A Paper On Geospatial Computing
ABSTRACT
This paper insights into the recently developed
technology of digital image processing
i.e geospatial computing. Geospatial computing provides a complete
environment for the visualization, exploration, and analysis of geospatial data
such as vector maps, geo referenced imagery, and terrain data. A significant
portion of this paper explains the concept of GIS (geographic information
systems).
Digital
computing allows the capture and sharing of knowledge across networks such as
the Internet. Simultaneously, geographic information system (GIS) technology is
evolving and provides better methods to understand, represent, manage, and
communicate the many aspects of the earth as a system. A GIS is a unique kind
of database of the world—a geographic database (geo database). It is an
‘information system for geography’. Fundamentally, a GIS is based on a
structured database that describes the world in geographic
Terms.
This
paper also describes the rules for geospatial semantic web applications.
Geographic
information is represented by a series of geographic datasets that model
geography using simple, generic data structures. A GIS includes a set of comprehensive
tools for working with the geographic data. A GIS supports several views for
working with geographic information. the views are :
1.
Geodata base
2.
Geovisualization
3.
Geoprocessing
Geospatial information is crucial
to bridging the knowledge gap. Unfortunately, existing geographic information
systems (GIS) lack the flexibility needed to efficiently address this
challenge. The answer is Seamless Geospatial Computing, which can make sense of
an enormous volume of data, deliver on-demand capabilities, and provide the
agility required to deal with today's event-driven business tactics and
safeguard an organization's long-term survival.
Seamless Geospatial Computing will allow organizations to geospatially enable existing business processes and quickly create new enhanced applications
Seamless Geospatial Computing will allow organizations to geospatially enable existing business processes and quickly create new enhanced applications
The paper thereby with the help
of block diagrams sketches and examples ,has tried to overview the concept of
geospatial computing.s
INTRODUCTION
Image processing is a technique in which an
image is digitized to convert it to a form which can be stored in a computer's
memory or on some form of storage media such as a hard disk or CD-ROM. This
digitization procedure can be done by a scanner, or by a video camera connected
to a frame grabber board in a computer. Once the image has been digitized, it
can be operated upon by various image processing operations.
Image processing operations can be roughly
divided into three major categories, Image Compression, Image Enhancement and
Restoration, and Measurement Extraction. Image Compression involves reducing the amount of memory needed
to store a digital image. Image defects which could be caused by the
digitization process or by faults in the imaging set-up (for example, bad lighting)
can be corrected using Image Enhancement techniques. Once the image is in good
condition, the Measurement Extraction operations can be used to obtain useful
information from the image.
In a world living with the
effects of the Sept/ 11. 2001, attacks, the margin for error is razor thin.
Tolerance for errors, lapses and poor response will continue to decrease as
globalization, supply chain complexity and the overall context of uncertainty
increase. Organizations are walking along a tightrope perched between risk and
investment—standing still isn’t an option .This is why geospatial information
is becoming more important in reducing the gap between how the world is
represented in information systems and the real world. Geospatial information
can close the gap between what we know and what we should know.
Geospatial computing provides a
complete environment for the visualization, exploration, and analysis of
geospatial data such as vector maps, geo referenced imagery, and terrain data.
These capabilities are useful in fields such as aerospace, defense, geophysics,
intelligence, oceanography, and other earth and planetary sciences.
Seamless Geospatial Computing has enormous
volume of data, deliver on-demand capabilities, provide the agility required to
deal with today’s event-driven business tactics and safeguard an organization’s
long-term survival . Seamless Geospatial Computing enables the use of
geospatial information in an service oriented architecture ( SOA ) environment,
including direct connection to industry-standard databases. In this
environment, geospatial data sources, services and visualization work together
seamlessly with other business services to enhance existing and future business
processes.
Geospatial computing
MATLAB and the Mapping Toolbox provide
a complete environment for the visualization, exploration, and analysis of
geospatial data such as vector maps, geo referenced imagery, and terrain data.
These capabilities are useful in fields such as aerospace, defense, geophysics,
intelligence, oceanography, and other earth and planetary sciences.
The Mapping Toolbox provides a
comprehensive set of functions and graphical user interfaces for building map
displays and performing geospatial data analysis in MATLAB. You can create map
displays that combine data from multiple modalities and display them in their
correct spatial relationships. The toolbox supports standard analyses, such as
line-of-sight calculations on terrain data or geographic computations that account
for the curvature of the Earth’s surface.
