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3D BUILDING GIS DATABASE GENERATION FROM LIDAR DATA AND FREE ONLINEWEB MAPS AND ITS APPLICATION FOR FLOOD HAZARD EXPOSURE ASSESSMENT

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Identifying buildings that are exposed and vulnerable to flooding is important in flood disaster preparedness, risk assessment, and mitigation. In most cases, the availability of a 3D building database where each building is attributed in terms of name, type (e.g., residential, commercial, government, educational, etc.), and height (among many other attributes) makes the required analysis fast, efficient and informative. In this paper, we highlight the usefulness of a 3D building database generated from LiDAR data and free online web maps in assessing the exposure and vulnerability of buildings to flooding through a case study made for Cabadbaran River Basin, Mindanao, Philippines. The results of this case study consist of a series of maps and statistics showing exposure and vulnerability of buildings to flooding that can be utilized by Local Government Units and the communities in the river basin in their flood disaster risk reduction and management strategies.
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3D BUILDING GIS DATABASE GENERATION FROM LIDAR DATA AND FREE ONLINE
WEB MAPS AND ITS APPLICATION FOR FLOOD HAZARD EXPOSURE ASSESSMENT
Jojene R. Santillan, Meriam Makinano-Santillan, Linbert C. Cutamora, Jesiree L. Serviano
CSU Phil-LiDAR 1, College of Engineering and Information Technology, Caraga State University, Ampayon,
Butuan City, Philippines
E-mail: santillan.jr2@gmail.com
KEY WORDS: 3D buildings, database, LiDAR, flood, exposure, vulnerability
ABSTRACT: Identifying buildings that are exposed and vulnerable to flooding is important in flood disaster
preparedness, risk assessment, and mitigation. In most cases, the availability of a 3D building database where each
building is attributed in terms of name, type (e.g., residential, commercial, government, educational, etc.), and
height (among many other attributes) makes the required analysis fast, efficient and informative. In this paper, we
highlight the usefulness of a 3D building database generated from LiDAR data and free online web maps in
assessing the exposure and vulnerability of buildings to flooding through a case study made for Cabadbaran River
Basin, Mindanao, Philippines. The results of this case study consist of a series of maps and statistics showing
exposure and vulnerability of buildings to flooding that can be utilized by Local Government Units and the
communities in the river basin in their flood disaster risk reduction and management strategies.
1. INTRODUCTION:
Flooding is one of the most destructive natural disasters in the Philippines. It can cause loss of lives and damages to
properties as well as to infrastructures like buildings, roads and bridges. Excessive quantity of rainfall brought by
tropical storms is the most common cause of flooding, just like what happened in Metro Manila during the passing
of Tropical Storm Ondoy (Cheng, 2009), and in various provinces in Mindanao when Tropical Storms Agaton and
Seniang caused rivers to overflows (NDRRMC, 2014; 2015).
Although the frequency of flood-related disasters has grown in recent years, the tools to model and understand
flood risks have also increased in number, had become more sophisticated, and are readily available for use.
Examples of these are flood models that allow hydrodynamic scenario simulation of flood water propagation, as
well as in the assessment of flood damage (Vojinovic and Tutulic, 2009). The hazard maps produced by these flood
models are used as important inputs for assessing the exposure and vulnerability of localities to various flood
scenarios, usually conducted using Geographic Information System (GIS) tools and techniques (Fedeski and
Gwilliam, 2007). An important part of this GIS-based assessment is the identification of the type, location, and
height of buildings that are exposed to various scenarios and levels of flood hazards. One practical application is to
identify in advance the specific buildings and households that can be affected by an expected flooding scenario in
order to prevent casualties through evacuation, or to lessen the impact through conduct of flood mitigation
activities. In this situation, the availability of a 3D building database where each building is attributed in terms of
name, type (e.g., residential, commercial, government, educational, etc.), and height (among many other attributes)
can make the required hazard and vulnerability assessment fast, efficient and informative.
LiDAR-derived products such as Digital Surface Models (DSMs) have been widely utilized to map earth features
such as buildings, roads and bridges (Priestnall et al., 2000; Wang and Schenk, 2000). The availability of height
information provided by LiDAR data makes it very suitable in generating a 3D building database. Several
approaches have been developed in extracting 3D building information from LiDAR data (Rottensteiner, 2003;
Sohn and Dowman, 2007). Building information such as geometry, position and height are primarily generated.
