CityGML+ARML Workshop

1. CityGML+ARML Workshop Discussion 1 Introduction Alex Karman, CTO and Co-Founder Revolutionary Machines, Inc.

2. Using CityGML As a Universal Index of the Physical World Our goal: a visual, browsable, 3D index into everything that intersects with the urban landscape • Groups, roles and responsibilities • Assets and resources • Workflows and processes • Analytics and intelligence

3. Our Approach • Use CityGML not as data, but as an index • Don’t modify CityGML to add more properties • Use XLINK hooks to external RDF data sets • Use SPARQL and Xquery to browse • Use the Semantic Web to add context to the urban environment • Use Augmented Reality to filter location-specific and role-specific information to actors

4. Benefits of this Approach • Fast federated search and retrieval • Interoperability over multidisciplinary data sets • Cooperation without coordination (“serendipity”) • Flexibility and Agility • Preservation of context, provenance, and assumptions • Enhanced fusion and collaboration • Enhanced support for polymorphism, inheritance, reification, and inference

5. History: OGC • The Open Geospatial Consortium is the epitome of successful standards bodies • In 15 years, it has promulgated over 30 specifications • The GIS industry has adopted and embraced these standards • The success of OGC standards has ushered in a new era of enhanced GIS interoperability, multidisciplinary activity, and coherent National Spatial Infrastructure policy

6. History: CityGML CityGML is a newly-adopted OGC standard for 3D mapping of urban terrain • It adds semantics to Geographic Markup Language (GML) so that computers (and people) can understand what structures are made of • It properly models interior and subterranean spaces • It properly models the land-structure interface • It has virtual surfaces so that the volumes of negative spaces can be computed • It integrates GIS (maps) with CAD (blueprints)

7. History: ARML • Augmented Reality Markup Language is a newly-adopted OGC specification for adding semantics to computer vision • For several years, computer vision frameworks such as OpenCV and Kinect have allowed devices as small as smart phones to recognize real-world objects in camera views from COLLADA files (from any angle) • ARML offers XML semantics to allow applications to comprehend and act appropriately upon what CV sees • ARML also offers a JavaScript event-driven model so that behaviors are easily encoded (or changed in real time) in thin client applications

8. Limitations (Until Now) • CityGML is extremely verbose • XML can only explain through containment • Describing a city space means big schemas and big XML instances • Even small CityGML data sets require large downloads and large CPU • The old API did not decouple ARML from COLLADA, so an implementer had to tackle the whole CV stack • There was never a way to combine information about what an object looks like on the outside (COLLADA) with what it is made out of (CityGML). Implementers had to tie behaviors directly to the appearance of an object, not the substance of an object • ARML allows us to divide and conquer: the CityGML folks can focus on tactical exploitation, the CV industry improves recognition

9. What Can You Do with CityGML? • Civil Engineering and Urban Planning • Resilience planning, disaster planning, emergency response • Supply chain, supply chain vulnerability, logistics • MOUT, targeting, damage assessment • Life-after-people longitudinal studies and policy • Chernobyl, Namie-Dachi • Back-fill building interiors and subterranean features onto existing 3D urban maps • Structural failure forensics (I-35 bridge collapse example) • Role-specific IAPs, SOPs • Auto-generated AARs • Dynamic COIs • eDiscovery (in the legal sense)

10. CityGML as a Real-World Index • Its utility is not just in telling you what a structural component is made of! • CityGML turns structural components into an index • Who designed, manufactured, delivered, installed, maintained, inspected, repaired it? Where are they now and how do I contact them? • What equipment, skills or materials are necessary to interact with it? Where and how do I get them? • Integration with CIA World Fact Book, World Development Indicators, Human Geography publications • CityGML lets analysts locate and quantify anything related to the structures in an area of interest with a single spatial query

11. CityGML as an IndexSupports Personalized Real-Time Plans • Incident Action Plans with real names, organization charts, communication plans, procedures, full FEMA Incident Command System document generation • Full Joint Field Office, Unified Incident Command assignment and staffing • Full NRF support (NIMS, NIPP, HSIP) • Full NIEM integration • ARML adds real-time, computerized vision prompting via heads-up display or smartphone

12. Demo We want to demonstrate the use of CityGML as an index. To do that, we will perform the following tasks: • Embed a link in the CityGML document to an external RDF resource, using XLINK. • Search for and retrieve that link in the CityGML document, using XQuery. • Query the external RDF resource, using SPARQL. • Parse the JSON result set, using Javascript. • Render the result, using OpenLayers. • Get more dynamic, using the Parliament semantic database and Python. There are step-by-step instructions at blog.rev-mac.com/node/8

13. Acronyms • AAR – After Action Report • AOI – Area of Interest • COI – Community of Interest • FGDC – Federal Geographic Data Committee • FSAM – Federal Segment Architecture Methodology • HSIP – Homeland Security Infrastructure Program • IAP – Incident Action Plan • MOUT – Military Operations in Urban Terrain • NIEM – National Information Exchange Model • NIMS – National Incident Managementsystem • NIPP – National Infrastructure Protection Plan • NRF – National Response Framework