Oracle Spatial User Conference! May 19, 2011 Ronald Reagan Building and International Trade Center Washington, DC USA

Transcription

1 ! May 19, 2011 Ronald Reagan Building and International Trade Center Washington, DC USA

2 Matt McCue Application Architect/Developer Woolpert Inc. Joseph Seppi Program Manager Woolpert Inc.

3 Challenges and Advancements in Remotely Sensed Data Storage

4 More Data More payloads in service (civil and military) Higher density, higher frequency sensors More 3D More persistence More full-motion video Higher resolution (smaller GSD) More phenomenologies More modalities More cache More metadata More views & perspectives

5 Outlook Today s DoD theatre sensor volumes alone exceed capacity to store and transport Expected to reach 1 petabyte/day of data soon. Even if data storage cost continues to decline by 50%/year, 1 yottabyte would still cost $3.2 trillion in 2015 Yottabytes Zettabytes Exabytes Annual Sensor Data Volume Petabytes Terrabytes Storage & Transport Capacity Source: DARPA 8/16/2010

6 TCPED Convergence accuracy Photogrammetry Surveillance Reconnaissance volume speed

7 More payloads in service All values are estimates

8 Higher density, multipurpose sensors Improvements in digital sensors and prosumer components have created new opportunities for small and medium-format airborne imaging systems Passive & Active Systems

9 More Special Duty Payloads

10 Compound Focal Plane Arrays Argus-IS 368 5mpix sensors to create a 1.8 gigapixel image at frames/second

11 Small & Medium Format Arrays Guardian Eagle WAAS Sensor Wide Area Sensor Head 6 16 Megapixel Cameras 1 Frame per Second 10 Megabytes per Second Zoom / Spot Sensor Head 1 16 Megapixel Camera I Frame per Second 1.5 Megabytes per Second

12 More persistence Airborne persistent surveillance for forensic analysis/playback Change detection Even though we refresh more frequently we still tend to keep everything

13 More modalities We create different products for different consumers of the same imagery. For aerial photography alone: RAW and uncompressed 12 & 16-bit for image analysts Single-frame NITF for analysts 8-bit GeoTIFF Compressed MrSID, JPEG and GeoPDF Pyramided tile-cache for web users Anaglyphs, Holographs, Texture Maps Video (MPEG, MP4, MJPEG, WMV)

14 More Modalities High Altitude Long Stand Off Sensors Persistent Staring Wide Area Sensors Small UAV EO and IR Sensors Ground based Sensors

15 More Modalities

16 More Modalities Staring EO / IR Close In Sensing Staring RF (Anchor)

17 More 3D From a single collection: DEM Bare Earth DSM Above Ground Features INT - Intensity 3D from INT Fused DSM and R,G,B

18 More perspectives Layered sensing and sensor fusion Oblique imagery (x5) Street View Mobile Mapping & Terrestrial LiDAR EO-colorized point clouds 3D renderings

19 More video Computer vision & photogrammetry applied to FMV to produce moving ortho-image mosaics Rapid 3D models from single moving cameras

20 More phenomenologies Automated feature extraction creates more derivative products from single collects

21 Cache We pyramid everything for easy transmission via the web or portable media while persisting and duplicating the source data

22 More metadata We want to preserve everything from a mission not just project-level metadata Flight layouts, logs, weather reports Trajectory (SBET) and GPS data Camera data (Exif) Triangulation (AT) Ground Control Files QA/QC results

23 Data Management Even though storage is cheap, it can not linearly-scale to keep up with the avalanche of geospatial data being generated The problem is not that our sensors produce too much data; the problem is the structure of the data and how we manage it. Defense Advanced Research Projects Agency Adaptive Execution Office

24 Way Ahead Compressive models, lossless abstractions of high resolution data Containerization new ways to encode and decode compound geospatial data sets Deduplication defining and persisting a single source of truth and creating on-demand derivatives Oracle s SRDBMS and middleware are being used effectively and provide a robust scalable architecture to effectively tackle the challenges that lie ahead

25 Sensor Data Fusion SAFE SWE SAFE + SWE Future Work

26 SAFE Situational Awareness Fusion Engine Purpose: Provide a framework for: Aggregation and Analysis of spatial and non-spatial data. Design goals: Interoperability Scalability Efficiency Portability Simplicity Demonstrates a use-case scenario for tiered remote sensing How Oracle technologies are or could be further used

27 SAFE Interoperability Leverage existing web standards for: Data Loading HTTP Post Data Retrieval WMS/WFS System Management Web Services This allows for clients on most platforms to communicate with the SAFE. Java, C#, Visual C++, ios, Android etc. Means that the SAFE is open to computers, mobile devices and embedded systems.