Using LiDAR data, buildings may be distinguished from vegetation objects by evaluating shape measures, or the
heights of the features (Wang and Schenk, 2000). In a LiDAR DSM, buildings can be considered as detached
objects rising vertically on all sides with a minimum height above the bare earth. In terms of geometry, buildings
footprints are bounded by distinct edges of regular shapes relative to natural objects. Most building surfaces can be
approximated by simple geometric shapes (i.e., triangles, squares, and rectangles).
One limitation of using purely LiDAR data such as a DSM in building extraction is the difficulty to correctly
delineate the shape of the building due to the presence of nearby vegetation that covers some parts of the building.
In this case, the use of high resolution images is a useful supplementary dataset to aid or complete the extraction
process. Another limitation is the inability to determine the type of the building, and this sometimes requires field
inspection or the use of secondary spatial datasets such as maps.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
In this paper, we generated a 3D GIS
database of buildings in Cabadbaran
River Basin, Mindanao, Philippines
through analysis of various datasets that
included 1-m resolution LiDAR DSM
and Digital Terrain Model (DTM), high
resolution images in Google Earth, and
free online web maps such as
Wikimapia. The use of Google Earth
images and Wikimapia to address the
limitation of using the DTM and DSM
during the building type classification.
We also present in this paper how the 3D
buildings database was used as exposure
datasets for the flood hazard assessment
of the river basin that included
assessment of the vulnerability of the
buildings to the flooding.
2. THE STUDY AREA
Cabadbaran River Basin (Figure 1) is
located in Agusan del Norte, Mindanao,
Philippines. It covers a major portion of
Cabadbaran City. It has an approximate
drainage area of 215 km2. The
Cabadbaran river basin is one of the
areas affected by Tropical Storms „Agaton‟ and „Seniang‟ last January 2014 and December 2014, respectively.
Flooding due the heavy rains brought by these tropical storms caused widespread damages in agriculture and
infrastructures within the river basin, especially in Cabadbaran City. The presence of buildings and the occurrence
of flooding make the river basin an ideal case study area for integrated 3D building database generation and flood
hazard assessment.
3. MATERIALS AND METHODS
3.1 Datasets Used
We used the 1-meter resolution LiDAR-derived Digital Surface Model (DSM) and Digital Terrain Model (DTM)
for extracting the building features within the river basin (Figure 2). The LiDAR DSM and DTM were acquired and
processed by Data Pre-Processing Component of the University of the Philippines Diliman Phil-LiDAR 1 project.
The two DEMs were provided in ESRI GRID format with Universal Transverse Mercator (UTM) Zone 51 North
projection and the World Geodetic System (WGS) 1984 as horizontal reference. Both DEMs have the Mean Sea
Level (MSL) as vertical datum.
For areas covered with dense vegetation, Google Earth images were utilized to improve the precision in extracting
the buildings. These high resolution satellite images were also used to re-check and compare the building polygons
extracted from the LiDAR DSM.
Free online web maps such as Wikimapia (http://wikimapia.org/) and Google Map
(https://www.google.com.ph/maps) (Figure 3) were used to gather information such as name and type of the
buildings within the river basin. We used the provided table from UP-Diliman Phil-LiDAR 1 project containing a
summary of different types of buildings with corresponding codes for the building type attribute.
Flood depth maps generated by Caraga State University through the CSU Phil-LiDAR 1 project were used as input
in flood hazard and building vulnerability assessment. These flood depth maps represent maximum depth of
flooding due to rainfall events with varying intensity and duration (i.e., return periods of 2, 5, 10, 25, 50 and 100-
year). These flood depth maps were transformed into flood hazard maps by categorizing the flood depths into
hazard levels as follows: low (<0.50 m depth), medium (0.50 m 1.50 m depth), and high (>1.5 m depth). These
maps are shown in Figure 4 and Figure 5.
Figure 1. The Cabadbaran River Basin, Agusan del Norte, Philippines,
shown here in a Digital Surface Model.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 3. Screenshots of the online web maps: Google map (left) and Wikimapia (right) that were utilized as
references for building attribution.
