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Environmental Systems Research Institute, Inc. (909) 793-2853380 New York Street, Redlands, CA 92373 Fax (909) 793-5953

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Scanning Data EntrySolutions forARC/INFO GIS

An ESRI White Paper

Contents Page

Executive Summary 1

Evaluating Scanning Data Entry 7

ESRI Scanning Data Entry SolutionsA GIS Focus 31

Glossary 45

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Copyright 1995 Environmental Systems Research Institute, Inc.All rights reserved.Printed in the United States of America.

The information contained in this document is the exclusive property of Environmental Systems ResearchInstitute, Inc. This work is protected under United States copyright law and other international copyrighttreaties and conventions. ESRI grants the recipient of the ESRI information contained herein the right to

freely reproduce, redistribute, rebroadcast, and/or retransmit this information for personal, noncommercialpurposes including, teaching, classroom use, scholarship, and/or research, subject to the fair use rightsenumerated in Section 107 and 108 of the Copyright Act (Title 17 of the United States Code). No part of thiswork may be reproduced or transmitted for commercial purposes in any form or by any means, electronic ormechanical, including photocopying and recording, or by any information storage or retrieval system, exceptas expressly permitted in writing by Environmental Systems Research Institute, Inc. All requests should besent to Environmental Systems Research Institute, Inc., 380 New York Street, Redlands, CA 92373 USA,Attention: Contracts Manager.

The information contained in this document is subject to change without notice.

RESTRICTED RIGHTS LEGENDUse, duplication, and disclosure by the government are subject to restrictions as set forth in FAR 52.227-14Alternate III (g)(3) (JUN 1987), FAR 52.227-19 (JUN 1987), or DFARS 252.227-7013 (c)(1)(ii) (OCT

1988), as applicable. Contractor/Manufacturer is Environmental Systems Research Institute, Inc., 380 NewYork Street, Redlands, CA 92373 USA.

ESRI, ARC/INFO, PC ARC/INFO, ArcView, and ArcCAD are registered trademarks; ARC COGO, ARCNETWORK, ARC TIN, ARC GRID, ARC/INFO LIBRARIAN, ARCPLOT, ARCEDIT, TABLES,Application Development Framework (ADF), ARC Macro Language (AML), Avenue, FormEdit, ArcSdl,ArcBrowser, ArcDoc, ARCLine, ARCSHELL, IMAGE INTEGRATOR, DATABASE INTEGRATOR, DBIKit, WorkStation ARC/INFO, ArcTools, ArcStorm, ArcScan, ArcExpress, ArcPress, Mapplets, SPATIALDATABASE ENGINE (SDE), PC ARCEDIT, PC ARCPLOT, PC DATA CONVERSION, PC NETWORK,PC OVERLAY, PC STARTER KIT, PC ARCSHELL, Simple Macro Language (SML), ArcUSA, ArcWorld,ArcScene, ArcCensus, ArcCity, the ESRI corporate logo, the ESRI globe logo, the ARC/INFO logo, the ARCCOGO logo, the ARC NETWORK logo, the ARC TIN logo, the ARC GRID logo, the ARCPLOT logo, theARCEDIT logo, the Avenue logo, the ArcTools logo, the ArcStorm logo, the ArcScan logo, the ArcExpresslogo, the PC ARC/INFO logo, the ArcView logo, the ArcCAD logo, the ArcData logo, ARCware,ARC News,ArcSchool, ESRITeam GIS, ESRIThe GIS People, GIS by ESRI, ARC/INFOThe World's GIS,Geographic User Interface (GUI), Geographic User System (GUS), Your Personal Geographic InformationSystem, and Geographic Table of Contents (GTC) are trademarks; and the ArcData Publishing Program,ARCMAIL, ArcQuest, ArcWeb, and Rent-a-Tech are service marks of Environmental Systems ResearchInstitute, Inc.

The names of other companies and products herein are trademarks or registered trademarks of their respectivetrademark owners.

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Air Photos Maps

Overview of Scanning Data Entry Pathways

Scanner

RasterDatabase

ARC/INFOCoverageDatabase

Vectorize Raster-to-vector

conversion

Vectorize Heads-up

digitizing

Database merge with Existing vector data COGO data CAD data

Georeference Raster edit Tiling Raster-to-raster

Conversion

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Costs and Benefitsof ScannedData Entry

Cost analysis of GIS projects shows that database automation often

accounts for more than 75 percent of the total project expense.

Scanning data entry is a viable cost-reduction alternative for this most

expensive GIS componentdata automation.

The cost of scanning data entry has decreased dramatically since 1990.

Even agencies with limited budgets and relatively small data automa-

tion projects are discovering that a scanning data entry strategy makes

good economic sense. Database automation strategies based on

scanning are cost-competitive with, and in some cases can be

significantly less expensive than, other methods.

Scanning technology is no longer the data capture solution of the

distant future, but is quickly becoming the preferred method of data

capture for GIS. Data automation methods within ESRI's Database

Development Group have shifted dramatically in recent years to

scanning data entry as the preferred solution.

Is Scanning DataEntry Appropriatefor Your Project?

Scanning data entry provides many advantages to GIS users.

Scanning is generally faster and a great deal more accurate than table

digitizing. Scanned data that are subsequently vectorized have more

consistent coordinate placement than data entered through manual

digitizing. Scanning data entry methods can be easily learned andused by existing staff. Scanning data entry can be used to generate

application-specific data that are not commercially available. Recent

technical advances in hardware and software have tailored scanning

data entry capabilities specifically for GIS requirements.

GIS users should carefully evaluate scanning data entry in the context

of project requirements, which can vary greatly. Factors to consider

include data sources and their availability, map quality, update

frequency, data volume, accuracy requirements, and system capacity.

As you consider various data automation options, the specific require-ments of your project will guide your analysis of the alternatives. For

example, if your GIS application uses street centerline data with

address ranges, you may find standard "off-the-shelf" data from a

commercial data vendor a good solution. But commercial data may

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not exist for all of your organization's needs. For example, a data

layer such as property boundaries (i.e., land parcels) may not be

commercially available.

When you prefer to do data automation in-house, scanning data entry

takes no more time than table digitizing and offers better coordinate

accuracy and consistency without the random errors often associated

with manual methods. If you have an existing digital database and

need to perform only a low volume of intermittent updates, table

digitizing these updates may be appropriate. Even so, incremental

updates from scanned documents are a feasible alternative.

The available data sources will also influence the feasibility of

scanning data entry. The scanning data entry alternative is most

appropriate when the data do not already exist in digital form but do

exist in document form. Scanning data entry does require a source

document of some kind. If these documents are of poor quality,

scanning data entry can be effective but will require more operator data

cleanup. Scanning data entry is most useful and cost-effective when a

high-quality data source (e.g., maps or air photos) is available.

Feature layers on separate documents reduce processing requirements.

Some applications may be able to use raster data obtained without

document scanningfor example, satellite or airborne scanner dataprovided on digital media. Scanning data entry technology offers

software tools that take advantage of commercially available raster

data.

When planning scanning data entry projects, it is very important to

spend time assessing your needs before implementing a solution.

Accuracy requirements and the characteristics of your source

documents will determine the most appropriate hardware and software

package. For example, the need for raster integration may require

additional disk storage or a more powerful CPU. If you work mainly

with air photos and do not have stringent accuracy constraints, youshould consider heads-up digitizing as a scanning data entry option.

If you have good digital geodetic control and a complete series of

plats, raster-to-vector conversion using that control can be an effective

strategy. Used for appropriate applications and implemented

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correctly, scanning can be the most cost-effective and efficient method

for capturing your data.

