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


Rondonia – Matched Filter


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


Human Impacts: Fire in Africa

Roads increase probability of burns


Human Impacts: DMSP Fires in Indonesia

Fires influenced by ENSO and Global Warming

Climate influenced byCO2 emissions


Roads and Deforestation in the Amazon

Rondonia 1975

Rondonia 1992

Source – USGS Earthshots


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


UncertainHuman Futures

Increasing populations strain food production

Increasing temperature strain tropical climates


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


Utility Definition

• Consistency• Accuracy • Availability• Repeatability


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


Example of a High Utility ‘Physical’ Dataset

• USGS ETOPO30– 30 m Digital Elevations

– Global Coverage

– Universally Available

– Many derived products• Surface topology

• Stream networks


Example of a High Utility DatasetDigital Chart of the World

• 1:1,000,000 • global data • Created by ESRI• Repeatable?


GRD Potential Data Sources

Spectral Bands

Spa

tial

[m

2 ]

100 101 102 103

100

101

102

103

IKONOS

TM

AVHRR

AVIRIS

InexpensiveWidely Available


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


Matched Filtering-I

Rotate Data CloudTo Maximize Signal


Clustered Matched Filtering-II


MF exampleTM Rondonia 1998 – Bands 345


Rondonia Example – Bands 123


Rondonia – Matched Filter


Rondonia – Hi Pass – Band 1

-1 -1 -1

-1 8 -1

-1 -1 -1


Rondonia – 1998 – Local Range


Rondonia1996 SMA Error


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

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