Remote Sensing Systems, Remote Sensing Systems, Geographic Information Systems, Geographic Information Systems,

andandthe Classification of Urban Terrainthe Classification of Urban Terrain

Fred CameronFred Cameron

Operational Research Advisor toOperational Research Advisor to

Director General Land Combat DevelopmentDirector General Land Combat Development

Kingston, OntarioKingston, Ontario

OutlineOutline

• Introduction and Historical Material– Ellefsen’s Study from 1980-86– Military Doctrine

• Sensors• Geographic Information Systems (GIS) and

Associated Analytical Tools• Queen’s University Study

– Geographical Information Systems and Remote Sensing– Metadata and Interoperability – DIGEST Standard– Artificial Intelligence and Rule Based Systems– Categorization, Land Cover, Land Use, and Semantics

• Models, Simulation, and Operational Research

Urban Terrain Zone ClassificationUrban Terrain Zone Classification

• Ellefsen’s Study, circa 1987– ‘Procedures’ and ‘Definitions’– ‘Urban Morphology’– ‘The Growth of Cities’ and ‘Structures and

Materials’– ‘Classifications’– ‘Quality Control’ / ‘Validation’ – Comparison

to Ground Truth– Recommendations

Ellefsen’s RecommendationsEllefsen’s Recommendations

• Develop terrain databases for many world cities– For theoretical studies– To have an inventory for operations

• Develop spatial models of urban terrain• Anticipate new types of feature

– Construction techniques will continue to advance– Local conditions may induce innovative techniques

• Share the knowledge on urban characteristics widely• Direct the concept of urban terrain zones at combat

development and weapon development communities

Source: Ellefsen, Urban Terrain Zone Characteristics, 1987

World Cities in the Study World Cities in the Study

Urban Construction: Example of OptionsUrban Construction: Example of Options

Stone

Brick

Concrete block

Block/brick

M asonry

Pour in place

Tilt up

Box-w all

Concrete

O ption 1M ass constructuion

Half tim bered

W ood, stucco sheathingBrick sheathing

Balloon(lt w ooden fram e)

W ooden post and lintel

Heavy cladding

G lass, plastic , sheet metalBrick veneer, lt w eightconcrete aggregate

Light cladding

Steel/reinforced concrete

O ption 2Fram ed construction

Problem:Construct a sound, useful, econom ical building

Source: Ellefsen, Urban Terrain Zone Characteristics, 1987

Some Factors Influencing Some Factors Influencing Construction OptionsConstruction Options

• Epoch of construction• Local knowledge of architects and

engineers: structures and materials• Availability of materials• Abilities of the workforce • Local ‘political considerations’

– Mood of the citizens and their leaders– Zoning restrictions– Desire for ‘public display’

Urban Terrain Zone Classification SystemUrban Terrain Zone Classification SystemA – Attached

• A1 – Core area• A2 – Apartments/hotels, core

periphery• A3 – Apartments/row houses• A4 – Industrial/storage, full urban

form• A5 – Old commercial ribbons• A9 – Old core, vestigial

Dc – Detached, Close-set• Dc1 – Urban redeveloped core area • Dc2 – Apartments, >75% ground

coverage• Dc3 – Houses, >75% ground

coverage• Dc4 – Industrial/storage• Dc5 – Outer city• Dc7 – Engulfed agricultural village• Dc8 – Shanty towns

Do – Detached, Open-set• Do1 – Shopping centers• Do2 – Apartments, <75% ground

coverage• Do3 – Houses, <75% ground

coverage• Do4 – Industrial/storage, truck

related• Do5 – New commercial ribbons• Do6 – Administrative cultural

Others• ON – Open Space, not built upon• OW – Open Space, wooded, not built

upon• Do31 – Leased garden areas with

small structures

Ellefsen’s Categories – A SampleEllefsen’s Categories – A Sample

Zone A1 (core area)Zone A2 (apartments,

hotels, core periphery)

Zone A3 (attached houses) Zone A9 (old core, vestigial)

Source: Ellefsen, Urban Terrain Zone Characteristics, 1987

Modified Ellefsen Categories Modified Ellefsen Categories • FM 3-06.11 (supercedes FM 90-