ESRI Solutionsfor Scanning

Data Entry

ESRI has used scanning data entry technology for many years. The

ESRI Database Automation Group has adopted scanning data entry as

a preferred methodology. ESRIsoftware has supported raster data

sets for many years, and a new ESRI software product called

ArcScanfocuses specifically on providing scanning data entry

software tools. ArcScan is closely integrated with the rest of

ARC/INFO in a single software environment. Thus, all the

functionality of ARC/INFO can be combined with specialized

software for scanning data entry. ESRI can provide turnkey scanning

data entry systems through reseller agreements with industry-leading

hardware vendors. Many other companies have joined ESRI's open

systems approach and offer complementary capabilities that work with

the ARC/INFOdata structures and user interface. ESRI scanning

data entry solutions are affordable, easy to use, and are integrated with

ESRI's advanced GIS data management technology. ESRI scanning

data entry solutions provide a clear and effective alternative for data

automation.

About ThisWhite Paper Scanning data entry technology offers a variety of tools formanipulating raster data and for converting raster data to vector data.A thorough evaluation of project needs and available data sources will

determine the tool or tools that work best for you. The next section

presents information to help you evaluate scanning data entry. ESRI's

rich toolset for scanning data entry and integrated data editing/data

management technology is described in the last section. A glossary

provides definitions for many of the specialized terms used in

scanning data entry.

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Evaluating ScanningData Entry

This section is designed to help you evaluate scanning dataentry. Careful evaluation is a key factor in ensuring your

success. If you take the time to think it through and

evaluate the options and trade-offs carefully, your projectwill benefit greatly. Evaluation considerations should

include the data available, the hardware and software, and

the methods or procedures.

EvaluatingData Sourcesfor Scanning

Data Entry

Understanding data sources is crucial to understanding how scanning

data entry can be used in your GIS. Scanning solutions are as diverse

as the data used in the GIS applications they support, because

scanning and vectorization requirements are determined by the data.

Two main categories of data are used with scanned data entry projects.

The first is paper or Mylar maps, containing line art, that are scanned

into bi-tonal (black-and-white) raster data sets, or images. Scanned

maps in raster format are usually converted to vectors using raster-to-

vector conversion programs. The second category of scanned

documents in wide use is aerial photographs, typically black and

white, that are scanned into a grayscale image. Scanned photos are

not conducive to automated raster-to-vector conversion techniques and

are often used in raster format. Scanned photos are often used in

vector conversions as visual background for heads-up digitizing.

While both types of documents lend themselves to scanning dataentry, each has different processing requirements. Scanned data entry

can build a GIS database using any or all of the following data

sources:

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Line Work Maps Low-quality 36 x 44 (E format) maps. Ranging fromantique linen maps, to CAD plotter output, to blue lines, to as-

builts, the overwhelming majority of hard-copy maps are low

quality. Quality, in the scanning sense, refers to the quality of the

media itself and the problems the media presents to the scanning

and vectorization process, not to the informational quality of the

data on the document. This is the most common data source

category for scanned data entry applications.

High-quality E format maps. Typically, these are Mylar

maps with multiple data layers. They are more frequently found in

larger, rather than smaller, organizations and public agencies.

Media quality is highfor example, the lines on the media are

clear and crisp and the media will not have extraneous marks or

"noise." This type of data can have clutter, in the form of

annotation or unwanted data layers, which can complicate the

vectorization process.

High-quality, single-layer E format maps. Mylar

separates, such as topographic contours, are often available in this

form. Soil maps, separates from a production map series, and

specially prepared Mylars can fall in this category. Cadastral maps

on Mylar fit in this category if they have only one data layer (i.e.,only parcels). This type of bi-tonal, line art map data source is the

most amenable to scanning data entry because its high quality

requires the least pre- or post-processing. Hallmarks of high-

quality documents for scanning are a single data layer, absence of

clutter, presence of registration tics (georeference marks), and

clean, high-contrast media.

11 x 17 (B format) plat maps. Plat maps in this format are

found throughout the United States. Map quality will vary.

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Line work can contain gaps. Inthis case, the gaps are caused by the

line symbology used to representintermittent streams. Gaps canalso be caused by low-quality data,resulting in noisy scanned output.

The ArcScan tracing tool can"jump" gaps to create continuous

vector output. Gap jumpingparameters can be modified to suit

data requirements.

Photographs andDigital Imagery

9 x 9 aerial photos. This source data category is widely used

with scanning data entry. Aerial photos are most often black and

white, with some in color when budget allows. The most common

use of scanned photography is to serve as a visual backdrop or

"reality check" to other data. Heads-up digitizing uses scannedphotographic images to guide operator coordinate capture by

"tracing" features from a display screen. Air photos are often the

most up-to-date data source. You can usually scan air photos at a

much lower resolution than line art. This can help compensate for

the greater storage requirements of grayscale images. Visual

display has less stringent resolution requirements than raster-to-

vector conversion.

Much photography being scanned today is uncorrected.

Uncorrected photography, while relatively inexpensive and useful in

many applications, should be used with care. Even though vectordata derived from uncorrected photography will overlay properly on

its source image, it is only accurate relative to its source data.

However, vector data converted from uncorrected photo images are

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likely to misregister with data from other sources. In addition,

uncorrected photographic images may not merge well with adjacent

images, and measurements made on uncorrected images will be

incorrect.

Large format photography. Large format photos (e.g.,

24 x 30 or 30 x 30) are available in a variety of scales. 1:24,000

orthophoto quads, usually black and white, have been produced

for large areas (e.g., statewide coverage). Orthocorrection can be

applied photographically or digitally. That is, orthophotos can be

scanned directly and used without need of coordinate correction,

or uncorrected stereophotos can be scanned and then

orthocorrected in digital form. The output of digital orthophotos

can be at any scale appropriate for the resolution of the image.

Other image data from airborne scanners or satellites.

LANDSAT and SPOT images are commercially available (e.g.,

through the ArcDataSMprogram) and typically offer ten- to thirty-

foot resolution. Airborne scanners linked with GPS receivers can

produce images with much greater resolution (pixel resolution of

less than one foot, for example).

Digital DataConcepts The raster data format is a cellular data format well suited for storingimages or maps. A raster data set is like a carpet of cells overlayingthe map where each cell has a value representing the corresponding

value beneath it in the map. For example, a raster data set of a

scanned map will have pixel values that correspond to the brightness

of the light reflected from the map.

This figure illustrates how apolygon, line, and point feature

would be stored in an x,ycoordinate system (vector) and a

row, column system (raster).

Polygon

Point

Line

x-axis

y-axis columns

rows

Vector Raster

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A raster data set can be bi-tonal, grayscale, or color (often satellite

images are displayed as false color images). Raster data can bedirectly useful in a GIS. For example, a scanned air photo can be

used as a backdrop to other infrastructure data, such as roads or

sewers, using ARC/INFO IMAGE INTEGRATORcapabilities.

The ARC/INFO GRIDextension uses the raster data format for

complex spatial analysis. Raster data sets tend to be largein the

multi-megabyte rangeand have special data processing needs.

Header record

Raster data file

Pixel data

.

.

.end of file

Raster data can be organized in a number of ways depending upon the

particular raster format. Typically, the raster data file contains a

header record that stores information about the data such as the

number of rows and columns, the number of bits per pixel, the colorrequirements, and the georeferencing information. Following the

raster header is the actual pixel data for the image. The internal

organization of the raster data is dependent upon the raster format.

Some formats contain only a single band of data, while others contain

multiple bands.

When planning your scanning data entry project, you should give

special attention to input resolution. Input resolution is the number of

pixels per inch in both x and y dimensions of the digital snapshot.

