Process Model

Premathmod D3.2. Database Implementation IST-2000-28177
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PREMATHMOD
IST–2000-28177
STATISTICAL AND MATHEMATICAL MODELLING, DATA
ANALYSIS, SIMULATION AND OPTIMISATION
METHODOLOGIES FOR PRECISION FARMING
Database implementation
D3.2.
Report Version: Final
Report Preparation Date: 01/06/2002
Status: public
Contract Start Date: 01/12/2001 Duration: 18 months
Project Co-ordinator: Lesprojekt sluzby
Martinov 197, 277 13 Zaryby, CZ
Project Officer: Mr. Johan HAGMAN
Project funded by the European Community
under the “Information Society Technology”
Programme (1998-2002)

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Contractual Date of Delivery to the CEC: 01/06/2002
Actual Date of Delivery to the CEC: 01/06/2002
Author(s): Kafka S., Holy S., Charvat K. Gnip P,
Gaspardo L, Dohmen B.,
Participant(s): Help Service Remote Sensing,
Lesprojekt sluzby, MJM,
Technofarming, and AgroSat
Workpackage: WP5
Est. person : 8 months
Nature: R
Version: 1
Total number of pages: 23
Abstract:
The report describes the model implemented for data storage methods within the
Premathmod system. The Premathmod system is an Internet solution based on
a combination of two Open Source tools: Mapserver and GRASS. Mapserver is an
interface tool. It is used for communication with users. Grass is a powerful analytical tool.
The combination of both instruments is a new way of spatial data processing in precision
farming.
The system works with raster and vector data formats, but the data used for an analysis
are in raster format supported by Grass. The system also allows to access data on other
servers via WMS services.
There are two existing basic variants of Premathmod system:
• A farmers’ version for data display
• An analytical version for data analysis
Keyword list:
Precision farming, data storage, GIS, Grass, Mapserver, Open Source, WMS services

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1. EXECUTIVE SUMMARY
The database for storing, access and analysis of field measurement data was
implemented. The measured data are converted into unique raster format. The grid-based
model is used for this purpose. All measured data are converted into this grid model.
Original data are stored in the system and can be displayed.
The workpackage task was:
• To define a standard structure of data
• To create the software tools for conversion of data from various sources and its
transformation to the standard format
The definition of the grid structure is important for the mathematical analysis. This
structure is suitable for Grass analysis. It enables a user to combine and analyse data
from different sources. It is also possible to display the data using Mapserver functionality.
The data security is very important as the private and sensitive data are used within the
system. User ID and a password are used to protect data of the system.
Data in the system are processed by a service company and distributed to the farmer. It is
possible to combine private and public data using Web Mapping services. The
Premathmod system also offers distribution of data to the variable rate application
machines (belonging either to farmers or service companies).

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2. CONTENTS
1. Executive summary....................................................................................................................3
2. Contents .....................................................................................................................................4
3. Introduction................................................................................................................................5
4. Data sources ...............................................................................................................................6
5. Data processing in the Central database of Precision farming...................................................8
6. Data storage................................................................................................................................9
7. Data interpolation.....................................................................................................................13
8. WMS DATA ACCESS............................................................................................................14
9. Data security ............................................................................................................................15
10. Premathmod analysis tool........................................................................................................16
Data import. ...............................................................................................................................16
Map .............................................................................................................................................16
Analysis......................................................................................................................................17
Statistics.....................................................................................................................................18
Metadata ....................................................................................................................................20
Settings.......................................................................................................................................21
11. Future plans:.............................................................................................................................22
12. References................................................................................................................................23

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3. INTRODUCTION
Premathmod project works with new technologies in the field of precision farming. These
methods have to be presented to farmers and managers operating in agriculture.
Premathmod system is becoming a very powerful GIS tool in farm management. A farmer
can get by Internet technologies much more easily essential information for crop growing,
weather, farm management, grain market and so on. One of the most important
knowledge a farmer acquire when adopting this different way of farm management is an
elementary experience of using IT technologies, Internet environment and Precision
farming system in rural daily farm work.
Premathmod system brings new information and communication solution for precision
farming. This solution is based on connection of following advanced technologies:
• Full internet data access based on Open source solution
• Open source geocomputing
• WEB mapping services
• Mobile data access
• Access to different data sources on different places
It is expected, that the data will be stored, processed and analysed by service companies,
but farmers will have a full access to functionality of the system.