Figure 2. The LiDAR DTM and DSM for a portion of Cabadbaran River Basin.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 4. Flood hazard maps of Cabadbaran river basin for 2-, 5-, & 10-year rainfall return periods.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 5. Flood hazard maps of Cabadbaran river basin for 25-, 50-, & 100-year rainfall return periods.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
3.2 Building Extraction from LiDAR
data
Buildings were manually digitized from the
LiDAR DSM using ArcGIS 10.1 software
(Figure 6). The footprints of the buildings
were traced using the polygon feature type.
In addition, we also used the high resolution
images from Google Earth as reference for
checking and comparing the results of the
extracted features. Since some buildings
were covered by dense vegetation and their
shapes were indistinguishable in the DSM,
we examined its corresponding Google Earth
image to identify the shape of the feature.
The extracted buildings were saved as a GIS
Shapefiles.
3.3 Height Estimation and Feature
Attribution
The heights of the buildings were calculated
using the average of the base elevations and
top elevations for each footprint (Figure 7).
The base and top elevation values were
extracted from the DTM and DSM,
respectively.
The extracted buildings were attributed using the data
obtained from the Wikimapia and Google Map. Information
such as the type (Table 1) and building names (for non-
residential types) were obtained from these sources.
Table 1. Summary of the types of the building with
corresponding codes.
Building Type
Code
Residential
RS
School
SC
Market/Prominent Stores
MK
Agricultural & Agro-Industrial
AG
Medical Institution
MD
Barangay Hall
BH
Military Institution
ML
Sports Center/Gymnasium/Covered Court
SP
Telecommunication Facilities
TC
Transport Terminal (Road, Rail, Air, and Marine)
TR
Warehouse
WH
Power Plant/Substation
PP
NGO/CSO Offices
NG
Police Station
PO
Water Supply/Sewerage
WT
Religious Institution
RL
Bank
BN
Factory
FC
Gas Station
GS
Fire Station
FR
Other Government Offices
OG
Other Commercial Establishments
OC
Figure 6. The manually digitized buildings (in blue color) in
Cabadbaran river basin overlaid in the Digital Surface Model.
Figure 7. Building height estimation using the
LiDAR DSM and DTM.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
3.4 Flood Hazard and Building’s Vulnerability Exposure Assessment
We conducted GIS overlay analysis of the building footprints and 3D flood hazard maps of the basin to identify
which buildings are exposed to various levels of flood hazards (e.g., low, medium, high). In addition to this, we
also characterized the degree of flood exposure of buildings by comparing their heights with the simulated flood
depths in determining the vulnerability of the buildings in the river basin. If a building is located in a location
where flood depth is less than 0.10 m, then it is coded as “Not vulnerable”. If the flood depth at the building‟s
location is 0.1 m to less than 0.25*h (h is building height), then the vulnerability is “Low”. On the other hand, if the
flood depth is > 0.25h and ≤ 0.5h, then the vulnerability is medium. If the flood depth is > 0.5h , then the
vulnerability is high.
4. RESULTS AND
DISCUSSION
4.1 Buildings Database
Figure 8 shows a snapshot of
the buildings extracted from
the LiDAR DSM. There were
a total of 9,086 identified
buildings.
Figure 9 provides a summary
of the results in attribution of
the extracted buildings.
Statistics shows there were
8,605 residential buildings
identified, and it comprises
95% of the total buildings
within the river basin.
Figure 9. The number of buildings in Cabadbaran River Basin according to type.
6 4 22 1 12 13 4 172 28 2 13
8,605
179 18 4 3
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Number of Building
Building Type
Figure 8. The extracted buildings in Cabadbaran River Basin classified according to
type.
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4.2 Exposure of Buildings to Flood Hazards
Figure 10 shows the number of buildings under various hazard levels of flooding due to rainfall events with varying
return periods. The locations of these exposed buildings are shown in Figures 11-12.
Statistics show that as the rainfall return period increases (which also means increase in rainfall intensity and
duration), the number of buildings affected by flooding also increases. Consequently, the number of buildings that
are not flooded decreases. In all rainfall scenarios considered, majority of the buildings appears to be not flooded.
For flood-affected buildings, more buildings are exposed to „low‟ flood hazard levels than those in „medium‟ and
„high‟ hazard levels. This result means that majority of areas within the river basin where buildings are located are
relatively not prone to flooding; and if there is flooding, the level of hazard is low.
Figure 10. Number of buildings in Cabadbaran River Basin exposed to various hazard levels due to flooding caused
by rainfall events of different return periods.