Most scanners allow some control over input resolution. In general,

you should try to reduce data storage requirements by choosing thelowest resolution that will cleanly capture your data. Some

experimentation will be required as you "fine tune" your scanning data

entry methods.

The input resolutions shown in Table 1 are typical, but not absolute

resolutions. Resolution is expressed in dots per inch (dpi). Doubling

resolution (e.g., from 400 dpi to 800 dpi) can have the effect of

quadrupling data set size (compressed formats will show less increase

in size). Scanning data at a resolution greater than that required by the

source document will only increase data storage requirements with no

appreciable improvement in data quality. Unneeded input resolutioncan even create processing problems by exaggerating errors in poor-

quality data (e.g., additional white noise).

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TABLE 1Raster Data Sources

Type ofSource Data

T y p i c a lDataExample

Typical Usein ScanningData Entry

T y p i c a lScanner (Input)R e s o l u t i o n

Raster DataFormat

Typical RasterData Size

Low quality36 x 44 inch(E format) map

As-builts,blue lines

Raster-to-vectorconversion usinginteractive techniquessuch as raster cleanupand line following

400 dpi RLC or GRID bi-tonal (compressed)

6 megabytes

High quality Eformat map

Contours onMylarseparates

Raster-to-vectorconversion usinginteractive (multipledata layers or clutter) or

batch (single data layer)

400 to 800 dpi(depending on datatype and quality)

RLC or GRID bi-tonal (compressed)

6 to 30megabytes

11 x 17 inch(B format) map

Plat map Raster-to-vectorconversion

400 to 500 dpiresolution variesby source dataquality

RLC or GRID bi-tonal (compressed)

1 to 4 megabytes

9 x 9 inch aerialphoto (black andwhite)

Standard airphoto

Visual backdrop forother data entrymethods such asCOGO or heads-updigitizing. Ortho-photo production

200 dpi TIFF(uncompressed)

4 megabytes

9 x 9 inch aerial

photo (color)

Standard air

photo (color)

Visual backdrop for

other data entrymethods such asCOGO or heads-updigitizing. Orthophotoproduction

200 dpi TIFF

(uncompressed)

4 megabytes

(8 bit color),12 megabytes(24 bit color)

Large formataerial photo(30 x 30 inch)

Orthophoto Visual backdrop forother data entrymethods such asCOGO or heads-updigitizing. Ortho-photo production

200 dpi TIFF(uncompressed)

36 megabytes

Three band satelliteimage

EOSAT orSPOT Imagedata

Visual backdrop forheads-up digitizing ofmajor roads

Purchased inraster format

Band interleaved,etc.(uncompressed)

Varies with typeof data and area ofcoverage, 2 to600 megabytes

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Raster data can be compressed. That is, you can use a data storage

scheme to reduce the amount of disk space required to store the data.

Bi-tonal raster data can be compressed to a greater degree than

grayscale or color data because the cell values of bi-tonal data can be

represented with a single biteither black or white, on or off, data or

no data. Grayscale and color raster data can also be compressed, but

with lesser compression ratios and at a higher processing cost to

support decompression. When raster data are compressed for storage,

it must be decompressed for display and other operations.

Vector data are in a format that represents map features with the x,y

coordinates of the features. Where a raster data set would represent a

feature by tagging all the cells that overlay the feature, a vector data set

would represent the feature by listing the coordinates of points along

it. Many GIS applications, such as parcel maintenance, demographic

analysis, or vehicle routing, require data in a vector format.

Raster data are unsuitable for these applications because, although the

raster and vector data may look the same displayed on a screen, raster

data have very different characteristics. Scanned raster data are simply

a "digital snapshot" of the source documenta scanned map has

pictorial information and limited connectivity to other data. ARC/INFO

georelational vector data, on the other hand, maintains the internal

spatial relationships of the features it represents and has far moreinformation than a simple picture. In addition, georelational vector data

have strong connections to other related data, such as tables stored in a

relational database management system (RDBMS).

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The Georelational Model

Raster data can also be used to yield a vector representation of the

same map datathis process is called raster-to-vector conversion and

is a main topic of this white paper. Only recently, however, has

raster-to-vector conversion technology become affordable, reliable,

and widely available. These advances in both hardware and software

have made scanning data entry a feasible alternative.

Evaluating

Computer Hardwarefor ScanningData Entry

Scanning data entry has special hardware requirements. These

hardware capabilities support raster data characteristics such as large

data set size and cellular format. A scanning data entry system can

include several hardware components:

The Scanner Scanners to input hard copy paper maps or photos. Scanners take a"digital snapshot" of the source material and store this raster data on

disk. Advanced scanners offer on-screen graphical user interface

(GUI) control software to enhance ease of use. Scanners are available

at a variety of output resolutions, support a variety of media sizes, and

can output black-and-white, grayscale, and color images. Scanners

can output scanned images directly to work-station secondary storage

via a high bandwidth interface (e.g., SCSI). Scanners output data instandard raster data formats such as RLC (for bi-tonal data) or TIFF

(for grayscale data).

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A scanner is a device with a mechanical document feed that is set

above a row of cameras. The document feed can either be continuous,

as in a drum scanner, or direct feed, where the document feeds in the

front and comes out the back of the device. A light source and a glass

window are between the cameras and the document. The light source

is angled in such a way as to shine through the window and reflect off

the document. There is usually a white background behind the

document in the event that the media is transparent. The reflected light

enters the cameras that focus the image onto a charge-coupled device

(CCD). The CCD is a ceramic board with an imbedded array that

translates the presence or absence of light into digital form. The

output from the CCD is a value (usually from 0 to 255) that indicates a

level of gray. Usually, a grayscale value of zero indicates total

absence of light, while a value of 255 indicates complete light

saturation.

Scanners sense reflected light

values and store the reflectance

values as a digital image.

Scanner Basics

Media feedMechanism

Map orPhoto

CCD

Camera

GlassWindow

Digital Image Output

LightSource

White Background

Media

MovementLightSource

Scanner resolution is important. Optical resolution is the ability of

cameras in the scanner to discern data. As a rule of thumb, the optical

resolution of a scanner is expressed in this formula: optical resolution

in dpi = (number of cameras + 1) * 100. Thus, a scanner with three

cameras can offer an optical resolution of 400 dpi. Scanners can also

offer interpolated resolution, in which the data from the CCD are

resampled into smaller pixels. Thus, a scanner with optical resolution

of 400 dpi can also offer inter-polated resolution of 800 dpi. Thismethod can produce an output image with higher resolution, but not

necessarily with greater accuracy. In general, you should evaluate

scanners for GIS applications using optical resolution because GIS

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data are too complicated to be adequately captured with interpolated

resolution data.

Positional accuracy is also an important consideration in GIS

applications that require accurate spatial data placement. Positional

inaccuracy can result from media slippage in the scanner document

feed mechanism or from miscalibrated cameras.

Decide on the minimum resolution and accuracy required by your

application. Then find the least expensive scanner that will meet those

needs. A less expensive scanner that provides 200 dpi optical

resolution is adequate for scanning photographs used as visual

backdrops. On the other hand, if your application must scan closely

drawn "tight" contours, you will need a scanner capable of at least 800

dpi optical resolution. And, as when buying any piece of equipment,

you should consider reliability, repair costs, maintenance agreements,

user support, and so on.

The Computer High-powered computer workstations capable of displaying andmanipulating raster data. Raster data require a bitmapped graphics

monitor for display. Even with bi-tonal data, a color monitor is useful

to support color overlay of vector data. If you are working with

grayscale data, you need at least an 8-bit color display in order to

support a color space that includes both the grayscale image and therest of the graphical user interface.

