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Utilization of LiDAR and IKONOS Satellite Data for Security Hotspot Analysis based on Realism of 3D City Model - Mazlan Hashim, Maged Marghany, Mohd Hafiz Anuar and Mohd Rizaludin MahmudInstitute of Geospatial Science & Technology (INSTEG)
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Utilization of LiDAR and IKONOS Satellite Data for Security Hotspot Analysis based on Realism of 3D City Model
Mazlan Hashim, Maged Marghany, Mohd Hafiz Anuar and Mohd Rizaludin Mahmud
Institute of Geospatial Science & Technology (INSTEG)INSPIRING CREATIVE AND INNOVATIVE MINDS
Introduction
• Geospatial applications for national security concerns– Social unrest– Terrorism motives
• Some of the important applications : – homeland security, – border monitoring, – battlefield intelligence, and – sensitive facility monitoring
Enabling technology photo-realism in 3D
• Stereo aerial photography• Reconstruction of geometry • Rendering radiometry• Visualization
– 3D and virtual reality
Enabling technology photo realism in 3D
• Stereo aerial photography– Fine spatial resolution satellite image – LiDAR
• Fused LiDAR and IKONOS
• Reconstruction of geometry • Rendering radiometry
– Real life texture
• Visualization– 3D and virtual reality– Decision making with multi-criteria for security
hotspot analysis– Surrounding effects – rain effects
Existing
Introduce
The context
Past
Now
Problem
production of photogrammetric-based 3D city and their analysis is a task/ business of “photogrammetric+3D visualizer”
Wide spread availability of statistical graphics and GIS software
Appearance of visualization and mapping services in GIS and related enabling technology
Photo realism not for to novice and casual users Thematic mapping and decision support tools become
principally available to novice and casual users, but Many potential users lack the required knowledge to
utilize the tools in new applications
Our Study
• Method of utilizing 3D city urban model to assess possible shooting location for security hotspot detection
• Materials– LiDAR (Leica ALS50, DSM gridded 25cm)– IKONOS satellite data (pan-sharpened)– In situ GPS for GCP– Meteorological data
Study Area• Merdeka
Square, Kuala Lumpur
LIDAR• Leica AL50 LiDAR scanner
system• Aircraft speed: 70m//s• Flying height: 1500 m above
ground level• Scanner field of view (half
angle): +16o
• Scan frequency: 14 Hz• Swath width: 863 m (600 m
with a 30% sidelap)• Pulse repetition rate: 10 kHz• Sampling density: average 2.4
m
FiIn situ GCP collectionTextures for rendering
Method
Systems:•Erdas Imagine V9.2•Arc GIS•Arc Scene•Google Sketup5
Data Pre-processing
Two tedious merging tasks:2)Merging Images with different sensor
geometry (spatial)– Use LiDAR as master (navigation
information)– Use spatial domain fusion (varible domain
fusion, high frequency domain modulation)
Data Pre-processing
Two tedious merging tasks: (cont’d)2) Spectral sharpening - Ensure no artifacts (visual and spectral
information ?
Simulated effective range
Desert eagle: Effective range 200m(magnum)
Dragunav: Effective range 800m
M16A: Effective range 450m
δ
Scores and rank of options
Crit 1: viewshed
0 0.5 1.0
Poor Excel
Crit 2 Eff dist
0 0.5 1.0
Poor Excel
Crit 3 weapon type
0 0.5 1.0
Poor Excel
Crit 4: Sniping Quality
0 0.5 1.0
Poor Excel
Crit 5: Rainfall effects
0 0.5 1.0
Poor Excel
Opt 1 Opt 2 ….. Opt n
Crit 1 0.33
Crit 2 0.18
Crit 3 0.88
Crit 4 0.90
Crit 5 0.15
OverallScore x
AssigningWeights tocriteria
Aggregating values
The ranking, weight and normalized weight
Distance(meter)
Direct ranking Weight
Normalized Weight
Normalized weight to 100
50 1 16 0.117647 12100 2 15 0.110294 11150 3 14 0.102941 10200 4 13 0.095588 10250 5 12 0.088235 9300 6 11 0.080882 8350 7 10 0.073529 7400 8 9 0.066176 7450 9 8 0.058824 6500 10 7 0.051471 5550 11 6 0.044118 4600 12 5 0.036765 4650 13 4 0.029412 3700 14 3 0.022059 2750 15 2 0.014706 1800 16 1 0.007353 1
136 1 100
Viewshed
N
Az (0 ~360) Horizon, 0o
90oTarget
Viewing angle
Viewangle
Dirrank
Wt
110
110
max
:::::::::::::
4546
400
Min0
::::::::::
0
90 0 0
Multi-criteria for ranking risk
W = n – rj +1Σ (n- rk +1)
where:W is the normalized weight for the jth criterion;n are numbers of criteria under consideration; andrj and rk are rank position of the criterion. ( k = 1,2,…,n).