10-1) Combined Arms Operations in Urban Terrain, US Army, February 2002

• FM 34-130 July 1994 Intelligence Preparation of the Battlefield, US Army, July 1994

• Jamison Jo Medby, Russell W. Glenn, Street Smart: Intelligence Preparation of the Battlefield for Urban Operations, RAND, MR-1287-A, 2002

• Sean J. A. Edwards, Mars Unmasked: The Changing Face of Urban Operations, RAND, MR-1173-A, 2000

Source: FM 3-06.11, Chapter 2 Urban Analysis

Sensors – Example: The Rapid Terrain Visualization (RTV) Aircraft

LIDAR LIDAR WorkstationWorkstation

LIDAR LIDAR WorkstationWorkstation

IFSAR IFSAR AntennasAntennas

IFSAR IFSAR AntennasAntennas

IFSAR IFSAR WorkstationsWorkstations

IFSAR IFSAR WorkstationsWorkstations

Source: US Army’s Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager

LiDAR Light Detection and RangingIFSAR Interferometric Synthetic Aperture Radar

Collection SpecificationsCollection Specifications

Source: Turner and Moscoco, 2002

Characteristic LiDAR IFSARFlight Altitude 2000 m above ground level 6000 m above ground level

Swath Width 540 mLevel III: 1600 mLevel IV: 630 m

Flight Speed 140 knots 180 knots

Collection Rate 25 square kilometers per hourLevel III: 50 sq. km per hourLevel IV: 25 sq. km per hour

ProcessingRate 3 hrs processing per 1 hr flight

Real-time onboardprocessing

Time Day or night Day or night

Weather No clouds, minimalprecipitation

No limitations

LiDAR Light Detection and RangingIFSAR Interferometric Synthetic Aperture Radar

Level I(Current Archive)

Level II(SRTM)

Level III(RTV)

Level IV(RTV)

Level V(RTV)

90 m spacing 30 m spacing 10 m spacing 3 m spacing 1 m spacing

Notional Difference in DTED ResolutionNotional Difference in DTED Resolution

DTED = Digital Terrain Elevation Data SRTM = Shuttle Radar Topographic Mission RTV = Rapid Terrain Visualization projectSource: US Army’s Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager

LiDAR - Multiple Laser ReturnsLiDAR - Multiple Laser Returns

Source: US Army’s Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager

Assume: • first return is from top of tree canopy• last return is from the ‘ground’

Example: Line of Sight from LiDAR DataExample: Line of Sight from LiDAR Data

• ArcGIS Military Analyst methods applied to LiDAR data from Toronto

Source: Harrap and Lim, ‘Terrain Classification for Military Operations in Urban Areas’, 2003

Example: View Field from a PointExample: View Field from a Point

Field of view (green) from top of the Provincial Legislature in Toronto

Source: Harrap and Lim, ‘Terrain Classification for Military Operations in Urban Areas’, 2003

Example:Example:Building Extraction to GIS ShapesBuilding Extraction to GIS Shapes

• With some semantic assumptions, extraction of features can build GIS data with minimal intervention by an operator

• LIDAR Analyst, developed by Dr. Vincent Tao at York University, Toronto, does a good job on urban areas as shown.

Source: Harrap and Lim, ‘Terrain Classification for Military Operations in Urban Areas’, 2003

Pickering, Ontario

Bonn, Germany

Pan-chromatic Imagery

Classification by Alternate Methods

Classification by eCognition

Example from eCognitionExample from eCognition

Source: Birgit Mittelberg ‘Pixel Versus Object:A method comparison for analysing urban areas with VHR [very high resolution] data’ see http://www.definiens-imaging.com

Roles and UnderstandingRoles and Understanding

• Level of understanding is determined by process

• For Example (after Pigeon, 2002)– Sniper needs to have high spatial and environmental

texture resolution (i.e., the semantics of the immediate cover environment)

– Search and Rescue (SAR) pilot needs to have low spatial accuracy and high environmental texture resolution (i.e., the semantics of the landing zone environment)

– Blast models (physical) need medium to high spatial accuracy and accurate semantics of the target area

Modeling, Simulation, and OR AnalysisModeling, Simulation, and OR Analysis

• For Theoretical Analysis in Simulation: – Need representative terrain… but also– Need to know selected terrain is representative– Need to know ‘land use’ for entity behaviour