The workstation needs to have the CPU power required to manipulate

and display large amounts of data rapidlythis consideration should

not be underestimated, as slow response in handling large data sets

can adversely affect project productivity. One workstation can be

dedicated as a scanner and/or plotter server if warranted by high

usage, or the workstation can perform other functions.

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A Plotter Plotters capable of creating hard-copy output of raster data. Plottertechnologies that support output of raster data include electrostatic, ink

jet, and thermal transfer. Plotters can be color or black and white

(black-and-white plotters usually support grayscale output). Pen

plotters are not appropriate for raster data output. For optimum utility,

plotter software should be capable of combining raster and vector

data.

Data Storage Secondary storage devices, such as high-speed magnetic disk drives,or high-volume optical disk drives capable of storing large raster data

files. If you envision an on-line raster database you should be careful

to assess data storage needs carefully. To estimate your raster data

storage requirements, multiply the number of documents you need tohave available on your system at any one time by the raster data set

size for that document type in Table 1. Many applications need to

have only a limited amount of raster data on-line; other applications

want to develop a library of raster data for ad hoc access. Estimation

of your total storage requirements should include the storage needs of

system software, application software, and other types of data (e.g.,

vector data and editing copies of raster data).

A variety of technologies are available to ease the data storage burden.

First, tape archival can be used to simply take unneeded data off the

system. Some sites use a two-tiered approach to data storage in whichmore frequently accessed raster data are kept on high-speed magnetic

disks, and less frequently used data are migrated to optical media.

Optical systems can be purchased with software to perform data

migration automatically at off-peak times. Optical systems and

magnetic systems should be transparently usable as mountable file

systems, usually accessible through an open network access standard

such as a Network File System (NFS).

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Networks High-speed local area networks (LANs) capable of transferring largeamounts of data at high rates of data throughput. LAN configurations

for distributed processing support scanning data entry by connecting

specialized computing machinery on a high-speed data pathway.

Today's LAN configurations can isolate high bandwidth raster data

traffic from other functions by creating subnets using network

bridges. LAN-based systems are modular and scalable.

Evaluating Softwarefor Scanning

Data Entry

Scanning data entry has special software requirements. Software

tools are used for managing, manipulating, and displaying raster data,

converting raster data to vector data and providing a graphical user

interface to make scanning data entry easy to do. Some of these toolsoperate in batch mode or "behind the scenes" with little user inter-

action. Their benefit is increased savings of people's time through

increased automation. Others require a higher level of user

interactiontheir benefit is the intelligent combination of machine and

human capabilities to attain higher productivity. Software functions

needed to support scanned data entry projects include the following:

Raster data management. The data produced by scanning

data entry must be organized in an orderly way. Georeferenced

data should be organized geographically. Data management

software can optimize storage and retrieval of raster data evenwhen data volumes are large. For integration with other systems

and scanners, the software should provide raster-to-raster data

conversion. Raster data management software can extract, edit,

and merge a raster data set from a raster database.

Software data compression. Software data compression can

minimize raster data storage requirements. A variety of industry-

standard data compression formats are available. Industry-

standard data compression formats include RLC and CCITT

Group 3 and Group 4. The CCITT compression standards are

implemented in the TIFF raster data standard. Eight to ten timesdata size reduction can be achieved with bi-tonal data. The amount

of actual data reduction will depend on the compression algorithm

used and the complexity of the data. Typically, denser more

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1

2

3

4

5

Example Scanning Data Entry Configurations

Simpler configuration:

UNIXWorkstation

MagneticDisk

SCSISCSI

More complex configuration:

Scanner

Scanner

Raster Plotter

Loc al Area Network

MultipurposeUNIX Workstation

Optical StorageDevice "Jukebox"

1

2

3

4

5

DedicatedUNIX Workstation

1

2

3

4

5

UNIX Server

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ArcScan georeferencingmenu supports

multiwindow visualinteraction.

Raster data display. Raster data can be displayed with full

control over display symbology and graphic overlay of vector

data. Background values in bi-tonal raster data can be displayed

transparently, thus allowing concurrent display of multiple raster

data sets. Software can alter the display characteristics of

grayscale and color images to suit the needs of the application.

The software provides direct output of raster data to raster-capable

plotters, thus enhancing output speed and reducing hard-copy

processing requirements. The software can merge raster and

vector data in the same hard-copy plot.

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ArcScan raster editing menu. Raster data editing. The software provides capability to clean

up raster data with tools that work directly on the raster data

format. Raster editing is a common pre-processing step to raster-

to-vector conversion. For example, raster editing software can

remove speckling from raster dataand cleaner raster data are

converted to vector data with less post-processing.

Raster-to-vector data conversion. Software can provide an

array of tools for converting raster data to vector data. The tools

offer the flexibility to adapt to a wide variety of raster data. For

high-quality media, batch vector conversion software may be a

good choice. When source documents are lower quality or have

much clutter, interactive line-following software is often

preferable. When photos are scanned, heads-up digitizing can be

appropriate. Maps that are scanned to capture coordinate

information often have a wealth of feature attribute information as

well. This attribute data can be interactively captured during

scanning data entry procedures if the scanning software tools are

well integrated with other editing functions.

The reason that raster-to-vector conversion requires special

algorithms to overcome problems posed by noise and clutter is that

the raster data format is simply a pictorial representation of the

data. That is, when the conversion software examines the raster

data, it can see only pixels of black and whiteit cannot see whatthe raster "picture" is supposed to represent. Thus, conversion

software will attempt to make vector lines out of all data it

encounters, even if the "lines" are really the pen strokes that make

up annotation. Conversion software deals with this kind of

problem in many different ways, but human intervention is often

necessary.

GUI interface and software integration. Ease-of-use is

greatly enhanced through a Graphical User Interface (GUI) that

provides point-and-click control of software functionality. For

highest efficiency, the conversion software should be integratedwith other raster and vector editing functions in a common

software environment. By integrating scanning data entry

technology in a common software environment, more software

functions are available and access to more types of data is possible

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at each processing step. It is easier to learn an integrated software

system that has a consistent and attractive "look and feel."

Evaluating ScanningData Entry

Methodologies

Scanning data entry methods vary as widely as the applications in

which they are used. Some of these methods are unique to scanning

data entry; others take advantage of data and software integration to

bring additional functionality scanning data entry projects. Noise

removal and clutter removal are pre-processing methods. Pre-

processing prepares the raster data for the raster-to-vector conversion

step.

Noise removal. Scanned maps will have a certain amount of

noise. The lower the map quality, the higher the noise content.

Noise is data that do not have informational content. For example,

a common type of noise is tiny spots called speckles that are an

artifact of the scanning process. The speckles are unwanted in the

final vector output. Various methods can be used to remove noise

from the image so that vectorization can proceed. These methods

are collectively called noise removal.

Clutter removal. Even a high-quality map may have unwanted

data on it, such as annotation. Often maps show more than one

data type or layer. For example, a map might show parcelboundaries, easements, and street names. When only the parcels

are to be vectorized, the easements and street names are clutter.

Scanning data entry has developed methods for clutter removal,

such as raster editing tools that "white out" indicated areas.

Software filters that remove data below a given threshold size can

be useful for removing the relatively small lines that make up

lettering (annotation).

Post-processing. Once the raster-to-vector conversion has

been performed, the output vector data can be post-processed.Post-processing is usually accomplished with vector editing

software to correct output vector data. The more closely the post-

processing software is integrated with the vectorization software,

the more efficient the entire vectorization process becomes.