4.3 Vulnerability of Buildings to Flood Hazards
Figure 13 shows the number of buildings in Cabadbaran City proper that were categorized according to their
vulnerability to flooding caused by rainfall events of different return periods. The locations of these vulnerable
buildings are shown in Figures 14-15.
It can be observed that majority of the buildings are not vulnerable to flooding, especially for flooding due to
rainfall events of 2, 5, 10, and 25 return periods. For flood affected buildings, more buildings are in „low‟
vulnerabilities, with increasing number as the rainfall return period increases.
The generated statistics also show that buildings exposed to medium and high flood hazard levels does necessarily
mean they will also have medium and low vulnerability. Looking at the graphs (Figure 10 and 13), the total number
of buildings under medium and high hazard exposure are higher than the total number of buildings in medium and
high vulnerabilities. This implies that even if a building‟s location has medium or high flood hazard levels, a
building‟s vulnerability can be lesser if its height is much higher than the depth of flooding. All of these results,
however, only used height as basis for assessing a building‟s vulnerability. The type of building material and other
factors were not considered.
6,422
2,314
334
16
5,880
2,735
451
20
5,460
2,961
632
33
4,988
3,190
791
117
4,720
3,241
959
166
4,407
3,225
1,248
206
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Not Flooded Low Medium High
No. of Buildings
Flood Hazard Level
Buildings Exposed to Different Flood Hazard Levels
2-year
5-year
10-year
25-year
50-year
100-year
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 11. Flood hazard exposure levels of buildings in Cabadbaran City proper for a 2-, 5-, & 10-year rain return
period flood event.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 12. Flood hazard exposure levels of buildings in Cabadbaran City proper for a 25-, 50-, & 100-year rain
return period flood event.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 13. The number of buildings in Cabadbaran River Basin vulnerable to flooding caused by rainfall events of
varying return periods.
6,658
2,334
79 15
6,142
2,817
107 20
5,708
3,170
182 26
5,230
3,527
256 73
4,929
3,745
301 111
4,610
3,963
370 143
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Not Vulnerable Low Medium High
No. of Buildings
Flood Vulnerability Type
Vulnerability of Buildings to Flooding Caused by Rainfall Events of
Different Return Periods
2-year
5-year
10-year
25-year
50-year
100-year
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 14. Vulnerability of buildings in Cabadbaran City proper for a 2-, 5-, & 10-year rain return period flood
event.
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36th Asian Conference on Remote Sensing 2015 (ACRS 2015), Quezon City, Philippines, Volume 3, pp. 2037-2050, 2015
Figure 15. Vulnerability of buildings in Cabadbaran City proper for a 25-, 50-, & 100-year rain return period flood
event.
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CONCLUSIONS AND FUTURE WORKS
In this paper we showed that a 3D building database extracted from LiDAR data (with each building attributed in
terms of type and height) can be a valuable dataset in assessing the exposure and vulnerability of buildings to
flooding. The results of this case study made for Cabadbaran River Basin highlights the use of this database to
generate statistics as well as in creating maps that can show the spatial distribution of buildings exposed to low,
medium and high hazard levels of flooding caused by rainfall events with return periods of 2, 5, 10, 25, 50 and
100-year. The height information derived for each building also allowed fast generation of statistics and maps
showing the building‟s vulnerability to flooding. Although the vulnerability assessment was purely based on the
building height, the information generated from the analysis can be very useful in flood disaster preparedness and
mitigation. One practical application would be identifying those buildings (and informing their occupants) that can
be of danger when a particular rainfall event of specific return period is expected to occur. Since the maps and
statistics of those buildings exposed and vulnerable to flooding were already generated according to rainfall return
period, it is already easy to identify those locations and conduct appropriate measures such as early evacuation to
prevent casualties.
The next phase of this study is to expand the analysis where the type and material of buildings will be considered in
the exposure and vulnerability assessment. Also, we will improve and consider physical basis in computing a
building‟s vulnerability level. Currently, the vulnerability levels adopted in the analysis were only based on
building height and flood depth, and the criteria used were initially assumed by the authors due to absence of
proper reference material during the time when this study was conducted.
ACKNOWLEDGEMENT
This work is an output of the Caraga State University (CSU) Phil-LiDAR 1 project under the “Phil-LiDAR 1.
