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Oracle Spatial User Conference!

May 19, 2011 Ronald Reagan Building and International Trade Center

Washington, DC USA

Matt McCue Application Architect/Developer – Woolpert Inc.

Joseph Seppi Program Manager – Woolpert Inc.

May 2011 Oracle Spatial User Conference

Challenges and Advancements in Remotely Sensed Data Storage


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


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

Petabytes

Terrabytes

2010 2020

Source: DARPA 8/16/2010

Annual Sensor Data Volume

Storage & Transport Capacity

TCPED Convergence

speed

accuracy

volume

Reconnaissance

Photogrammetry

Surveillance


More payloads in service

All values are estimates


Higher density, multi-purpose sensors

•  Improvements in digital sensors and “prosumer” components have created new opportunities for small and medium-format airborne imaging systems

•  Passive & Active Systems


More Special Duty Payloads May 2011

Oracle Spatial User Conference

Compound Focal Plane Arrays

• Argus-IS

• 368 5mpix sensors to create a 1.8 gigapixel image at 12-15 frames/second


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


More persistence

•  Airborne persistent surveillance for forensic analysis/playback

•  Change detection •  Even though we refresh more frequently we

still tend to keep everything


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)


More Modalities

High Altitude Long Stand Off Sensors

Persistent Staring Wide Area Sensors

Small UAV EO and IR Sensors

Ground based Sensors

More Modalities… May 2011 Oracle Spatial User Conference

More Modalities

Staring RF (Anchor)

Staring EO / IR Close In Sensing


More 3D

From a single collection:

DEM – Bare Earth

DSM – Above Ground Features

INT - Intensity

3D from INT

Fused DSM and R,G,B


More perspectives

•  Layered sensing and sensor fusion

•  Oblique imagery (x5) •  Street View •  Mobile Mapping &

Terrestrial LiDAR •  EO-colorized point

clouds •  3D renderings


More video

•  Computer vision & photogrammetry applied to FMV to produce moving ortho-image mosaics

•  Rapid 3D models from single moving cameras


More phenomenologies

•  Automated feature extraction creates more derivative products from single collects


Cache

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


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


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


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


Sensor Data Fusion SAFE SWE SAFE + SWE Future Work


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


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.


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.


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.


SAFE

•  Portability •  Primarily build in Java •  Uses open source technologies:

•  Apache Tomcat •  Active MQ •  GDAL

•  Oracle spatial database.


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.


SAFE Diagram May 2011 Oracle Spatial User Conference

SAFE

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


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.


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.


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.


SAFE

•  Analysis Example: building of interest


SAFE

•  Analysis Example: image containing building


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: http://www.opengeospatial.org/

projects/groups/sensorweb


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.


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.


SWE

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


SWE May 2011 Oracle Spatial User Conference

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

SAFE + SWE

•  Uses a 52 North implementation of a SWE SOS.

•  Functionality includes: •  Core SOS, •  Transactional SOS and •  Additional SOS operations

•  Custom client


SAFE + SWE

•  Core SOS operations: •  GetCapabilities •  GetObservation – data encoded in O&M format. •  DescribeSensor


SAFE + SWE

•  Transactional SOS operations: •  RegisterSensor •  InsertObservation


SAFE + SWE

•  Client program •  Control a Pan-Tilt-Zoom camera. •  Access traffic cameras.


SAFE + SWE

•  Client Diagram


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).


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