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uncluttered data. Batch vectorization will require post-processing.

Since batch vectorization proceeds without operator intervention

and vectorizes all data input, low-quality, cluttered data can reduce

its efficiency by requiring high levels of post processing.

Typically, the user tests a variety of vectorization parameters to

find the combination of parameters best suited to the data, and then

initiates batch vectorization using those parameters. The major

advantage of batch vectorization is that once parameters are set,

vectorization can proceed unattended and produce an output vector

data set in much less time than other methods. Since all the maps

of a single map series tend to be alike and can use the same

parameters, batch vectorization is well suited to projects that scan a

large number of similar maps.

Both interactive vectorizers and batch vectorizers can deal with a

variety of cartographic problems such as dotted line symbolism.

The choice of which vectorizer to use is mostly data dependent.

Having both methods available broadens the array of available

tools. Vectorizers of both kinds work only with bi-tonal raster

datathey cannot presently be used with grayscale or color raster

data.

Feature attribute capture. Line followers and batchvectorizers both output lines in vector format. These methods are

good for automating the line symbology on maps. Maps can also

contain other data such as point symbols and annotation. The

symbols and annotation are often attribute data associated with a

point, line, or area feature on the map and may be of value to the

GIS database. The informational content of the point symbology

and annotation can be captured by methods that combine heads-up

digitizing with data editing capabilities. Advanced scanning data

entry software will allow the user to point at symbology or

annotation and attach its information to a feature. In a single

integrated software environment, the user can take advantage ofimaging capabilities, vector editing capabilities, and forms entry

capabilities, all within an application tailored to capturing data

from a specific map series. Operator intervention and some key

entry is required, but this method can be an efficient way to

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capture attribute information along with coordinate information in

one handling of the source document.

Heads-up digitizing. Here, the user captures coordinate

information by tracing features directly from data displayed on the

screen. A digitizing table is not used by all. Hence the term

"heads-up"an allusion to the heads-up instrument display

technology developed for aircraft pilots.

Heads-up digitizing is a scanning data entry method that is

commonly used when coordinate accuracy need not be at

engineering levels. As the operator digitizes from the screen,

output accuracy is determined by the accuracy of the source

document, the resolution at which it was scanned, the resolution at

which it is displayed, the resolution of the screen itself, and the

skill of the operator. Usually greater accuracy can be attained by

other methods. Heads-up digitizing is usually performed on

georeferenced raster data sets.

Heads-up digitizing is most commonly performed using scanned

images and thus can be a way to quickly capture the most current

information. Vectorizing methods are the most common way to

capture information from scanned maps, although heads-up

digitizing can be used with scanned maps.

Orthophoto production. Scanned stereopair images can be

digitally orthocorrected to produce a digital orthophoto. Optically

corrected orthophotos can be scanned directly into digital

orthophotos. A digital orthophoto can be plotted at virtually any

scale. For example, a series of 30 x 30 orthophotos can be

scanned, merged into a common database, and reproduced on a

plotter without reference to the original size, coverage, and format

of the hard-copy photos. While output scale can be changed

freely, care should be taken not to "blow up" the image too much

or the output image will appear blocky. Orthophotos can also beplotted with overlays of vector data.

Combining scanned data with other data. The data

produced through scanning data entry may not be the only data of

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interest. For example, scanned and vectorized data can be fit into

a geodetic control network created by coordinate geometry

(COGO) data entry. This technique can be used to localize, or

bound, error in scanned plats. Vectorized data can be combined

with purchased or table-digitized vector data. Accurate

georeferencing is very important when using scanned data with

other data.

Scanned data also have attributes. Adding attributes to scanned

data is usually necessary. For example, scanned contours need to

be tagged with their elevation values for use in digital terrain

projects. If scanning data entry software is integrated with other

GIS software tools, any or all of those tools can be made part of

the scanning data entry and data automation process.

Incremental data automation. Some organizations have

adopted an incremental approach to database creation. Incremental

methods include in-house staff digitizing on a time-available basis,

addition of digital data from other sources, such as CAD data from

land developers, or conversion of other digital data such as legal

descriptions. Incremental database generation approaches can

work. But, since any GIS must have data in order to be effective,

it can be wise to input some data immediately so as to demonstrate

immediate GIS benefits.

Scanning data entry supports incremental database development.

With scanning data entry, a raster database can be produced

quickly by scanning maps or air photos and georeferencing the

scanned images to real-world coordinates. The georeferenced

raster data can provide immediate benefit as a visual backdrop to

other data and as a data source for vector conversion. The raster

database can provide complete seamless coverage for an agency's

entire area of responsibility (e.g., a city or a county), and vector

conversion can proceed incrementally, on an as-needed or highest-

need basis.

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ESRI Scanning Data

Entry Procedures

ESRI's experience as a user of scanning data entry technology enables

us to share a very practical viewpoint. The ESRI Database Develop-

ment Group has used scanning data entry for many projects. The

Digital Chart of the World project, performed by ESRI as the prime

contractor to the Defense Mapping Agency, used scanning data entry

to develop a 2.8 gigabyte vector database from over 2,800 source

documents. The ESRI Database Development Group has wide

experience with scanning data entryand much of this experience is

reflected in this chapter.

The group adapts scanning data entry technology and methods for

each project, as determined by the project goals and available source

documents. Even so, a standard processing sequence has evolved.This processing sequence is shown in the following flowchart. Note

that some sort of pre- and/or post-processing is an assumed

requirement. Scanning data entry can reduce data automation time

requirements, but it will not eliminate them.

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Find or draft georeference marks(tics) on source documents

Hand prep source documents

Determine scan resolution Determine proposed

processing flow

EvaluateProjectGoals

EvaluateSource

Documents

Scanning Data Entry Procedures

Maps Air photo s

Post process(vector cleanup)coverage data

Test and set scannerparameters

Heads-up digitize

ScanDocuments

Pre-process (rasteredit) raster data

Vectorize

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ESRI Scanning DataEntry SolutionsA GIS Focus

ESRI's experience as a manufacturer and user of GIS

technology enables us to view scanning data entry fromthe special perspective of GIS needs and requirements.

GIS requirements are different from CAD and engineer-

ing requirementsESRI solutions are based on GIS

requirements. For example, GIS data typically depict

irregular features from the real world, not the right

angles and straight lines of CAD drawings.

ESRI provides a wide range of products and services that can support

GIS scanning data entry projects. ESRI brings its experience in GIS

and scanning data entry together in a software package calledArcScan. ArcScan is specifically designed to support GIS scanning

data entry projects. ArcScan is a fully integrated extension to

ARC/INFO, and takes advantage of ARC/INFO's complete GIS

functionality.

ARC/INFO itself provides much ancillary capability to scanning data

entry projects, including vector data editing and management.

ARC/INFO provides additional raster data support through its bundled

IMAGE INTEGRATOR capabilities. ARC/INFO software extensions

for surface modeling, raster data modeling, network modeling, and

coordinate geometry can all play a part in scanning data entry projects

because these capabilities are available in the common ARC/INFOsoftware environment.

ArcScan, ARC/INFO, ArcView, ArcCAD, third-party software

integrated with ESRI software, the ArcData program, ESRI services,

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and the ESRI hardware reseller program can be flexibly matched to the

exact requirements of your scanning data entry project and your GIS

implementation.

ArcScan ArcScan is a set of software tools that support data automation usingscan digitizing. These tools permit GIS applications to automate

vector databases using scanned raster data sets as input. ArcScan is

an extension to ARC/INFO and is fully integrated with the ARC/INFO

software environment. The ArcToolsArcScan menu system

provides an easy-to-use interface for raster editing and interactive

vectorization. ArcScan includes a Users Guideand Command

Referencethat describe ArcScan capabilities to users.