28 SAFE Scalability (Machine-Software-Data) Use a Message Oriented Middleware (MOM) to distribute processing. Allows entire SAFE system to be run on one or multiple machines system performance and load can determine this. Extend data loader for new data types. Each data type has its own Workflow.

29 SAFE Efficiency Efficiently store a variety of data types. Raster Vector LiDAR Non-spatial Only store the data once. Efficiently search large, and disparate data sets. Run spatial queries, instead of manual searches across a file system.

30 SAFE Portability Primarily build in Java Uses open source technologies: Apache Tomcat Active MQ GDAL Oracle spatial database.

31 SAFE Simplicity Simple Java API for loading data. File Stream File system watcher program to load data. Multithreaded Can have multiple instance running concurrently. WMS, WFS and JAX Web Service for consuming data.

32 SAFE Diagram

33 SAFE Supported data types: NITF Tiff GeoTiff Jpeg Jpeg2000 Non-geo referenced imagery Shape files LIDAR point clouds

34 SAFE Integrated Systems: Layered Sensing Testbed- an Air Force Research Lab (AFRL) layered sensing demonstration environment with embedded NASA WorldWind globe. SmartView Woolpert s Aerial Imagery viewer with embedded NASA WorldWind Globe. CowPath Disease Tracking System An AFRL research application designed to track the spread of disease. Connected both iphone and Droid. Integrated Systems (continued): ArcGIS WMS ArcMap integration.

35 SAFE Analysis: Various raster data formats normalized into SDO_GEORASTER. Vector data normalized into SDO_GEOMETRY. Spatially referenced and indexed. Can execute Spatial Proximity and/or Spatial Interaction queries using SDO library.

36 SAFE Analysis Example: Find all imagery that intersects with a known building. Run Spatial Interaction query SDO_ANYINTERACT(A,Q).could also use SDO_RELATE( ) Query run over data collected over days, weeks, months, years in a matter of seconds.

37 SAFE Analysis Example: building of interest

38 SAFE Analysis Example: image containing building

39 SWE Sensor Web Enablement A set of open standards/specifications that turn web accessible sensors into a connected network called a Sensor Web. Developed and maintained by the Open Geospatial Consortium. Currently seven specifications, we use two. Read more at: projects/groups/sensorweb

40 SWE Targeted Functionality: Sensor Web Discovery of sensor systems and observations. Determine sensor capabilities. Geo-locate sensors automatically. Standardize sensor measurements. Sensor management/control. Publish/Subscribe to sensor alerts.

41 SWE Observations & Measurements (O&M) - standard model for representing and exchanging observation results. XMLSchema defines the XML encoding for measurements and observations. Data types Relationships of the data types Observation - is an event with a result. Result a value describing a phenomenon.

42 SWE Sensor Observation Service (SOS) - service that provides access to sensor systems and their observations.

43 SWE (OGC Sensor Web Enablement: Overview and High Level Architecture, 2007)

44 SAFE + SWE Uses a 52 North implementation of a SWE SOS. Functionality includes: Core SOS, Transactional SOS and Additional SOS operations Custom client

45 SAFE + SWE Core SOS operations: GetCapabilities GetObservation data encoded in O&M format. DescribeSensor

46 SAFE + SWE Transactional SOS operations: RegisterSensor InsertObservation

47 SAFE + SWE Client program Control a Pan-Tilt-Zoom camera. Access traffic cameras.

48 SAFE + SWE Client Diagram

49 Future Work Load other types of spatial data for analysis. Geo-located Twitter data. Intelligent control/coordination of sensors: Build AI agent framework for controlling devices. Expand SWE implementations to support sensor control. Sensor Model Language (SensorML).

50