1. Remote Sensing Systems, Geographic Information Systems, and the Classification of Urban Terrain Fred Cameron Operational Research Advisor to Director General Land Combat Development Kingston, Ontario Kingston, Ontario

2. Outline Introduction and Historical Material –Ellefsens Study from 1980-86 –Military Doctrine Sensors Geographic Information Systems (GIS) and Associated Analytical Tools Queens 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

3. Urban Terrain Zone Classification Ellefsens 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

4. Ellefsens 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

5. World Cities in the Study

6. Urban Construction: Example of Options Source: Ellefsen, Urban Terrain Zone Characteristics, 1987

7. Some Factors Influencing Construction 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

8. Urban Terrain Zone Classification System A – 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

9. Ellefsens 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

10. 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

11. Sensors – Example: The Rapid Terrain Visualization (RTV) Aircraft LIDAR Workstation IFSAR Antennas IFSAR Workstations Source: US Armys Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager LiDARLight Detection and Ranging IFSARInterferometric Synthetic Aperture Radar

12. Collection Specifications Source: Turner and Moscoco, 2002 LiDARLight Detection and Ranging IFSARInterferometric Synthetic Aperture Radar

13. Level I (Current Archive) Level II (SRTM) Level III (RTV) Level IV (RTV) Level V (RTV) 90 m spacing30 m spacing10 m spacing3 m spacing1 m spacing Notional Difference in DTED Resolution DTED = Digital Terrain Elevation Data SRTM = Shuttle Radar Topographic Mission RTV = Rapid Terrain Visualization project Source: US Armys Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager

14. LiDAR - Multiple Laser Returns Source: US Armys Rapid Terrain Visualization Project, Mr. Mike Hardaway, Technical Manager Assume:first return is from top of tree canopy last return is from the ground

15. Example: 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

16. Example: 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

17. Example: Building 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

18. Pickering, Ontario Bonn, Germany Pan-chromatic Imagery Classification by Alternate Methods Classification by eCognition Example 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

19. Roles 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

20. Modeling, 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

21. MOUT FACT = Military Operations in Urban Environment Focus Area Collaborative Team Models Covered by the MOUT 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 Combat XXI –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

22. Assessment of Current Models 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

23. Model Assessment Findings Source: Crino, Representation of Urban Operations in Military Models and Simulations Needs ImprovementAdequatePoor

24. Conclusions 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

25. References 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, Queens 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