NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-18-65-2018Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake
in JapanMoyaLuislmoyah@irides.tohoku.ac.jpYamazakiFumioLiuWenYamadaMasumiInternational Research Institute of Disaster Science, Tohoku
University, Miyagi, Sendai, 980-0845, JapanDepartment of Urban
Environment Systems, Chiba University, Chiba 263-8522, JapanDisaster Prevention Research Institute, Kyoto University, Gokasho,
Uji, 611-0011, JapanLuis Moya (lmoyah@irides.tohoku.ac.jp)4January2018181657826May201719June201720November201720November2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/18/65/2018/nhess-18-65-2018.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/18/65/2018/nhess-18-65-2018.pdf
The 2016 Kumamoto earthquake sequence was triggered by an Mw 6.2
event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April,
an Mw 7.0 event (the mainshock) followed. The epicenters of both
events were located near the residential area of Mashiki and affected the
region nearby. Due to very strong seismic ground motion, the earthquake
produced extensive damage to buildings and infrastructure. In this paper,
collapsed buildings were detected using a pair of digital surface
models (DSMs), taken before and after the 16 April mainshock by airborne
light detection and ranging (lidar) flights. Different methods were evaluated
to identify collapsed buildings from the DSMs. The change in average
elevation within a building footprint was found to be the most important
factor. Finally, the distribution of collapsed buildings in the study area
was presented, and the result was consistent with that of a building damage
survey performed after the earthquake.
Introduction
The detection of affected areas after an earthquake is very important for
disaster response activities. Allocating resources such as relief forces,
food, medicine, and shelter is crucial after a natural disaster strikes (Das
and Hanaoka, 2014). Thus, proper information on the damage situation will
improve the efficiency of distributing relief resources. The extent of the
affected area also provides an idea of the scale of the disaster and an
estimate of the relief demand. Damage assessment after an earthquake
disaster is important for the scientific community as well. A significant
amount of information has been obtained from previous earthquakes and used
to improve construction design codes to evaluate and mitigate damage to
buildings and infrastructure in the event of future earthquakes. For
instance, Whitman et al. (1973) provided earthquake damage probability
matrices using data collected after the 1971 San Fernando, California
earthquake. Yamazaki and Murao (2000) proposed vulnerability functions for
Japanese buildings based on building inventory and damage data and the
spatial distribution of strong motion (Yamaguchi and Yamazaki, 2001) during
the 1995 Kobe earthquake in Japan.
The post-event lidar data for the study area. Shaded colors
represent the elevation. The green rectangle shows the locations of the area
surveyed by Yamada et al. (2017). The inset shows Kyushu Island, and the blue
polygon in the inset depicts the study area.
Information gathered from field surveys is invaluable and very precise;
however, the process requires significant time and effort, and access to
affected areas is often hindered by road closures and secondary hazards.
Remote sensing is an effective tool for detecting damaged areas because it
can be used to document damage to large areas without direct access to the
affected area (Yamazaki and Matsuoka, 2007; Rathje and Adams, 2008;
Dell'Acqua and Gamba, 2012). Immense improvement to the accessibility of
remote-sensing imagery data and geospatial data processing tools has been
achieved over the last several years (Vuolo et al., 2016; Korosov et al.,
2016). A dramatic increase in the number of satellite, aircraft, and unmanned
aerial vehicle (UAV) sensors has been observed as well. One of the most
successful approaches for assessing damaged areas is based on change
detection between a pair of images taken before and after an
earthquake (Meslem et al., 2011; Liu et al., 2013; Uprety et al., 2013). In
addition, remote sensing has been used for long term urban recovery
monitoring (Hoshi et al., 2014; Hashemi-Parast et al., 2017).
Schweier and Markus (2006) pointed out that airborne light detection and
ranging (lidar) data can be used to classify collapsed buildings using the
following geometrical features of a building extracted from lidar data: the
height change from the initial one, the reduction of the total volume, the
footprint borders, the inclination of the structure, the debris spread
outside the footprint, the additional covered area outside the footprint,
and the damage situation of the roof. They proposed a modification of the
previous damage classification method (Okada and Takai, 2000) using these
geometrical features. Although they suggested the use of airborne lidar data
to analyze collapsed buildings, applications to real cases were not
provided.