With MATLAB and the Mapping
Toolbox, you can:
Import data from standard
geospatial formats and specific data products
Organize, extract, combine, and
otherwise manipulate map and geospatial data
Create 2-D and 3-D map displays
with more than 60 map projections
Analyze map and geospatial data
Display and analyze terrain data
With the addition of the Image
Processing Toolbox, you can:
Enhance geospatial images for
analysis or visual interpretation
Perform spatial transformations
Perform image registration using
control points
Detect and measure image features
RULES FOR GEOSPATIAL SEMANTIC WEB
APPLICATIONS
The use of geospatial data is
ubiquitous (present every where or in several places at a time) in many
real-world applications. For example, geospatial data is useful for planning
bus routes for a city, for finding suitable helicopter landing areas for a
military operation, and for analyzing the nuclear weapon production capability
of a hostile country.In a typical geospatial application, the semantics of data
( e g vector data, sensor data, and
satellite images) and the procedures for processing the data (e.g., functions
for coordinate transformation and image processing ) are usually tightly coupled
within the application implementations.
With the emergence of semantic web languages
(i.e., RDF/ RDFS and OWL) geospatial application will be able to exploit ontologies (branch of metaphysics that deals
with the nature of existence) for geospatial knowledge sharing and computation. RDF/RDFS and OWL allow the
semantics of geospatial data to be explicitly defined using ontologies. The
benefit of using rules in a geospatial application is twofold.
First, rules can help to decouple
the low-level implementation for processing geospatial data from the high-level
computational logics that guide the execution of the low-level procedures.
Second, when rules are used in
conjunction with rule engines, they can help to enable different kinds of
logical inference support in addition to OWL/RDF ontology reasoning – e.g.,
default reasoning and fuzzy reasoning.
AN EXAMPLE USE OF RULES
Let’s consider a bus route
planning application. When planning a bus route for a particular city, an
analyst must determine the specific locations to place bus stops. In order to
do so, the analyst must consider various kinds of geospatial data about the
city, e.g., the traffic flow information (the directions in which different
traffic flows), road networks (how roads are interconnected), historic traffic
data (how roads are being used by the commuters), and “ridership” information
(estimates of the number of busriders at different locations).
Let’s assume that all relevant
geospatial data is expressed as ontologies using the OWL language. Because the
bus route planning application understands the shared ontologies, it is able to
acquire and process relevant geospatial data. However, in order to suggest the
specific locations where the bus stops should be placed, rules are required.
For example,
RULE1: IF distanceFrom(?locA,
?locB, ?dist) AND
LessThan(?dist, 100, “meters”)
AND
is TypeOf(?locA,
fea:RoadIntersection) AND
isTypeOf(?locB, fea:ShoppingMall)
THEN
busStopCandidate(?locA)
RULE2: IF busStopCandiate(?locA)
AND
existingBusStop(?locB) AND
distanceFrom(?locA, ?locB, ?dist)
AND
lessThan(?dist, 700, “meters”)
THEN not(busStopCandidate(?locA))
In the above example,
“RULE1” expresses that a road intersection
is a good bus stop candidate if it is less than 100 meters away from a shopping center.
“RULE2” expresses that a location is not a good bus stop candidate if it
is too close (less than
700 meters) to an existing bus stop.
IMPLEMENTATION REQUIREMENTS
In order to integrate rules into
geospatial semantic web applications, we must define a language for expressing
rules and a framework for processing rules. We believe that the implementations
should meet the following requirements:
1. The language should allow the
expression of a predicate term with multiple arguments. For example, its common
for geospatial relations to have more than two arguments (e.g. distance From(Point
A, Point B, 5) or direction From(Feature X, Feature Y, “north”)).
2. The framework for processing
rules should support procedural attachments for built-ins. In geospatial
applications, functions for computing various geometric attributes of a feature
(e.g., a road or a building) are of great importance. Geospatial functions such
as “locatedIn”, “distanceFrom”, “overlaps”, and “contains” can only computed
when the topology, geometry, and CRS
(Coordinate Reference System) of the features are known. For example, the
distance
between two roads can only be
calculated when the system knows the specific CRS that is used in describing
the geometric coordinates of the roads.
3. The framework should support
the serialization of rules. For example, if the underlying system uses Prolog
to perform rule-based inference, the framework should allow applications to
serialize rules from the Prolog native representation to the supported semantic
web rule languages (e.g., SWRL), and vice versa. This requirement is essential
because external rule-based inferences are usually defined in the native
representation of the associated rule engines, as opposed to RDF.
4. The framework should provide a
mechanism for detecting and resolving rule conflicts. In the previous example,
it is possible that the properties of a
particular location satisfies both “RULE1” and “RULE2” – i.e.
bus Stop Candidate (some Loc A)
and not(bus Stop Candidate (some Loc A)).
5. The framework should define
suitable interfaces for editing and managing rules. There are differences
between editing ontologies and editing rules. For example, rules may be
expressed using ontology languages such as OWL. However ,the classic
class-subclass UI view is inadequate for rendering rule structures and
displaying the relations between different rule statements to a user.
GIS
Geographic
Information system is a computer system for capturing,storing,quering,analyzing
and displaying geographically referenced data.