ArcScan Capabilities ArcScan users can create ARC/INFO coverages by extracting linefeatures from scanned monochrome document images. This is done

using interactive, automated line-following software within

ARCEDIT. Examples of linear features that can be extracted and

added to a coverage include street lines, utility lines, contour lines,

parcel boundaries, and soil polygon boundaries. This technique for

digitizing features is simpler, more accurate, and often faster than

traditional manual and heads-up digitizing.

ArcScan provides tools for editing monochrome, grayscale, and

pseudo-color single-band imagery within ARCEDIT.

ArcScan provides a powerful and efficient set of tools in ARC/INFO

for importing, correcting, editing, plotting, and exporting scanned

raster images. ArcScan supports industry-standard raster data formats

and can accept data from many types of scanners.

ArcScan Components ArcScan functional components include raster database constructiontools, raster pre-processing tools, integrated raster-to-vector editing

tools, and an interactive raster-to-vector conversion tool. ArcScan

soft-copy and hard-copy raster display is provided using standard

ARC/INFO display functionality. ArcScan tools work with the highly

efficient ARC/INFO grid raster data format.

Raster DatabaseConstruction Tools

Scanned raster images in a variety of standard formats (e.g., TIFF,

RLC, SunRASTER) and compressions (e.g., Run Length

Compressed, CCITT Group III, CCITT Group IV) can be converted

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to and from compressed grids using the IMAGEGRID and

GRIDIMAGE conversion tools. The GRIDMERGE tool can be used

to build a raster database from georeferenced input grid raster data

sets. The ArcScan conversion tools transfer runs of data directly,

rapidly converting large scanned monochrome documents.

Geometric Correctionand Noise

Removal Tools

The ArcScan raster pre-processing tools prepare raster data sets for

additional processing. A set of geometric correction tools can be used

to correct orientation errors during scanning, distortions in the source

document, and georeferencing. These tools perform the following

operations:

Rotate a grid by a multiple of 90 degrees. Commonly used when

documents are scanned sideways. Flip the contents of a grid from top to bottom. Commonly used

when documents are scanned upside down.

Mirror the contents of a grid from left to right. Commonly used

with translucent documents that are scanned wrong side up.

Correct a skewed document by converting a user-specified

parallelogram on the input document to a rectangle on the output

document. A common distortion encountered in scan digitizing is

a skew caused by paper feed that is not perfectly aligned.

Apply a warping transformation to georeference a grid to real-world coordinates.

The following noise cleanup tools can be applied to either the entire

image or a selected image area:

Remove specks of black noise from a scanned image. Most

scanned documents will show speckling to varying degrees.

Apply a majority rule filter to a scanned image. Commonly used

to correct dropout in noisy scanned lines.

ArcScan raster datamanagement menu.

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Integrated RasterVector Editing Tools

Grids can be edited in conjunction with coverages. The ARC/INFO

software environment supports editing monochrome, grayscale, and

pseudo-color raster data. Users can edit multiple grids during an

ARCEDIT session. ARCEDIT supports full pan and zoom display in

map coordinate space of both raster and vector data. A multilevel

undo capability provides the user with a "safety net" during raster

editing. The user can concurrently edit both raster and vector data.

The editing tools include

Filling Tools: Fill the interiors of user-defined boxes, circles,

and polygons. Fill a connected entity of pixels by pointing to any

pixel in the entity.

Drawing Tools: Rasterize the boundaries of user-defined

boxes, circles, and polygon.

Pixel Editing Tools: Set and query the value of individual

pixels by pointing to the screen.

Brush Tool: Change the value of pixels in the grid by dragging

a brush on the screen.

Rasterization Tools: Rasterize the selected set of arcs into theedit grid.

Selection Tools: Select all cells within a box, circle, or

polygon.

Geometric Operations: Move, rotate, flip, and mirror the

selected region.

Filtering Operations: Despeckle, smooth, or enhance the

selected region.

Georeferencing Tools: These tools allow a user to

interactively position and rescale the edit grid in map coordinate

space, deskew the edit grid, and warp the edit grid using a link

coverage created in ARCEDIT. Georeferencing is accomplished

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by identifying common points in the raster data set and in real-

world coordinates.

The mouse is used to register theimage to real-world coordinates.

InteractiveVectorization Tools

With ArcScan you can extract the centerlines of linear features from a

raster document with optimized user intervention. Interactive raster-

to-vector conversion using an automated line-following, or line-

tracing, tool is especially useful for selective raster-to-vectorconversion from a raster data set with multiple data layers. The

ArcScan line tracer, because of its high degree of user control, can

also vectorize complex and difficult data. With the line tracer tool you

can efficiently produce high-quality vector output. The trace tool

performs automatic intersection straightening and automatic line

generalization based on user parameters.

The ArcScan Tracing Tool worksin the ARCEDIT environment.

The trace tool snaps to the center of a raster line. Tracing begins atthat point, stopping at junctions to obtain user input. The user

interacts with the tracer using the mouse or keyboard, and controls the

direction taken by the tracer at the junction. Tracer features include the

ability to jump gaps, the ability to snap to the center of a heavy raster

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line, and smart retrace. Built-in junction memory prevents retrace of

explored paths, offering improved line tracing efficiency. Line

following can be done with user interaction, or in fully automatic

mode, all connected line work within a defined area can be vectorized

without operator intervention. The line tracer can work from bi-tonal

ARC/INFO grid or RLC raster data.

ArcScan provides automaticcleanup of line intersections

guided by user set parameters.

Because the line tracer is built into ARCEDIT, you can interleave

manual digitizing with line following, enabling more productive

processing of noisy data. You can use a menu interface for heads-up

digitizing to tag features with attribute data shown in the raster

document. The line trace tool works with the multiple windowing

capability of ARCEDIT. ArcScan automatically moves a close-up

view of the tracing activity, following the tracing activity even as itmoves out of view.

ARC/INFO ARC/INFO is a full-feature GIS capable of meeting the complexrequirements of a wide variety of GIS applications. All the

capabilities of ARC/INFO can be applied to scanning data entry

projects. One of the most important features of ARC/INFO for

scanning data entry projects is ARC/INFO software's user interface

environment, ArcTools. ArcTools organizes ARC/INFO software's

thousands of GIS tools and provides a single look and feel to

ARC/INFO functionality, including the ArcScan extension.ARC/INFO capabilities can be fully customized using the ARC Macro

Languageprocessing procedures for scanning data entry can be

tailored to the specific needs of the GIS application.

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The integrated ARC/INFO software environment makes allARC/INFO functionality immediately available. This offers a

scanning data entry project the ability to take advantage of ARCEDIT

vector and attribute editing capability. This ARC/INFO data

automation functionality can be added to the techniques specific to

scanning data entry. The final result of scanning data entry is a

topologically correct ARC/INFO georelational database that can

support GIS analysis and advanced display.

An important feature of ARC/INFO software's integrated environment

is that all editing and data entry functions can use the ArcStorm

database manager. ArcStorm (ARC/INFO STORage Manager)provides feature-level access to seamless spatial databases. ArcStorm

supports production-level data entry by multiple users.

Because scanning data entry is implemented in the ARCEDIT

environment, advanced spatial editing features can be utilized. These

advanced features include editing of complex and user-defined

features and interactive topology creation. In short, once the edit

session is over, no further processing is required.