Applications of lidar for damage detection are still few compared with other
remote-sensing technologies. The main reason is the lack of lidar data
before a disaster. However, Aixia et al. (2016) performed a study on the
possibility of detecting building damage using only a post-earthquake lidar
digital surface model (DSM). Their results are promising for buildings with
simple roof shapes, such as flat and pitched roofs. Rehor et al. (2008)
proposed the use of a plane-based segmentation method to detect damaged
buildings, wherein the number of unsegmented pixels in damaged buildings is
larger than in undamaged buildings. Labiak et al. (2011) proposed an
automated method to detect and quantify building damage using only a
post-earthquake lidar DSM as well, but their results had low accuracy for
heavily damaged and collapsed buildings. Hussain et al. (2011) combined
lidar data with GeoEye-1 imagery to detect damaged buildings after the 2010
Haiti earthquake. They detected 190 damaged buildings out of 200; however,
their procedure required manual intervention, and the damage level was not
clearly classified. Instead of lidar data, Maruyama et al. (2014)
constructed two DSMs from two sets of aerial
images: before and after the earthquake. Then, the collapsed buildings after
the 2007 Niigata-Chuetsu-Oki earthquake, Japan, were identified using the
difference in elevation between the DSMs.
An Mw 6.2 earthquake struck Kumamoto Prefecture, Japan, on
14 April 2016 at 21:26 JST. The event produced structural damage and
resulted in nine human casualties (Cabinet Office of Japan, 2017). Then,
28 h later, a second earthquake with Mw 7.0 occurred close to
the first one. Thus, the first event was designated the “foreshock” and the
second the “mainshock”. The epicenter of the foreshock was located at the
end of the Hinagu fault, and the epicenter of the mainshock was located in
the Futugawa fault. Both events were located in the town of Mashiki, which
has a population of 33 000. The number of aftershocks following these events
reached the largest number among recent inland earthquakes in Japan (Japan
Meteorological Agency, 2017). The total number of deaths due to direct causes
reached 50, and over 8000 residential buildings were severely damaged or
collapsed due to the Kumamoto earthquake sequence.
Among the several remote-sensing technologies used to monitor the area
affected by the Kumamoto earthquake (Yamazaki and Liu, 2016), a pair of
lidar data sets taken before and after the mainshock were available (Moya et
al., 2017). As mentioned before, this kind of data set is not often
available. Therefore, this study explores the potential use of lidar data to
identify collapsed buildings over the affected area. Building collapse is
still the main cause of casualties and hence its prompt recognition is
crucial for search and rescue operations. The difference in elevation, the
standard deviation, and the correlation coefficient are tested for
this purpose.
Study area and data set
After the foreshock, a lidar-surveying flight was carried out during
15:00–17:00 (JST) on 15 April 2016 in order to record the effects of the
earthquake (Asia Air Survey Co., Ltd., 2017). It produced point clouds with
an average point density of 1.5–2 points m-2. Subsequently, because
the unexpected mainshock occurred, a second mission was set up during
10:00–12:00 (JST) on 23 April 2016, which produced point clouds with an
average point density of 3–4 points m-2. Both sets of lidar data were
acquired using a Leica ALS50II instrument and the same pilot and airplane.
After rasterization of the raw point clouds, two DSMs with a data spacing of
50 cm were created. The DSMs collected before and after the mainshock will
hereafter be referred to as the BDSM and ADSM. Figure 1 shows the extent of
the ADSM, which represents the entire study area. It covers the main part of
Mashiki and some parts of Nishihara village, Mifune and Kashima towns, and
Kumamoto city.
The study area is located in the near field of the Kumamoto earthquake
sequence where significant permanent ground displacements were produced
during the earthquake. A direct comparison of the BDSM and ADSM shows that
the building coordinates do not match because the ADSM contains coseismic
displacements. Therefore, the ADSM was shifted before detecting the damaged
buildings based on the permanent crustal movement calculated by Moya et al. (2017). To do this, an automated procedure for calculating the permanent
three-dimensional (3-D) displacement was implemented. The permanent ground
displacement was calculated by 100 m grid size and then applied to the
ADSM pixels within the grid size. Figure 2 illustrates the calculated
permanent ground displacement of the common lidar data area. In the figure,
the results of new field measurement carried out in August 2016 for
surveying reference points after the Kumamoto earthquake are also shown (Geospatial Information Authority of Japan, 2017). The coseismic
displacements estimated from the lidar data show good agreement with the
survey results (Fig. 3). In Fig. 2, the causative fault is located in
the areas where sudden changes in the direction of the permanent ground
displacement are observed. Over the entire study area, a maximum horizontal
displacement of approximately 2 m was observed.