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4. DATA SOURCES
Maps - The very important data sources for precision agriculture are field maps. These
maps are typically accompanied by soil maps, parcel maps etc. Such data are digitised
from existing paper maps but the majority of analysed parameters is not described in such
maps (e.g. density), therefore soil samples positioned by GPS device have to be collected
in the field. In central database of Precision farming can such data be used as a
background information layer for field boundary maps and various control points.
GPS measurement, soil sampling and field measurement - Soil samples have to be
analysed for the main nutrients to show their availability for plants in the surface and in
subsurface layers of soil. There are also other parameters affecting the mobility of
nutrients through soil profile and soil intensity and capacity factors. Fluctuation of individual
analysed parameters varies in time and field relief. Field boundaries are measured in WGS
84. Signal GPS and DGPS are received by TRIMBLE AgGPS 132 DGPS. Receiving data
are collected by H-GIS software in Arc view or MapInfo data form (.shx, .shp, .mid, .mif,
.dbf). GPS and DGPS signals are receiver with sub meter accuracy. Data of each field is
transferred to the central database separately under central geographic coordination
(wgs_Lat, wgs_Lon) with six decimal places. Area and distance of a field can be presented
in metric units (hectares, square meters, square kilometres, meters, kilometres). Any other
information, which is coming to database, is sorted and assigned to field boundary. Future
data processing is limited by field boundary. Field boundary is on the top of database tree.
All future maps will be created on this field boundary. In addition, this field boundary can
be changed according to end user needs up to specific crop rotation. Field area can be
separated into several parts and reassembled back after particular use. Basic Lab analysis
is done to determinate a level of phosphorus (P), potash (K), magnesium (Mg), calcium
(Ca) in mg in 1kg of soil and pH of soil. The Lab determination of content of each nutrient
is done by method MEHLICH III. The Lab results of soil analysis are converted into .xls or
.dbf formats. The data are then transferred to geographic coordinates in central database,
according to ID number of soil sample.
Yield maps – measuring of crop - Yield maps are important data source for controlling the
fluctuation of soil parameters. The yield maps construction is connected with spatial
technological equipment on the harvesting machines. The goal of this project is not to
directly develop the equipment for measuring yield on harvesting machines, but the
methodology of yield maps constructions. Another important part is measuring of nutrients
level, other chemical elements, and moisture during harvest and etc. Data are collected in
row format according to producer technology. (.yld –Ag Leader, .fsy – Massay Ferguson
and etc.) In fact, data are collect as follows:
• Mass flow in kg.m-2
• Moisture in %
• Yield volume kg.m-3
• Harvest speed km.hour-1
• Id field
• Crop
• GPS time and etc.
Those raw data are processed and the final result is presented as grain in kg*ha-1 in
calibration moisture. For example: Winter wheat…6,000 kg.ha-1, moisture 15%
Satellite imagery - Remote sensing images were not usually used in Europe. Small image
resolution and cloudy weather are the most important reason for that. High-resolution data
are also affected by cloudy weather, so that radar data seems to be the most interesting
remote sensing data.