Implementation ease, and simplicity of reasonings of output to decision-makers:
Rank viewshed + effective distance for the 3 weapon types
Producing 3 risk maps
Refined for sniping quality ratio
Simulated rainfall effects
Sniping quality ratio
N
AzHorizon, 0o
90oTarget
Viewing angle
P
A
B
Effective view, elliminating blind spots, etc = angle APB=180o;
Account both viewing angles (locus!)
Ratio
(degree/180) Weight
below 0.25 1
0.25 < x <0.5 2
0.5 < x <0.75 3
0.75 < x < 1 4
Rainfall effects
Target heavy medium drizzle
viewer
Rain amount(mm)
VisibilityDistance (m)
Dir ranking
No rain (clear day)
800 1 (max)
Heavy 150 4 (min)
Medium 300 2
Light (drizzle)
500 3
ResultsCity block
City block with arbitrary textures
Textures from real photos can be draped on city block model
ResultsConstructed city blockwith some real draped texture Top view
Constructed block, side view (east)
Complete texture compiled,draped on 3D block
Results
viewshed analysis draped on the Lidar data
Visible sitting areas from vantage points (white building tops)
Snipping effective range from centre of square
Desert eagle: 200m M16A: 450m Draganov: 800m
Location of potential risk security hotspots visualized in: 2D planar view
Visualization in 3D
Rank Building
Light red (86) Sultan Abdul Samad
Dark orange (79) Merdeka Square
Dark orange (79) Royal Club
Orange (72) Library
Orange (72) Church
Orange (72) Bank Rakyat
Orange (72) Bank Pertanian
Orange (72) HSBC Bank Building
Dark yellow (65) Bukit Aman building
Dark yellow (65) OCBC Building
Dark yellow (65) CIMB Bank Building
Yellow (58) Muamalat Building
Yellow (58) Daya Bumi Building
Yellow (58) DBKL Building
Light Green (30) Tradewind Building
Green (23) Bank Negara Building
Dark Green (16) KWSP building
Dark Green 2 (9) Maybank
Refined hotspots after applying sniping spot quality ratio
No Building Degree Ratio MCA Total Score1 Sultan Abdul Samad building 110 0.611 86 52.5462 Royal Club Building 63 0.35 79 27.653 Bangunan Bukit Aman 44 0.244 65 15.864 Merdeka Square 30 0.167 79 13.1935 Church 17 0.094 72 6.7686 OCBC Building 14 0.077 65 5.0057 Public library 12 0.067 72 4.8248 Daya Bumi Building 12 0.067 58 3.8869 Bangunan Pertanian 9 0.05 72 3.610 DBKL Building 10 0.055 58 3.1911 Bank Rakyat Building 8 0.044 72 3.16812 CIMB Building 7 0.039 65 2.53513 Muamalat Building 7 0.039 58 2.26214 HSBC Building 4 0.022 72 1.58415 Tradewind building 7 0.039 30 1.1716 Bank Negara 5 0.027 23 0.62117 KWSP building 3 0.017 16 0.27218 Maybank 3 0.017 9 0.153
Rain effects
Rain effects: heavy
Control
Simulated for desert eagle
2D as buffer!
Rain effects: heavy; simulated for desert eagle and M16A
Rain effects for Draganov (800m)
Light
medium
heavy
In-situ verifications
Visibility analysis of 18 security hotspots
(a) Sultan Abdul Samad Building (100%)
(d) Public Library (38%) (c) Church (29.17%)
(d) Royal Club Building
(86.84%)
Assessments
randomly selected a total of 30 check points within the generated model
RMSE for planimetric and vertical accuracies are +1.129 and +1.288m, respectively.
Overall accuracy (RMSE) is +1.713m
Comparable to LiDAR generated DEM (Sun et al 2008; Muane 2001)
Conclusion
Demonstrated the use of integrated fine satellite remote sensing with airborne LiDAR data for identifying possible security hotspots in context of snipping from vantage points;
3D urban city block offers very effective visualization for security hotspot analysis;
Method demonstrated offers more practicality appeals to security industry and related decision makers;
Contribute to upcoming trend in the uses of spatial technology in security analysis in many aspects of public safety and homeland security.