Estimated three-dimensional coseismic displacement after the
mainshock of the 2016 Kumamoto earthquake. The black arrows and the shaded
colors indicate the horizontal and vertical displacements obtained from
lidar (Moya et al., 2017). The blue arrows indicate the
horizontal displacements at the control points measured by the Geospatial
Information Authority of Japan (2016).
Comparison between the coseismic displacements estimated from the
lidar data (Moya et al., 2017) and from field measurements (Geospatial
Information Authority of Japan, 2016).
Examples of collapsed buildings from the lidar data. The
left column shows the photos taken after the mainshock by the authors. The
middle column shows the lidar data, wherein the blue points depict the BDSM and
the red points the ADSM. The right column shows the elevation differences
between the two DSMs, wherein the solid lines depict the building footprints
and the dashed lines depict the footprint reduced by 1 m.
Building damage survey data from Yamada et al. (2017). The location
of the survey area is shown in Fig. 1.
Detection of damaged buildings
To focus on buildings, a geocoded building footprint data set, provided by
the Geospatial Information Authority of Japan (GSI), was used. Only
buildings with footprint areas greater than 20 m2 were evaluated.
Because the point densities of the BDSM and ADSM are different and the
footprint data include some errors, perfect matching of the DSMs with the
building footprints could not be achieved. For this reason, the building
footprints were reduced by 1 m (i.e., the reduced polygon is located inside
a building footprint), and they were projected onto the same reference
system as that of the DSMs (Fig. 4). The lidar data within the reduced
building boundaries were then extracted and processed. The reason for using
the reduced building boundaries was to discard the DSM data near the
building boundaries in the subsequent analysis. The distance of the buffer (1 m)
was decided based on a preliminary evaluation of the data (Moya et
al., 2016).
Figure 4 illustrates five buildings located in the study area. For each
case, the BDSM (blue dots), the ADSM (red dots), and the difference between the
two DSMs are depicted. These buildings were selected in order to demonstrate
different damage patterns: nondamaged, tilted, and collapsed buildings. It
is worth noting that the difference between the DSMs for a nondamaged
building (Fig. 4a) shows high values around the boundary of the building
footprint, which was caused by the effect mentioned earlier. These errors
are certainly present for tilted buildings as well and make damage detection
very challenging (Fig. 4b). Figure 4c shows a typical collapsed
steel-frame building with a well-known damage pattern that occurs with a
soft story or a weak story, that is, a significant difference in the stiffness/resistance
between one story and the rest. They show a significant horizontal/vertical
movement, which is easier to detect by lidar data. Figure 4d shows a
collapsed wooden building that was shifted significantly in the horizontal
direction. Conversely, the collapsed wooden building shown in Fig. 4e does
not exhibit a horizontal movement, only a vertical shift. Lateral spread
of debris is an important issue when the building is located along a main
road. For almost all the collapsed buildings, a clear decrease in building
elevation was observed from the lidar DSMs.
The number of buildings within the study area is very large, so it is
necessary to implement an automated procedure to evaluate the extent of
their damage. In this study, three parameters were used: the average height
difference between the two DSMs (ΔH) within the reduced building
footprint, its standard deviation (σ), and the correlation
coefficient (r) between the two DSMs. These parameters were calculated for
each building using the following equations:
ΔH=1N∑i=1NHai-Hbiσ=∑i=1NHai-Hbi-ΔH2Nr=N∑i=1NHaiHbi-∑i=1NHai∑i=1NHbiN∑i=1NHai2-∑i=1NHai2N∑i=1NHbi2-∑i=1NHbi2
where i∈{1,2,…,N} and N is the number of elevation
points inside a given reduced building footprint. Hai and Hbi
are the elevations from the ADSM and BDSM. The correlation
coefficient ranges from -1.0 to 1.0 and has proven to be effective in
detecting changes from a pair of satellite images (Liu et al., 2013; Uprety
et al., 2013). A value of r close to 1.0 indicates no change.
