U C S B GEOGRAPHY 8/6/2001NCRST Building A Global Road Database? Possibilities and Techniques for...
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Transcript of U C S B GEOGRAPHY 8/6/2001NCRST Building A Global Road Database? Possibilities and Techniques for...
8/6/2001 NCRST U C S B GEOGRAPHY
Building A Global Road Database? Possibilities and Techniques for Mapping Rural
Roads
Chris Funk
8/6/2001 NCRST U C S B GEOGRAPHY
Rondonia – Matched Filter
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Overview• Motivation– Q: Why build a Global Database of Roads?– A: There is only one world
• ‘Nature’ <> ‘Society’ • (ecologos) <> (economos)• Developed <> undeveloped
– Roads link societies to nature– communities to the global economy
• What defines utility– Consistent, Accurate, Available, Repeatable (CAAR)– Examples of Global Databases: DCW, ETOPO30
• Global Road Database– sources of information– Algorithms
• Matched Filter• Multi-spectral Analysis• Texture Analysis
8/6/2001 NCRST U C S B GEOGRAPHY
Human Impacts: Fire in Africa
Roads increase probability of burns
8/6/2001 NCRST U C S B GEOGRAPHY
Human Impacts: DMSP Fires in Indonesia
Fires influenced by ENSO and Global Warming
Climate influenced byCO2 emissions
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Roads and Deforestation in the Amazon
Rondonia 1975
Rondonia 1992
Source – USGS Earthshots
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GRD Application – Disaster Mitigation
• Case Study: Flooding in Mozambique
• Context: in Winter of 2000 tropical cyclones brought massive flooding to Southern Africa
• Largest single threat was lack of access to good drinking water
• Improved knowledge of roads would have aided relief efforts
Images www.disasterrelief .orgTaken at Relief Station at Gode
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UncertainHuman Futures
Increasing populations strain food production
Increasing temperature strain tropical climates
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Solution?
• Improve current knowledge by harnessing the power of geographic science
• Improved knowledge increases the quality of response
Data
Knowledge
Wisdom
Action
RS
GIS
.txt
Policy
8/6/2001 NCRST U C S B GEOGRAPHY
Utility Definition
• Consistency• Accuracy • Availability• Repeatability
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Geographic Science Provides Utility
Spectral Libraries and Spectral Analysis methods are tied to invariant physical properties of stuff
• Remote Sensing techniques can be applied uniformly across space
• Remote Sensing techniques can be applied uniformly across time
8/6/2001 NCRST U C S B GEOGRAPHY
Example of a High Utility ‘Physical’ Dataset
• USGS ETOPO30– 30 m Digital Elevations
– Global Coverage
– Universally Available
– Many derived products• Surface topology
• Stream networks
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Example of a High Utility DatasetDigital Chart of the World
• 1:1,000,000 • global data • Created by ESRI• Repeatable?
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GRD Potential Data Sources
Spectral Bands
Spa
tial
[m
2 ]
100 101 102 103
100
101
102
103
IKONOS
TM
AVHRR
AVIRIS
InexpensiveWidely Available
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GRD – Potential Algorithms
• Matched Filtering– Sub-pixel detection strategy– Applicable where spectral signal is distinct, but weak
• Spectral Mixture Analysis– Breaks pixel into sub-components– Useful when road has strong soil component– Roads can also appear as high error pixels
• Texture Analysis– Use spatial information to isolate road pixels– Applicable in situations where no systematic difference in road
material exists
8/6/2001 NCRST U C S B GEOGRAPHY
Matched Filtering-I
Rotate Data CloudTo Maximize Signal
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Clustered Matched Filtering-II
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MF exampleTM Rondonia 1998 – Bands 345
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Rondonia Example – Bands 123
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Rondonia – Matched Filter
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Rondonia – Hi Pass – Band 1
-1 -1 -1
-1 8 -1
-1 -1 -1
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Rondonia – 1998 – Local Range
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Rondonia1996 SMA Error
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Summary
• Extraction of Rural Roads from TM imagery seems practical and plausible
• Library-based spectral techniques perform well • We can and should build a global road database:
– Based on TM imagery– 100% coverage– ‘easily’ updatable– freely available
• Future directions– Improved spectral libraries– Santa Barbara Testbed – algorithm evaluation– Application/testing of rural road extraction techniques in US and
Brazil