automating the analysis of remotely-sensed data © macaulay land use research institute, l3-storm,...

Automating the analysis of

remotely-sensed data

© Macaulay Land Use Research Institute, L3-Storm, Redleaf Systems, 2000

Landsat TM imagery enhanced by Macaulay

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ETORA-II

• A toolkit to facilitate the automatic analysis of remotely-sensed imagery

– Faster application development through the use of Commercial Off-the-Shelf (COTS) products; and

– Cheaper application deployment through automation

• An Environment for Task ORientated Analysis

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Presentation Overview

• An ETORA-II application

• The case for automating the analysis of remotely-sensed data

• Some hard problems, and the limitations of available software

• What is required for automation, and why L3-Storm and Redleaf?

• ETORA-II features

• Summary

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• Developed for the Macaulay Land Use Research Institute, Aberdeen, Scotland

• Problem: the need to update the Land Cover of Scotland (1988) dataset

– Census of Scotland’s land cover (>1300 classes);– 20 person years to interpret and digitise aerial

photography;– ~£2m

• Solution: SYMOLAC-II, constructed using ETORA-II– Complex rules/regulations, involving multiple datasets

requiring a wide range of expertise;– Leading to an automated information system for

Scotland’s land cover

ETORA-II

The LCS8 dataset

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• A collaborative effort– MLURI as an end user;– Redleaf Systems as a software developer;– L3-Storm as a software developer/provider of COTS

products;– IPR agreement in place

ETORA-II

The LCS8 dataset

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• There is an industry-wide need to increase automation

The Case for Automation

RS data markets

RS data volume and variety, availability, awareness

An increasing need for human expertise... … and an increasing need

for automation

• Why?– The cost of delivering information to end-users

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Some Hard Problems

• There is more to automation than chaining together a series of operations

– many different approaches may be possible;– these approaches may not be linear;– the data and knowledge available for one geographical area may not

exist for another, or could be of lesser quality;– the results may be conflicting;– non-mathematical knowledge can improve results and increase

efficiency;– processing explanations must be easily accessible; and– new data, knowledge and software resources can become available at

any time.

• GIS/GIP packages have not been designed to support such processing

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Achieving Automation

• Automation must involve – Knowledge of the problem domain

• A dedicated reasoning component is required

– Command and control of GIS and GIP• The COTS approach;

• Reuse legacy systems

• A system capable of such automation must be– Flexible

• to accommodate the varied data, processing, knowledge, and reasoning strategies necessary to solve a problem;

– Extensible• to allow new data, knowledge, and software resources to be readily and cheaply

utilised; and

– Adaptable• enabling the system to work around typical complexities

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• What are ETORA-II’s underlying features, those that make it suited to supporting automated applications

– Flexible• to accommodate the varied data, processing, knowledge, and reasoning

strategies necessary to solve a problem;


utilised; and


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ETORA-II Flexibility

• COTS design– re-use of commercial software

Arc/INFO

ArcView

PV-WAVE

COTS Products

G2

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ETORA-II Flexibility: COTS

COTS

ETORA-II

G2Arc/INFO

PV-WAVE

Product

ArcView

Imagine PCI

ER-Mapper

Legacy systemsFurtherbridges...

EDP;RPC;CORBA;COM;Java

SYMOLAC-II, … ? Project

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ETORA-II Flexibility: COTS

COTS

ETORA-II

G2Arc/INFO

PV-WAVE

Product

ArcView

Imagine PCI

ER-Mapper

Legacy systemsFuturebridges...

EDP;RPC;CORBA;COM;Java

SYMOLAC-II, … Project

• Contributions– Eliminate excessive development;– Reduce risk; and– Minimise programme costs

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• Experts– agent-like collections of knowledge;

Experts

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ETORA-II Flexibility: Experts

• Collections of application-specific knowledge, represented within the G2 component

– Blackboard problem-solving model

• Experts can utilise G2’s powerful knowledge representation and reasoning capability, and command external software

• Planning and scheduling experts– Solution methodology is dynamic

• Concurrent and/or sequential responses

• Contributions– domain knowledge can be modularised;– many types of representation and reasoning are possible;– iterative, opportunistic reasoning, and “good enough” solutions;– others...

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• Experts– agent-like collections of knowledge

• Uncertainty handling– hypotheses and evidence

Hypotheses and Evidence

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ETORA-II Flexibility: Uncertainty

• Derived domain knowledge can be represented as hypotheses, with zero or more evidence items to believe or disbelieve them;

• Hypotheses and evidence are both created by experts;

• Uncertainty is a function of the belief;

• Based on Endorsement Theory (Cohen, 1986)

• Contributions– uncertainty considered throughout an application;– can support different uncertainty representations.

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• Experts– agent-like collections of knowledge

• Uncertainty handling– hypotheses and evidence

• Explanations– HTML statements produced by experts Explanations

?

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ETORA-II Flexibility: Explanations

• Experts associate statements with evidence– explanations do not just record expert activity

• Accessible outwith ETORA-II using any browser

• Contributions:– solution development;– value-added products

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utilised; and


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ETORA-II Extensibility

• New knowledge and data resources must be readily accessible– This property emerges from the use of experts– Adding new data– Adding new knowledge

• New software resources must be readily accessible– PCI, Imagine, GRASS, ER-Mapper, etc.– G2 supports: data, object, and RPC connectivity over TCP/IP and

DECnet; ActiveX, Java, CORBA• The COTS advantage

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utilised; and


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ETORA-II Adaptability

• Common complexities that require adaptation– uncertainties exist within the reasoning processes;– there is more than one interpretation of an area;– not all areas of interest can be analysed using the most effective data;– not all areas of interest can be analysed using the most effective

knowledge

• The property emerges from the use of experts

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• This capability is described as task-orientation: the ability to focus on each analysis task, employing the most effective data, method, and software resources to each;

• The specific features of a system capable of building task-orientated applications are:

– The ability to use multi-source data;– The ability to represent and reason with disparate knowledge;– The ability to dynamically adapt analyses to the specific nature of each

task, and the “real-world” aspects that might introduce uncertainty;– A bridged COTS environment; and– The ability to generate detailed reasoning explanations.

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Summary

• Automation is not a trivial process;

• GIS/GIP packages do not have the requisite capability;

• ETORA-II achieves this capability via– A COTS design;– Experts;– Hypotheses and evidence; and– Explanations

• The toolkit is– Flexible;– Extensible; and– Adaptable

• This capability is termed task-orientated

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Commercialisation

• Our partnership– MLURI as an end user– Redleaf as a software developer– L3-Storm as a software developer, and provider of COTS products

• We believe that task-orientation is necessary to facilitate greater automation within the analysis of remotely-sensed data;

• Redleaf and L3-Storm are seeking a complementary partner to further the development of ETORA-II towards a commercially viable product

– A commercial data/application provider with interests in global markets

automating the analysis of remotely-sensed data © macaulay land use research institute, l3-storm,...

Documents

varied data

knowledge available

linearthe data

software resources

accessible andnew data

software developerl3storm

increasing need

land cover of scotland