Scatter plots of the three parameters (ΔH, σ, r)
calculated from the lidar DSMs for the buildings surveyed by Yamada et
al. (2017).
Yamada et al. (2017) presented the distribution of building damage in the
central part of Mashiki, wherein the damage was determined from aerial photos
and field surveys. The damaged buildings were classified into four
categories: no damage (D0), partial/moderate damage (D1–D3), severe
damage/incline (D4), and story collapse (D5). Here, D1–D5 represent the
degree of damage according to Okada and Takai (2000), which is similar to
G1–G5 of the European Macroseismic Scale (EMS-98). Figure 5 shows the damage
distribution over the surveyed area, which is located along the north side
of the Akitsu River.
Figure 6 shows the scatter plots of the parameters calculated for the
surveyed buildings, and Fig. 7 shows the histograms of the three
parameters for the buildings with different damage levels, wherein the average (solid line) and
the standard deviation (dashed line) are also included.
Significant overlap of damage levels D0, D1–D3, and D4 was observed
regardless of which parameter was chosen. On the other hand, collapsed
buildings (D5) tend to have large negative values of ΔH. Therefore,
this paper focuses on the detection of collapsed buildings. It is important
to note that few collapsed buildings show positive values of ΔH. A
closer look showed that those buildings were covered by a neighboring
building that had collapsed.
Histograms of the three parameters (ΔH, σ, r)
calculated from the lidar DSMs for the buildings surveyed by Yamada et
al. (2017), separated into four damage levels.
Scatter plots of the three parameters (ΔH, σ, r)
calculated for all the buildings in the study area. The color represents the
density of points, wherein red shows the area with the highest density.
Although ΔH seems to be the dominant parameter for identifying collapsed buildings, the other two parameters (σ and r) can still
provide additional information. For instance, if we observe the collapsed
buildings from the scatter plot in Fig. 6c (red marks), a trend can be
observed in which r trends to one when σ is close to zero. This trend
is related to the collapse patterns and can be observed for the collapsed
buildings shown in Fig. 4. Figure 4d shows a completely collapsed
building in which the debris has spread laterally. For those cases, the values
of r are low and the values of σ are large. On the other hand, Fig. 4e
shows a collapsed building with a roof that remained almost the same shape
while it collapsed almost vertically. This means that all the elevations
inside the footprint decreased by about the same amount, thus leading to a
high value of rand a low value of σ. This pattern is often difficult
to detect from optical aerial and satellite optical images, because the
sensor measures the landscape vertically. The histograms for collapsed
buildings (Fig. 7) shows that several collapsed buildings have a value for
r greater than 0.5, and it would be difficult to detect this from aerial or
optical satellite imagery. Readers might notice that noncollapsed buildings
also have a value of r close to 1 and σclose to zero; however, those
can be first filtered using ΔH. Then, the pattern of collapse can be
evaluated from the other two parameters (using a decision
tree).
Within the study area, 26 128 building footprints were found. It is worth
mentioning that few buildings were not well registered in the GIS map.
Figure 8 shows the parameters calculated for each building, wherein the
shaded color depicts the density of the dots. Most of the points are located
at approximately (ΔH, σ, r)= (0 m, 0.5 m, 0.9), which
indeed represents noncollapsed buildings. Several buildings show positive
values of ΔH. A closer look revealed two principle factors: (1) the
collapse of a neighboring building and (2) plastic covers placed over the
roof for protection from the rain.
Kappa coefficient (a) and overall
accuracy (b) obtained from the comparison between the data surveyed
by Yamada et al. (2017) and the estimated collapsed buildings based on
different ΔH threshold values.
Classification of collapsed (red) and noncollapsed (blue) buildings
using the three parameters based on SVM.
The next concern was to define a criterion to set threshold values that can
differentiate collapsed/noncollapsed buildings properly. A number of
options were evaluated in this study. Since it is obvious that the buildings
with clear negative values of ΔH correspond to collapsed buildings,
we first analyzed the classification using a threshold for ΔHonly.
The buildings with ΔHvalues smaller than that threshold were
classified as collapsed; the buildings with ΔH values
greater than the threshold were classified as noncollapsed. The
possible thresholds were tested on the buildings surveyed by Yamada et al. (2017).
