<Insert Picture Here> Data Management Innovations for Massive Point Cloud, DEM, and 3D Vector Databases

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Data Management Innovations for Massive Point Cloud, DEM, and 3D Vector Databases

Xavier Lopez, Director, Product Management

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3D Data Management

• Technology Drivers: Challenges & Benefits

• Use Cases for 3D Data Management

• Overview of Spatial Databases

• Oracle 11g 3D Features

• Wrap-up

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Technology Drivers

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Technology & Business Trends

• Massive new sensor hardware capabilities

• Automated Data Capture / Model Creation (sensors)

• Increase productivity in 3D data management workflow

• Improve performance and scalability of existing workflows

• Bridging gap between point cloud surveys, GIS, CAD, BIM

• Propel 3D to Mainstream

• Mass Market: Consumer-focused systems

• Benefit from IT scalability, security, and reliability

• Files to Databases

• Merge Point Cloud content with other geospatial types

• Integrate attribute data with point cloud features

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Challenges: Managing Point Cloud Data

• Robust Data Management Challenges:

• High Density LIDAR: Sub-meter point spacing (billions of points)

• Combine with multi-spectral gridded data (terabytes of data)

• Versioning, Archiving (terabytes, petabytes)

• Back-up/recovery

• Data Transformation

• LIDAR point filtering, visualization, analysis

• Surfaces and 3D vector models

• Attribute Data Integration

• Leverage Grid computing, clustered servers

• Visualization & Analytics

• Integrate 3D models into business workflows

• Associate 3D objects/features to attributes

• Spatial query across point cloud features

• Managing updates

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What do Spatial Databases Bring?

• Scalability: Large seamless 3D scenes: Terabytes of objects

• Fast Retrieval: Geospatial (r-tree) Indexes on 3D point clouds

• Partitioning: Manage large seamless scenes

• Grid computing: Massive Data processing

• Interoperability: Fusion of aerial imagery, close-range airborne and ground video/LIDAR with traditional 2D vector models

• Spatial analysis: conduct traditional GIS queries on 3D scenes

• Transactional Updates

• Enterprise Integration: Integrate 3D models with business information.

• Versioning and Long Transaction Support:

• Data security, access control, encryption, authentication

• Open: Support by third party 3D viz and analysis tools

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Creating Value Added Data Products

CAD/BIM server

Point Cloud Repository

TIN server

3D Model server

Image server

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Data Collection Production Dissemination

& Exploitation

LIDAR Surveys Photogrammetry Aerial Photos Satellite Imagery COTS Scenes CAD Designs

Model/Scene Generation Image Texture Wrapping Versioning

Editing/Updates Quality Control Volumetric Analysis

3D Mapping Fly Through 3D analysis

Engineering Design Predictive Analysis Navigation Systems

Spatial DBMS in a Production Workflow:

LIDAR Ortho-

Photos CAD

Spatial RDBMS

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3D Information Management Challenges

• Scalability - large data volume

• Availability – tens of thousands of users

• Security – protect sensitive location data

• Performance – timely query response

• Accessibility – to enterprise applications

• Manageability – leverage IT resources

• Open – Supports standard interfaces and formats

= Database Strengths

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Use Cases

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GIS Analytical Modeling

& Simulation

Petroleum Exploration

Flood Plain Analysis

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CAD Infrastructure Design (Super Models)

Courtesy Parsons Brinckerhoff

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Google Earth 3D Mash-ups

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Simulation, Gaming, and VR

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Oracle Spatial Overview

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Customer Requirements

• Enterprise-class RDBMS to manage ALL geospatial types

• 2D, 3D, Rasters, Networks, Topology, Attributes

• Native type support, indexing, and analysis

• First commercial vendor to full 3D coordinate systems support

• Standards based: SQL, Java, .NET

• Addresses large volumes of 3D point data

• Building City models (Collada, CityGML)

• Laser scanning (LIDAR, sonar)

• Geo-engineering (CAD)

• Surfaces (TINS, DEMS)

• Addresses range of 3D application domains

• GIS, CAD/CAM

• City Modeling, environmental analysis

• Real Estate, asset management

• Personal navigation

• VR, gaming, simulation

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Spatial Data Types

All Location/Spatial Data Stored in the Database

Spatial Indexing

Fast Access to Spatial Data

Spatial Access Through SQL Spatial Analysis

Spatial Database Capabilities

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Spatial Databases:

