters for more direct energy predictions [2,9].
buildings according to architectural typologies. Results indicate that the important building characteristics are those metrics related to building height, volume and roof slope. ACKNOWLEDGMENT The authors would like to acknowledge the Environmental Prediction in Canadian Cities (EPiCC) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS), in addition to the CanmetENERGY division of Natural Resources Canada (NRCAN).
While building types 1 and 2 had classification accuracies greater than 85%, type 3 proved more erroneous in this model. The commission error associated with type 3 accounts for approximately 30% of the error in the entire model. Therefore, to improve overall accuracy of this approach, future work is best focused on determining appropriate metrics to better separate building types 2 and 3. The technique presented here is designed to function within the selected study area and for single-family residential dwellings. Further research is necessary to examine whether this approach is also valid for a broader range of building types (such as high density residential, industrial and institutional). Some of the primary advantages of using a decision tree classification with LiDAR data to determine architectural typologies include the high degree of automation and the minimal use of training data. However, more direct approaches for modeling energy consumption may also be possible using LiDAR derived building morphological characteristics without the need of first determining typologies and will be examined in further research.
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Crawley, D.B., Hand, J.W., Kummert, M., Griffith, B.T. “Contrasting the capabilities of building energy performance simulation”, Building and Environment, Vol. 43, No. 4, 2008, pp. 661-673 [2] Ratti, C., Baker, N., Steemers, K. “Energy consumption and urban texture”, Energy and Building, Vol. 37, No. 7, 2005, pp. 762-776 [3] Rylatt, R.M., Gadsden, S.J., Lomas, K.J. “Methods of predicting urban domestic energy demand with reduced datasets: a review and a new GIS-based approach”, Building Services and Engineering Research and Technology, Vol. 24, No.2, 2003, pp. 93-102 [4] Neidhart, H., Sester, M. “Identifying building types and building clusters using 3-D laser scanning and GIS-data”, In: Geo-Imagery Bridging Continents, 20th ISPRS Congress, Istanbul, 2004, pp. 715–720 [5] Sambridge, M., Braun, J., McQueen, H. “Geophysical parameterisation and interpolation of irregular data using natural neighbours”, Geophysical Journal Internationa, Vol. 122, No. 3, 1995, pp. 837-857 [6] Goodwin, N.R., Coops, N.C., Tooke, T.R., Christen, A., Voogt, J.A. “Characterizing urban surface cover and structure with airborne LiDAR technology”, Canadian Journal of Remote Sensing, Vol. 35, No. 3, 2009, pp. 297-309 [7] Bayer, T. “Automated building simplification using a recursive approach”, In: Cartography in Central and Eastern Europe (Eds. Gartner, G., Ortag, F.) Springer: Berline, 2009, pp. 121-146 [8] Burrough, P. A. and McDonell, R.A. “Principles of Geographical Information Systems”. New York: Oxford University Press, 1998. [9] Salat, S. “Energy loads, CO2 emissions and building stocks: morphologies, typologies, energy systems and behaviour”, Building Research & Information, Vol. 37, No. 5, 2009, pp. 598-609 [10] Sherrod, P.H. DTREG Predictive Modeling Software (Users Manual). Available from: http://www.dtreg.com/DTREG.pdf, 2008 [11] Cohen, J. “A coefficient of agreement for nominal scales”, Educational and Psychological Measurement, Vol. 20, No. 1, 1960, pp. 37–46
VI. CONCLUSION Efficient management of the energy consumed in buildings is critical as energy demand grows and sources of affordable energy become less abundant. Automating the techniques for determining individual building energy consumption is an important step in this management process. The results of our paper demonstrate the potential application of LiDAR datasets for use with current building energy models. Through an examination of LiDAR derived building morphological characteristics we present a technique to accurately classify
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