Figure 9 shows the Cohen's kappa coefficient and the overall
accuracy calculated from the comparison between the estimated collapsed and
noncollapsed buildings (i.e., using a given threshold) and the building
damage classes based on the ground truth. For the comparison, the buildings
with damage levels D0, D1–D3, and D4 were labeled noncollapsed buildings.
The Cohen's kappa (k) coefficient and the overall accuracy (OA) are expressed
as follows:
pno=(p21+p22)(p12+p22)pyes=(p11+p12)(p11+p21)po=p11+p22pe=pyes+pnoOA=pok=po-pe1-pe,
where p11 and p12 are the ratio of noncollapsed buildings predicted
as noncollapsed and collapsed buildings. p21 and
p22 are the ratio of collapsed buildings predicted as noncollapsed and
collapsed buildings. From Fig. 9, it is observed that a
threshold value of -0.5 m gave the highest values for both the Cohen's kappa
coefficient (0.80) and the overall accuracy (0.93).
To determine whether the use of all the parameters could produce better
accuracy in detecting collapsed buildings, the support vector machine (SVM)
method was selected to construct a plane that separates collapsed and
noncollapsed buildings in the three-dimensional database (ΔH, σ, r). The plane has the largest distance from the nearest training
data (ground truth data). Using kernel functions, SVM can be used to
construct a nonlinear function as well. However, in this study we only
evaluated linear functions (i.e., a plane or linear kernel function).
Figure 10 shows the plane constructed using SVM, wherein the red and blue
marks depict the collapsed and noncollapsed buildings based on the ground
truth. The plane was constructed using the same amount of data for the two
classes. Thus, 205 noncollapsed buildings were selected randomly from the
surveyed data. The analysis was performed several times, and although the
plane obtained showed small variations due to the random selection of the
training data, the Cohen's kappa coefficient produced in each analysis was
almost constant, with minor fluctuations around 0.80. The accuracy produced
by SVM is very similar to the accuracy obtained when only a ΔH
threshold is used. For a linear kernel SVM, the vector w
perpendicular to the decision plane is defined by the following expression:
w=∑iαiyixi,
where xiis a training vector that contains the three parameters (ΔH,
σ and r), yi represents the class that can be either
1 or -1, and the coefficients αi are obtained by solving the
following problem:
minα12αTQα-eTαQij=yiyjxi⋅xj0≤αi≤C,i=1,…,n,
where e is a vector with elements that are all ones. C is the upper bound and is
used as a regularization parameter.
The classifier's cross-validation accuracy as a function of
C. (a) Overall evaluated range of C. (b) A closer look
at values lower than 5.
The parameter C trades off misclassification of training examples against
simplicity of the decision surface. A low C value makes the decision surface
smooth and a high C value aims to classify all training examples
correctly (Skit-learn, 2017a). In this study a value C equals to 1 was used.
In order to evaluate its effects, a cross-validation procedure was
performed. For each C value, 80 % of the surveyed data were selected
randomly and were used to calibrate the SVM classifier. The rest of the
surveyed data were used to calculate a score that represents the accuracy.
The overall accuracy was chose as the score. The procedure was repeated five
times and the average was stored. Figure 11 shows the cross-validation
accuracy. It is observed the accuracy remains mainly constant with small
fluctuations at lower values. However, a difference of approximately 3 %
is observed between the worst and the best accuracy. Therefore, it is
concluded that the C value did not affect the SVM classifier in our study.
Classification of collapsed (red) and noncollapsed (blue) buildings
using the three parameters based on the k-means clustering method.
Confusion matrix calculated from the comparison of the ground truth
data and the predicted damage levels based on the ΔHthreshold (a), SVM (b), and k-means
clustering (c). Two damage levels, noncollapsed (1.0) and
collapsed (2.0), were employed.
(a) A map showing the distribution of collapsed (ΔH<-0.5 m) buildings, shown as red polygons, in the study area.
The pixel color represents the difference in elevation between the BDSM and
ADSM. The green squares show the locations of areas shown in
panel (b) and Fig. 15. Close-up view of area (b) in which
the collapsed buildings were concentrated. The black triangles show the D5
buildings from Yamada et al. (2017).
Close-up view of areas (c) and (d) in Fig. 14a
where collapsed buildings are concentrated. The red and green polygons are
the collapsed and noncollapsed buildings estimated using the
threshold (ΔH<-0.5 m).