Managing all Geodata Types

Data

3D Models (Buildings)

Networks

(Highway network) Parcels (polygons)

Imagery (Satellite)

Structured Networks/Boundaries (persistent topology)

Lidar

(Point Clouds)

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Advanced Spatial Data Management Capabilities

• SQL Spatial Type

• R-tree index

• Spatial Operators

• Spatial Reference System

• EPSG Support

• Geodetic (lat/long) Support

• Linear Referencing

• Spatial Aggregates

• Versioning

• Long Transactions

• Fusion Middleware MapViewer

GeoRaster Type

Network Data Model Linear Referencing Topology Data Model Geocoding Engine Routing Engine

Spatial Data Mining

Text Annotation/Orientation

3D Models and LIDAR Types

Web Feature Server (WFS‐T)

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Mapinfo MapXtreme Bentley

Spatial Databases Addressing GIS Interoperability Problem

Leica

Spatial

Data Warehouse

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Oracle 11g Spatial

3D Capabilities

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3D Functionality in Oracle Spatial 11g

• SDO_GEOMETRY (3D)

• SDO_TIN

• SDO_POINT_CLOUD

3D COORDINA TE SYSTEM S

Types

Building Models,..

Surface Modeling

Scene,

Object Modeling

Efficient

Storage

Query

Analysis

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3D Spatial Data Types

• Simple and composite Solids

• Solids are composed of closed surfaces

• It has to have one outer surface and one or more interior surfaces

• Cube is an example of a simple solid

• A pyramid is another example of a simple solid

• Composite solids: formed by multiple solids

• Always define a single contiguous volume

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Point Clouds: LIDAR

• Large volumes of point data acquired by sensors

• LIDAR (Light Detection and Ranging)

• Seismic sensors

• Millions of points used to model a scene

• New data type introduced to efficiently manage this type of point data

• TIN to create triangulation of such points

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TINs: Triangulated Irregular Networks

• Vector-based topological data model represents terrain/surface

• Contain a network of irregularly spaced triangles

• 3D surface derived from irregularly spaced points

• Each sample point has an x, y, z or surface value

Node No X Y Z

1 5 6 3

2 3 6 5

3 1 5 6

4 4 4 4

5 6 5 3

6 2 2 2

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Query, Analysis of 3D Data

• Given a 3D Geometry as Query, Identify data geometries that

• Intersect the Query (SDO_ANYINTERACT)

• Within specified distance from Query (SDO_WITHIN_DISTANCE)

• Nearest to Query (SDO_NN)

• Analysis Functions

• Relationship: Geometry-Geometry Intersection

• Length, Area, Volume Analysis

• Validation of 3D Geometry: Is Solid ‘closed’ ?

• Extrusion of 2-D Footprint to a 3-D Solid by specifying heights

• Association of Textures with LabelStrings of Geometry Elements

• Extraction of Elements using LabelString of Geometry

• Conversion Functions to/from GML, to KML/Collada, from CityGML

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3D Coordinate System Functions

• Associate a coordinate system with 3D data

• SDO_GEOMETRY

• Support transformations from one to another coordinate system

• Compute distances, and other spatial

relationships between two objects within the same coordinate system

Same use as 2D Coordinate Systems:

A reference system for spatial operations

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3D Coordinate Systems

• Vertical Coordinate Systems: Essentially 1-d coordinate system (w.r.t sea-level etc.)

• Geocentric: 3-d Cartesian

• Geographic-2d, Projected-2d: 2-d Ellipsoidal

• Geographic-3d: 3-d Ellipsoidal

• Compound Coordinate System

¾ Combines

• A Vertical Coordinate System with

• Either A Geographic-2D or A Projected-2D Coordinate System

9 Oracle Spatial 11g supports the following EPSG types

9Oracle Spatial 11g supports transformations between different

3D Coordinate Systems

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Summary

• Support for large seamless 3D scenes: Terabytes of objects

• Provides bridge to fuse 3D, 2D, CAD, raster data

• Fusion of aerial imagery, close-range airborne and ground video/LIDAR with traditional 2D vector models