• For Rehearsal Analysis in Simulation:– Need actual terrain– Need to know ‘land use’ for entity behaviour

• For Mathematical Analysis:– Need terrain with appropriate characteristics– Do not necessarily need extensive raw data on

terrain, but need to know that assumptions in the model (sensor ranges, weapons ranges, lethal effects, etc.) are appropriate

MOUT FACT = Military Operations in Urban Environment Focus Area Collaborative Team

Models Covered by the Models Covered by the MOUT FACT AssessmentMOUT FACT Assessment

• Integrated Unit Simulation System (IUSS) – “constructive, force-on-force model, for assessing the combat worth of systems

and sub-systems for both individuals and small unit dismounted warfighters in high-resolution combat operations”

• CombatXXI

– “high-resolution, closed-form analysis tool for the assessment of new technologies”

– “replacement for CASTFOREM”

• AMSAA Infantry MOUT Simulation (AIMS)– “small unit combat simulation designed to support AMSAA systems

performance analyses of infantry systems”

• OneSAF Objective System– “composable, next generation computer-generated force (CGF) that can

represent a full range of operations, systems, and control processes from the individual combatant and platform level to battalion level”

Source: https://www.moutfact.army.mil/frameset.asp?sec=research

Assessment of Current Assessment of Current ModelsModels

• Indirect Fire - Issues: effects on buildings, building contents, roads, bridges and subterranean infrastructure

• Tactical Communications - Issues: VHF radios, lack of propagation studies

• Mobility - Issues: NATO Reference Mobility Model V.2, decision-making on alternative paths through terrain

• Direct Fire - Issues: clearing buildings and hallways, deformable surfaces, non-lethal weapons, collateral damage, short-range engagements

• Wide Area Surveillance - Issues: radar, acoustics, SIGINT• Search and Target Acquisition - Issues: ACQUIRE model,

background noise, terrain and urban propagation, cues, shadows, rules of engagement, individual v. crew performance, and multiple targets

Source: Crino, ‘Representation of Urban Operations in Military Models and Simulations’

Model Assessment FindingsModel Assessment Findings

Source: Crino, ‘Representation of Urban Operations in Military Models and Simulations’

Needs ImprovementAdequate Poor

ConclusionsConclusions

• Dramatic remote sensing improvements for urban environments, e.g., LiDAR, IFSAR, multi-spectral and hyper-spectral cameras

• Rapid development in functionality of Geographic Information Systems, including imagery handling and automatic and semi-automatic classification

• Operational research practitioners need better understanding of cities and how they operate

• Coincidentally, so do military clients

ReferencesReferences• Scott T. Crino, ‘Representation of Urban Operations in Military Models and Simulations’ in

Proceedings of the 2001 Winter Simulation Conference, Dec 2001• Dispatches – “Training for Urban Operations”, Vol 9, No 2, Army Lessons Learned Centre,

Kingston, Ontario, May 2002• J-P Donnay, MJ Barnsley, and PA Longley, Remote Sensing and Urban Analysis, Taylor and

Francis, London and New York, 2001• Richard Ellefsen, Urban Terrain Zone Characteristics, US Army Human Engineering Lab,

Aberdeen, MD, 1987 • Rob Harrap and Kevin Lim, ‘Terrain Classification for Military Operations in Urban Areas’,

Queen’s University, Kingston, 2003• Jamison Jo Medby and Russell W. Glenn, Street Smart: Intelligence Preparation of the Battlefield

for Urban Operations, RAND, MR-1287-A, 2002• Bryan Mercer, ‘Comparing LIDAR and IFSAR: What can you expect?’ Proceedings of

Photogrammetric Week 2001• Birgit Mittelberg ‘Pixel Versus Object:A method comparison for analysing urban areas with VHR

[very high resolution] data’ Brochure from eCognition, see http://www.definiens-imaging.com• Luc Pigeon, ‘Concept of C4I data fusion command center for urban operations’ in Proceedings of

the 7th International Command and Control Research and Technology Symposium , Quebec, Sep 2002

• Jeffrey T. Turner and Christian P. Moscoso, ‘21st Century Terrain – Entering The Urban World’, Rapid Terrain Visualization Website: https://peoiewswebinfo.monmouth.army.mil/JPSD/rtv.htm , 2002