(a) Aerial image taken on 15 April; (b) aerial
image taken on April 23. The thick red polygons show buildings that collapsed
after the foreshock and were detected using the ΔH threshold.
This study also evaluated the potential use of unsupervised classification
for collapsed buildings. Specifically, k-means cluster analysis was
applied to all the data in the study area. Unlike SVM, k-means clustering
does not require training data. Therefore, the database of all the buildings
in the study area (Fig. 8) was used. The method clusters the data and
separates them into two groups, which represent the collapsed and
noncollapsed buildings. The objective of the method is to minimize the
inertia of each group, that is, the summation of the squared distance
between all the data points of a group and its centroid. The result is
highly dependent on the initialization of the centroids. Here, a k-means++
initialization scheme was used. K means++ initializes the centroids to
a distance from each other (Scikit-learn, 2017b). Figure 12 represents the
predicted collapsed and noncollapsed buildings using the k-means clustering
method, for which the Cohen's kappa coefficient obtained was 0.76. Figure 13
shows the confusion matrix calculated from the comparison between the ground
truth data and the predicted results from the three methods explained above,
applying a ΔH threshold, SVM, and k-means clustering. The first two
methods show the same level of accuracy, while k-means clustering shows a
lower accuracy.
Figure 14 illustrates the spatial distribution of collapsed buildings
estimated using a ΔH threshold of -0.5 m. A large number of collapsed
buildings were observed in the study area (Fig. 14a). The red and black
polygons represent the collapsed (D5) and noncollapsed (D0–D4) buildings. The color of the pixels represents the difference in
elevations between the ADSM and BDSM. Blue pixels depict differences of
elevations less than -0.5 m, and yellow pixels represent differences greater
than 0 m. Figures 14b and 15 provide a closer look of the areas where
the collapsed buildings are concentrated. Figure 14b also depicts the
location of the collapsed buildings surveyed by Yamada et al. (2017) as
black triangles. Within the study area, a total of 26 128 buildings were
evaluated, and 1760 buildings were classified as collapsed (ΔH less
than -0.5 m).
It was observed that some buildings that collapsed during the foreshock (14 April
event) were also detected by the lidar methods. In order to be detected, the
debris of those buildings should be either severely disturbed by the
mainshock (16 April event) or removed before the ADSM was recorded. For
instance, Fig. 16 shows two buildings that collapsed during the foreshock (Fig. 16a). However, because the mainshock produced a significant reduction
in their elevations (Fig. 16b), it was also detected from the pair of
lidar.
Discussion
This paper evaluated the use of lidar data to detect damaged buildings by
means of three parameters: ΔH, σ, and r. It was found that
collapsed buildings can be identified precisely from the average difference
in height, ΔH. However, the other two parameters can provide
additional information about the collapse pattern. The collapsed patterns
are correlated with the failure mechanism of buildings, which might highlight
some deficiencies in the design codes that were used in the construction
process. A detailed understanding of the failure mechanism is important to
the practice of forensic engineering, the investigation of failures and
other performance problems. Moreover, with further evaluation, the collapsed
pattern might contribute to future improvements of the building design
codes. Unfortunately, it was not possible to calibrate a threshold that can
properly classify different collapsed patterns. The main reason is because
there was no information related to the collapse patterns in the survey
data. Perhaps this task can be done in future research after new survey
data are released.
Some words regarding sources of error that were present in this study should
be mentioned. The footprint data provided by the Geospatial Information
Authority of Japan (GSI) is rather precise but not perfect. Three drawbacks
were observed: (1) a few buildings were not included in the database, (2) a
slight shift between the building footprint and corresponding lidar data is
sometimes observed, and (3) in some cases, a group of buildings, consisting
mostly of two or three buildings, were registered within one building
footprint. These uncertainties may have produced errors in the detection of
collapsed buildings. However, they did not have a significant impact on the
overall results, which is confirmed in Fig. 9, wherein the Cohen's kappa
coefficient and the overall accuracy are significantly high. This problem
can be solved by performing a manual inspection or automatic detection of
buildings from the BDSM in order to update the data set. However, the authors
decided to work with the data in its current state because this uncertainty
is likely to be present in other real situations in which a quick report on
damage extent is required.