The IMAGE INTEGRATOR functionality included with ARC/INFO

provides image conversion, management, and display capabilities fora wide variety of image data (see Table 2), including industry-standard

formats used by most scanner vendors. IMAGE INTEGRATOR

brings image handling capability to scanning data entry projects and

provides such benefits as concurrent raster and vector display for

heads-up digitizing and raster plotter support for hard-copy

production. These images can be easily converted into the user-

selected format of preference, geo-referenced and kept in an

ARC/INFO Image Catalog as a seamless raster database.

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Importantly, the IMAGE INTEGRATOR can also display images that

do nothave the inherent geographic component that maps and satellite

images do. This type of image can also be scanned from input

sources such as photographs, textual documents, and video input.

This type of image cannot be georeferenced and is commonly used as

a pictorial attribute of a coverage feature. A DBMS capable of storing

Binary Large Objects (BLOBs) can be used to manage this type of

image. ARC/INFO can access BLOB data in a DBMS.

Video image attribute, stored as aBLOB in an external DBMS

table, can be displayed with theIMAGE INTEGRATORcommand IMAGEVIEW.

Scanned document informationcan also be stored as a BLOB and

displayed using theIMAGEVIEW capability.

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grabbers can also be displayed during an ArcCAD session. Scanning,

raster-to-vector conversion, and raster editing are also available.

ArcView ArcView, ESRI's desktop GIS display and query software, is capableof viewing all the image formats supported by ARC/INFO. ArcView

can play a role in a scanning data entry project as a "quick look" tool

to view and verify scanned data. ArcView can display raster and

vector data simultaneously and can provide quick output of raster

graphics in industry-standard graphic formats such as PostScript.

ESRI Services ESRI provides a full range of services including training, databaseautomation, application development, and on-site technology transfer.

Since 1969, ESRI has supported hundreds of organizations

throughout the world in the design, development, and implementation

of GIS. ESRI's services support the complete GIS life cycle,

including implementation planning, system integration, database

development, application development, and system operation. ESRI

is unique within the GIS industry in its ability to provide such

comprehensive services in combination with a complete set of leading

GIS software.

Working with the leading hardware vendors, ESRI has successfully

provided turnkey geographic information systems to hundreds of

clients.

ESRI offers new ArcScan users an on-site ArcScan Start-up Support

Package. This package is two days of consulting and technical

training support by an ESRI technical analyst to help the new ArcScan

user implement this technology. The goal of the support package is to

transfer the skills necessary to begin using the ArcScan software in a

production environment. The subjects covered are document

preparation, system initialization, ArcScan software usage, quality

assurance techniques, and data structure considerations for scanning.

The ArcScan Start-up Support Package is provided at your site using

your equipment.

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ArcData ArcData is ESRI's program for providing published spatial andgeographically related digital data in ARC/INFO supported formats.

Existing off-the-shelf vector and raster databases can complement

scanning projects. The ArcData program includes satellite and other

imagery. Through the ArcData program, leading data vendors such as

EOSAT, Spot Image, and Hughes STX can provide imagery for

locations worldwide that can be used in scanning data entry projects.

Supported Devices As mentioned previously, ARC/INFO supports industry-standardraster data formats. For a scanner to be usable with ARC/INFO it

must output raster data in one of these standard formats, preferably

TIFF or RLC. Other factors, such as direct output to the UNIX file

system and UNIX-based controller software also bear on scannerease-of-use. ArcScan has been tested with scanners from leading

manufacturers. The section on evaluating scanning data entry

provides more information on scanner features that are important.

ESRI's machine-independent philosophy allows ARC/INFO users to

take advantage of new hardware developments as they occur.

ESRI defines level of support for peripheral devices, such as scanners

and plotters, using a numerical classification system. TheARC/INFO

Users Guide, Supported Devices, UNIX Workstations (Rev. 7.0)

provides classifications for specific scanner, plotters, and other

devices. The classification categories are outlined below.

Classes of SupportedDevices

There are five classes of support for supported devices:

Class 1: Fully

Supported,

In-House at ESRI

Class 1 devices have been tested at ESRI, run successfully with

ARC/INFO, and have an interface (driver, interface file, etc.)

provided with ARC/INFO Rev. 6.1.2. Any problems that occur with

these devices can be tested on-site because the device must remain on

ESRI premises for it to remain a Class 1 supported device. This is the

highest level of support.

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Class 5: Unknown The status or ability to interface this device is unknown at the time of

this publishing. New devices as well as devices not included in this

document that have not yet been tested fall into this category.

Class 6: Not

Supported

These devices will not work with ARC/INFO and therefore cannot be

supported. In most cases, unsuccessful attempts have been made to

interface these devices.

Citations Litton, Adrien L. "Automated Data Capture: The ScanningSolution," Proceedings, 1993 ARC/INFO User Conference

ARC/INFO: GIS Today and Tomorrow, ESRI White PaperSeries, September 1992

ArcCAD, The Integration of CAD and GIS, ESRI White Paper

Series, April 1993

Supported Devices Guide, UNIX Workstations

(ARC/INFO Rev. 7.0)

WorkStation ARC/INFO Technical Guide to Hardware Options

ARC/INFO Users Guide,Cell-based Modeling with GRID

A Wide Range of High-Quality Support for All Your GIS Needs,

ESRIBrochure

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GlossaryDefinitions of key terms that will helpyou to understand the concepts discussedin this document.

ARCEDIT ARCEDIT is the ARC/INFO environment for editing coveragecoordinate data and descriptive data. Its sophisticated graphic andediting capabilities provide the tools necessary for accurate data entry

and manipulation. These capabilities are important for creating and

maintaining geographic databases.

ARCPLOT ARCPLOT is the ARC/INFO environment for providing cartographictools for all of your ARC/INFO mapping needs, including full

cartographic design, display, and production capabilities.

ArcScan ArcScan is the ARC/INFO extension that provides capabilities tosupport scanning data entry. ArcScan is closely integrated with other

ARC/INFO functionality, particularly IMAGE INTEGRATOR and

ARCEDIT.

ArcTools ArcTools is a graphical user interface (GUI) environment providedwith ARC/INFO. ArcTools provides a consistent menu interface to

ARC/INFO and its subsystems and extensions.

attribute An attribute is a characteristic of a map feature described by numbersor characters, typically stored in tabular format, and linked to the

feature by a user-assigned identifier. For example, attributes of a

well, represented by a point, might include depth, pump type, and

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owner. Feature attribute information is often present in source

documents as symbology or annotation. For example, a plat map may

show Parcel Identification Numbers (PINs).

attribute table Attribute tables are tabular, flat, or relational files directly associated tothe spatial data and form the "relational" half of the georelational data

structure. ARC/INFO functions maintain the integration of spatial and

attribute data in feature attribute tables. Additional attribute

information may be kept in external attribute tables, perhaps

maintained as tables in an RDBMS.

bandwidth Bandwidth is a way of expressing how much data can be transmittedacross a communications medium at any one time. The higher the

bandwidth, the more activity the communications channel can support.

For example, local area networks have higher bandwidth than serial

communications links. Bandwidth is often measured in megabytes

per second.

bit The smallest unit of information that can be stored and processed in acomputer. A bit has two possible values, 0 or 1, which can be

interpreted as BLACK/WHITE or ON/OFF. Bi-tonal data can be

compressed into images that represent cell values with a single bit.

bi-tonal Bi-tonal, as applied to raster data sets, means the raster data have onlytwo possible values. Tonal quality is the brightness value, therefore

bi-tonal data have only two values, black and white.

black noise

Black noise, or addition of data.

Black noise is pixels with black values where the original information

content had white values. This often has the appearance of speckling,

tiny black spots on a white background. Noise is data in an commu-

nication channel that is random or has no informational content.