Five
elements of geographic knowledge
GIS
provides a comparatively new mechanism for capturing geographic knowledge. A
GIS is a system for management, analysis, and display of geographic knowledge,
which is represented using a series of information sets. These information sets
include:
Maps
and globes
Interactive
views of geographic data with which to answer questions, present results, and
use as a dashboard for real work. Maps and globes provide the
advanced GIS applications for interacting with geographic data.
Geographic
datasets
File
bases and databases of geographic information—features, networks, topologies,
terrains, surveys, and attributes.
Data
models
GIS
datasets are more than database management system (DBMS) tables or individual
data files. They incorporate advanced behavior and integrity rules. The schema,
behaviour, and integrity rules defined in data models play a critical role in
GIS.
Processing
and work flow models
Collections
of geoprocessing procedures for analysis and automating and
repeating multiple tasks.
Metadata
Documents describing the
other elements. A metadata catalog enables users to organize, discover, and gain access to shared
geographic knowledge.
These
five elements, along with comprehensive GIS software logic, form the building
blocks for assembling intelligent geographic information systems. Intelligent
GIS makes it possible for us to digitally encapsulate and to share geographic
knowledge. These elements provide a foundation for addressing many challenges
using GIS—for example, improvements in efficiency, intelligent and
informed
decision making, science-based planning, resource accounting, evaluation, and
communication.
Intelligent
GIS will enable us to capture and share geographic knowledge in many forms—as
advanced GIS datasets, maps, data models, the expertise of professionals who
have developed standardized workflows, and advanced models of geographic
processes. Intelligent GIS will also enable the building and management of
knowledge repositories that can be published and shared
for
others to use. GIS must be engineered to enable the creation, use, management,
and sharing of all five elements of geographic information.
THE THREE VIEWS
OF A GIS
Geographic information is
represented by a series of geographic datasets that model geography using
simple, generic data structures. A GIS includes a set of comprehensive tools
for working with the geographic data.
A GIS supports several views for
working with geographic information:
1. The Geodatabase view: A GIS is
a spatial database containing datasets that represent geographic information in
terms of a generic GIS data model— features, rasters, topologies, networks, and
so forth.
2. The Geovisualization view: A
GIS is a set of intelligent maps and other views that show features and feature
relationships on the earth’s surface. Various map views of the underlying
geographic information can be constructed and used as ‘windows into the database’
to support queries, analysis, and editing of the information.
3. The Geoprocessing view: A GIS
is a set of information transformation tools that derive new geographic datasets
from existing datasets. These geoprocessing functions take information from
existing datasets, apply analytic functions, and write results into new derived
datasets.
THE
GEODATABASE VIEW
A GIS
is a unique kind of database of the world—a geographic database (geo database).
It is an ‘information system for geography’.
Fundamentally, a GIS is based on a structured database that describes the world
in geographic terms.
.
Geographic
representations
As
part of a GIS geodatabase design, users specify how certain features will be
represented. For example, parcels will
typically be represented as polygons, streets will be mapped as centerlines,
wells as points, and so on. These feature representations are organized into
datasets, such as feature classes, raster datasets, and tables. Each GIS
dataset provides a geographic representation of some aspect of the world,
including:
•Ordered
collections of vector-based features (sets of points, lines, and polygons).
•
Raster datasets such as digital elevation models and imagery.
•
Networks
•
Terrains and other surfaces
• Survey
measurements
Spatial
relationships: topology and networks
Spatial
relationships, such as topologies and networks, are also crucial parts of a GIS
database. Topology is employed to manage common boundaries between features,
define and enforce data integrity rules, and support topological queries and
navigation—for example, to determine feature adjacency and connectivity.
Topology is also used to support sophisticated editing and to construct
features from unstructured geometry—for example, to construct polygons from
lines.
Networks
describe a connected graph of GIS objects that can be traversed. This is
important for modeling pathways and navigation for transportation, pipelines,
utilities, hydrology, and many other network based applications.
Thematic
layers and datasets
In a
GIS, homogeneous collections of geographic objects are organized into layers,
such as parcels, wells, buildings, orthophoto imagery, and raster-based digital
elevation models (DEMs). Each layer is georeferenced to specific locations.
Precisely defined geographic datasets are critical for useful geographic
information systems, and the layer based concept of thematic collections of
information is a critical GIS dataset concept.
Datasets
can represent:
• Raw
measurements such as satellite imagery.
•
Compiled and interpreted information.
•
Data that is derived through geoprocessing operations for analysis and modeling.
Many
of the spatial relationships between layers can be easily derived through their
common geographic location. GIS manages simple data layers as generic GIS
object classes and utilizes a rich collection of tools to work with the data
layers to derive many key relationships.
A GIS
will use numerous datasets with many representations, often from many
organizations. Therefore, it is important for GIS datasets to be:
•
Simple to use and easy to understand
•
Used easily with other geographic datasets
•
Effectively compiled and validated
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