Hazard Mapping of the Philippines using LiDAR” program funded by the Department of Science and Technology
(DOST). The SAR DEM and the LiDAR DTM and DSM used in this work were provided by the University of the
Philippines Disaster Risk and Exposure for Mitigation (UP DREAM)/Phil-LIDAR 1 Program.
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NDRRMC, 2014. NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression Agaton. National Disaster
Risk Reduction and Management Council. Retrieved February 1, 2014 from http://www.ndrrmc.gov.ph/.
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... The methods and results presented in this work are an update of what we have previously shown in an earlier work [5], where we have discussed building database generation, and hazard and vulnerability determination. In the present work, we present a complete discussion of the methodology which consist of (i.) generating a database of buildings from the DTM and DSM; (ii.) generation of flood depth and hazard maps through the use of a flood simulation model; and (iii.) ...
... Field surveys, familiarity with the area, and free online web maps such as Wikimapia (http://wikimapia.org/) and Google Map (https://www.google.com.ph/maps) were used as references to identify the type and name (for non-residential types) of the buildings. More details about the procedure can be found in [5]. ...
... On the other hand, if the flood depth is > 0.25h and ≤ 0.5h, then the vulnerability is medium. If the flood depth is > 0.5h, then the vulnerability is high [5]. ...
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External environmental hazards to which a city is exposed, such as those presented by its surrounding topography, can have a significant impact on the future integrity of its built structures and so on its economic and social sustainability. A methodology is proposed for assessing the risk of damage to buildings due to hydrological and geological hazards. It expresses risk in terms of the cost of damage. It estimates this from data, collected by a combination of physical survey and GIS mapping techniques, on the three elements of risk: exposure, hazard and vulnerability. These elements are, respectively, the extent and value of buildings exposed to risk, the severity and frequency of the hazard, and the degree of their vulnerability to damage. Working upwards from the scale of individual buildings, the estimate is aggregated to any appropriate urban spatial scale, such as neighbourhoods or electoral boundaries. The methodology has been tested in principle using a town in the UK as a case study. Further development of the work will look at reducing the data input by creating building typologies at increasing scales. Applications for the methodology are envisaged in urban planning both at policy and development control level and as a tool for climate impact studies.
Article
The use of airborne Light Detection And Ranging (LiDAR) technology offers rapid high resolution capture of surface elevation data suitable for a large range of applications. The representation of both the ground surface and the features on that surface necessitates the removal of these surface features if a ground surface Digital Elevation Model (DEM) product is to be produced. This paper examines methods for extracting surface features from a Digital Surface Model (DSM) produced by LiDAR. It is argued that for some applications the extracted surface feature layer can be of almost equal importance to the DEM. The example of flood inundation modelling is used to illustrate how a DEM and a surface roughness layer could be extracted from the original DSM. The potential for refining surface roughness estimates by classifying extracted surface features using both topographic and spectral characteristics is considered using an Artificial Neural Network to discriminate between buildings and trees.
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Automating data acquisition for 3D city models is an important research topic in photogrammetry. In addition to techniques that rely on aerial images, generating 3D building models from point clouds provided. by light detection and ranging (Lidar) sensors is gaining importance. The progress in sensor technology has triggered this development. Airborne laser scanners can deliver dense point clouds with densities of up to one point per square meter. Using this information, it's possible to detect buildings and their approximate outlines and also to extract planar roof faces and create models that correctly resemble the roof structures. The author presents a method for automatically generating 3D building models from point clouds generated by the Lidar sensing technology.
NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression Agaton. National Disaster Risk Reduction and Management Council
NDRRMC, 2014. NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression Agaton. National Disaster Risk Reduction and Management Council. Retrieved February 1, 2014 from http://www.ndrrmc.gov.ph/.
SitRep No. 22 re Effects of Tropical Storm SENIANG". National Disaster Risk Reduction and Management Council
NDRRMC, 2015. SitRep No. 22 re Effects of Tropical Storm SENIANG". National Disaster Risk Reduction and Management Council. Retrieved January 10, 2015 from http://www.ndrrmc.gov.ph/.
Building extraction and reconstruction from LiDAR data, International Archives of Photogrammetry and Remote Sensing
  • Z Wang
  • T Schenk
Wang, Z., Schenk, T., 2000. Building extraction and reconstruction from LiDAR data, International Archives of Photogrammetry and Remote Sensing, Vol. 33, No. B3, pp. 958-964.