Noise is usually caused by low data quality and is unwanted because

extra pre- or post-processing can be required to remove it. Blacknoise is the addition of data where none should exist. Seewhite

noise.

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BLOB Binary Large Object. A term often used with database managementsystems. Any large data set handled as binary data; often BLOBs are

raster image data.

categorical data Categorical data consist of values representing discrete categories,such as soil or vegetation type. Also referred to as nominal data.

CCD Charge-coupled device. A CCD is the electronic instrument used inscanners to sense brightness values. CCDs are usually capable of

distinguishing and outputting grayscale data that have a maximum of

256 levels of gray.

cell The basic element of spatial information in a grid data set. Cells arealways square. A group of cells forms agrid.

1

234567

Y-axis

X-axis(0,0)

Rows

Columns

Upper left corner

Value1

234567

Count8

114

125

3010

Cover-TypeW Pine

D FirMixedGrassWaterPavedAgriculture

}Cell size

cell based See raster.

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clutter

129

In this example, the annotation"129" is clutter. It overlays theline and will interfere with

vectorizing.

Unwanted data on a scanned map. Clutter, unlike noise, may have

informational contentbut not the information sought by the data

entry process. Clutter, like noise, can require extra pre- or post-

processing in order to remove it. Line-following raster-to-vector

converters are efficient at dealing with clutter because they utilize

human capabilities to discern clutter from desired data. Annotation

that overlays line work is a common type of clutter.

COGO 1. Coordinate geometry. Software that uses legal descriptions andsurvey information to create spatial vector data.

2. An ARC/INFO software extension.

continuous data Continuous data consist of values representing samples from acontinuous surface, such as elevation values. Also referred to as

ordinal or ratio data.

control point A control point is a location on the image or map having known real-world coordinates. Control points are also called registration marks,

or tics.

coordinates An expression of location in space by the provision of pairs ofnumbers that indicate offset from a known starting point. X,y

coordinates are an expression of position in Cartesian space. A

common coordinate, or georeferencing system, is a requirement forthe concurrent use of different types of data.

corrected photo See orthophoto.

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coverage A digital analog of a single map sheet forming the basic unit of vectordata storage in ARC/INFO software. In a coverage, map features are

stored as primary features, such as arcs, nodes, polygons, and label

points; and secondary features, such as tics, extent, links, and

annotation. Map feature attributes are described and stored

independently in feature attribute tables.

data automation The process of converting analog data such as maps, to a digitalrepresentation of the same information.

data model A data model is a formal method for arranging data to represent thebehavior of real-world entities. Fully developed data models describe

data types, integrity rules for the data types, and operations on the data

types. ARC/INFO software uses a georelational data model, a hybrid

data model that combines spatial data (in coverages and grids) and

attribute data (in tables). ARC/INFO's integrated data model allows

easy conversion between, and concurrent use of, raster and vector

data.

data quality In the context of scanning data entry, data quality refers to the qualityof the source document, that is, the media itself. Data quality does notrefer to the informational veracity, accuracy or precision of the data on

the media. Thus, a well-used, folded, wrinkled, and stained third-

generation blue-line map has less data quality than a new Mylar

overlay map having crisp, high-contrast line work.

DBMS Database management system; often a relational database managementsystem. A DBMS is the collection of software required for using and

manipulating a tabular database, and presenting multiple, different

views of the data. DBMS can also manage Binary Large Objects. SeeBLOB.

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dpi Dots per inch. Dpi is a common measure of resolution in scanners.The more dots per inch (sampling rate) a scanner has, the greater the

resolution.

dropout Dropout is an artifact of the scanning process that results in the loss ofdata where they should exist, such as pixel thinning in line work. See

white noise.

georeference To georeference is to establish the relationship between an image(row, column) coordinate system and a map (x,y) coordinate system.

Georeferencing is accomplished establishing control points that can beidentified in both coordinate systems, then creating the displacement

vectors, or links, between the control points. For example, once a

raster data set is georeferenced to a vector coverage, the raster and

vector data should overlay or register.

georegister See georeference.

georelational data

model

A hybrid data model used to represent spatial features. The

georelational data model encompasses coordinate, topological (geo),and feature attribute (relational) information.

GIS A geographic information system (GIS) is an organized collection ofcomputer hardware, software, geographic data, personnel, and

procedures designed to efficiently capture, store, update, manipulate,

analyze, and display all forms of geographically referenced

information. Complex spatial analysis is possible with a GIS that

would be difficult, time-consuming, or impracticable otherwise.

GPS Global positioning system. A system of geostationary satellites,ground receivers, and associated software that provides an

electronically instrumented means of determining position on the

earth.

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grid 1. A raster geographic data set for use with ARC/INFO software.Each grid cell is referenced by its geographic x,y location. Cells

store values. ArcScan functionality operates on the ARC/INFO

grid data structure for most operations.

2. One of many data structures commonly used to represent map

features. A raster-based data structure composed of cells of equal

size arranged in columns and rows. The value of each cell, or

group of cells, represents the feature value. (Also called

"Raster.")

1

0 0 0 0 0 0

0 0 0 0 0 0

0 1 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

Point features

1

0 1 0 0 0 0

1 1 3 3 3 0

1 1 0 0 3 0

1 0 0 0 0 0

1 2 2 2 0 0

1 0 0 0 0 0

Line features

2

3

1

1 1 2 2 2 2

1 1 2 2 2 2

1 1 1 2 2 2

1 1 3 3 3 3

1 1 3 3 3 3

1 1 3 3 3 3

Area features

2

3

Coordinate

Grid

GRID An ARC/INFO software product that provides a fully integratedraster- or cell-based geoprocessing system for use with ARC/INFO.

GRID supports a map-algebra spatial language allowing sophisticated

spatial modeling and analysis.

grid cell A discretely uniform unit that represents a portion of the earth, such asa square meter or square mile. Each grid cell has a value that

corresponds to the feature or characteristic at that site, such as a soil

type, census tract, or vegetation class. Seepixel.

GUI Graphical user interface. A highly visual and interactive method forsupporting human-computer interaction.

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heads-up digitizing The process of using a high-resolution, bit-mapped display and mouseto automate vector data by tracing features shown as an image on the

screen.

image A graphic representation or description of an object that is typicallyproduced by an optical or electronic device. Common examples

include remotely sensed data such as satellite data, scanned data, and

photographs. An image is stored as a raster data set of binary or

integer values representing the intensity of reflected light, heat, or

another range of values on the electromagnetic spectrum. Seeraster.

image catalog An image catalog is an organized set of spatially referenced, possiblyoverlapping, images that can be accessed as one logical image.

ARC/INFO IMAGE INTEGRATOR can use image catalogs for raster

data in formats such as TIFF or RLC. The ARC/INFO GRID data

structure does not use image catalogs.

IMAGEINTEGRATOR

A collection of image management and display tools in ARC/INFO

that allows vector and raster data to be displayed concurrently. Image

integrator commands are used to georeference and rectify images to

real-world coordinates, display images, and manage image catalogs.

image-to-worldtransformation

Image-to-world transformation is the transformation between image

locations and real-world or map coordinates.

interpolatedresolution

A method employed by scanner vendors to increase output resolution

by use of softwarethat is, each input pixel is interpolated to produce

more output pixels. Interpolating pixel values will not improve the

informational content of the original scanned data and is usually not aneffective method for GIS applications. See optical resolution.

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LAN 1. Local area network. Computer data communications technologythat connects computers at the same site. When computers are on

a LAN, they can share data and other computer resources, such as

printers and plotters. LANs are composed of cabling and special

data communications hardware and software.

2. An ERDAS image processing system file type

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