Of the three methods evaluated here, the k-means clustering exhibits the
lowest accuracy. The main reason is that, unlike the SVM method, the
k-means clustering does not use any truth data. However, it produced a
kappa coefficient of 0.76 and overall accuracy of 92 %, which is still
quite good. The k-means clustering method is useful for taking a first
glance at the distribution of collapsed buildings because the method does not
require any training data. The procedure is well-known and robust, with
several efficient algorithms with proven fast convergence.
Finally we would like to discuss the building damage rate in the Kumamoto
earthquake in comparison to other recent M7-level crustal earthquakes in
Japan. In the 1995 Kobe earthquake (Mw 6.9), about 49 000 buildings
were collapsed (G5 in EMS-98 scale) or severely damaged (G4) out of 560 000 buildings in the affected urban area (Building Research Institute,
1996). The recorded strong motion distribution in the Kobe earthquake was at
a similar level to that of the Kumamoto earthquake (Yamaguchi and
Yamazaki, 2001). However, in 1995, the number of strong motion accelerometers was
much lower, about only 10 in the hard-hit zone. The building density of the
affected area was much higher in the Kobe region than that of the Kumamoto
region. Considering these differences, it is difficult to conclude which
earthquake was more destructive. From our experiences (Yamazaki and Murao,
2000; Yamaguchi and Yamazaki, 2000), the severity of damage for timber-frame
houses in Mashiki was at a similar level to that in the hard-hit zone
of the Kobe region. There were a few more recent M7-level crustal
earthquakes in Japan, such as the 2004 Niigata-Chuetsu earthquake (Mw 6.6)
and the 2007 Niigata-Chuetsu-Oki earthquake (Mw 6.6). However, the population
density of the affected urban areas was lower in these events, and thus it
is again difficult to compare their damage situations (Nagao et al., 2011)
with that of Kumamoto, although their strong motion levels were again
comparable with that of the Kumamoto event.
Another point of discussion is whether or not the Mw 6.2 foreshock
influenced the overall damage situation of Mashiki. Our preliminary
conclusion is “partially yes, but not so much”. Based on numerical
analyses of the behavior of collapsed typical timber-frame buildings (Building
Research Institute, 2015), those built by the old seismic code (before 1982)
had mostly collapsed only from the mainshock's excitation, even without being
preshaken by the foreshock (Suto et al., 2017). However, in some cases, building
models had collapsed only from the sequence of the foreshock and
mainshock excitations. More detailed results on this matter will be
presented in the near future.
Conclusions
In this study, the spatial distribution of collapsed buildings was extracted
from a pair of lidar data sets taken before and after the 2016 Mw 7.0
Kumamoto earthquake. For this purpose, geographic information on building
footprints was employed. Three parameters were used: the average (ΔH) and standard deviation (σ) of the height differences between the
two DSMs and the correlation coefficient between them (r). The parameters
were evaluated using the building damage survey data set obtained by Yamada
et al. (2017); ΔHwas found to be very efficient for identifying collapsed buildings. However, the other parameters provided insights into
the collapse pattern. After evaluating different methodologies with which to identify collapsed buildings, buildings with ΔHless than -0.5 m were
considered to have collapsed. The distribution of collapsed buildings obtained by
Yamada et al. (2017) was illustrated together with the height difference
between the two DSMs, and good agreement was observed. From a total of
26 128 evaluated buildings, 1760 collapsed buildings were identified. To our
knowledge, this result may be the first case in which a large number of
collapsed buildings were identified from a pre- and post-event lidar DSM
pair.
It is expected that the use of lidar data to identify damaged areas will
eventually increase in the near future. However, because of the current lack
of data, the implementation of a method to identify collapsed building
using only post-event lidar data is important and will be considered in a
future study. Additional future studies related to the use of these lidar
data are the quantification of debris related to road
blockage, and the identification of landslides.
The digital surface models used in this study are owned and provided by Asia
Air Survey Co., Ltd. The building footprint data are available from the website of the Geospatial Information Authority of Japan.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was financially supported by a Grant-in-Aid for Scientific
Research (project numbers: 17H02066, 24241059) and the Core Research for
Evolutional Science and Technology (CREST) program by the Japan Science and
Technology Agency (JST) “Establishing the most advanced disaster reduction
management system by fusion of real-time disaster simulation and big data
assimilation (Research Director: Shunichi Koshimura of Tohoku
University)”. Edited by: Oded
Katz Reviewed by: two anonymous referees
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