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To set a management zone on the field, to check a lack of Nitrogen during spring
vegetation or to predict a yield before the harvest, data from Landsat 5 and Landsat 7 TM
were used with accuracy 30x30 m one pixel in multispectral. Landsat 5 – 7 spectral
bands, Landsat 7 – 8 spectral bands
Data format: CEOS, Level: SYSCOR, Cal: Pre-Flight, Rsmlp: NN
Air pictures - Aerial photographs can be used until now more effectively than RS data.
Colour infrared materials are mostly used in agriculture, because plant production can be
easily identified on these images.
Raw data was made up as a spectrozonal photo in geodetic format –3 bands (infrared,
near infra red and false colours). Altitude 2,500m, accuracy one pixel = 60cm x 60cm, 1
scene 5km x 5km.
Fertilisation maps – amount of fertilisation- the fertilisation maps from previous season are
also important information source for evaluation of soil parameters. Application data are
stored in central database as an information layer. Data could be stored as an application
map - .sti, .tif (simple or multi level variable application maps) or “as applied” data - .rpt
(monitoring of application what was done by application machines). All variable application
maps are in .tif format (demo maps) or .sti format (controller map).

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5. DATA PROCESSING IN THE CENTRAL DATABASE OF PRECISION FARMING
The collected data have to be processed individually according to the character of the
data.
• Field boundary mapping – error data are corrected in the file, changes are updated
• Electro-magnetic conductivity mapping- error data eliminated out of the file,
• Data processing and data classification of the selected field. Data processing and data
classification of the monitored area. Establishing the soil zones. Setting up a grid for
manual soil sampling or soil sampling by soil sample machines
• Soil sampling data – Controlling of the process of soil sampling in the net.
• Pre-processing of the soil samples before starting the lab analysis –air drying, sorting
and aliquotation, lab analysing after “Mehlich III”
• Soil test maps – data of the lab analysis are integrated in the central database and they
are processed there.
• Soil test maps are an important information layer for further recommendations.
• Yield data- the raw yield data are processed to a yield map. Such a yield map is
an additional important information layer for recommendations
• Agronomic data – data are coming from an end user (crop rotation, organic matter,
plan of yield and so on.)

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6. DATA STORAGE
The following types of data are stored in Premathmod system:
Field measurement
• Field boundaries – original measured vector data in ShapeFile polygon form (the data
are stored in separate file for every farm and this layer is accessible only after a user
login. Information of the field and field boundary is divided as follows:
o Year
o Crop rotation
o Organic matter spreading
• Sampling data –original measured vector data in ShapeFile polygon form (the data are
stored in separate file for every farm and this layer is accessible only after a user login.
There are following attributes
o Year
o Control points GPS
o NDVI
o Ca
o K
o Mg
o P
o PH
o CEC
o K/Mg

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• Yield measuring - original measured vector data in ShapeFile polygon form (the data
are stored in separate file for every farm and this layer is accessible only after a user
login
• Conductivity measuring - - original measured vector data in ShapeFile polygon form
(the data are stored in separate file for every farm and this layer is accessible only alter
a user login
• N sensor measuring - - original measured vector data in ShapeFile polygon form (the
data are stored in separate file for every farm and this layer is accessible only after a
user login
• Digital aerial photos – colour or infrared digital photos in raster form (data are stored in
form of colour composition for displaying, but also in the form of R, G, B components
for data analysis)
• Digital satellite images (Thematic Mapper, Spot), data in raster dorm (data are stored in
form of colour composition for displaying, but also in the form of spectral bands for data
analysis
Data for an analysis
The data for an analysis are stored in Premathmod system in raster form. The raster form
allows very easy and effective data analysis using Grass system. These entire layers are
accessible only after a user login. These layers are:
• Maps of nutrients content – raster maps derived from soil sampling. These data are
sorted according to nutrients and years
Differences in nutrients content in different years – data in raster form. Soil test data
are monitored also in time of sampling. This part can give to a user a quick overview of
soil nutrients level changes in topsoil layer. Color maps are prepared for each nutrient
separately, for example:
- Ca 1999– 2002
- CEC 1999– 2002
- K 1999– 2002
- K/Mg 1999– 2002
- P 1999– 2002
- PH 1999– 2002

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Maps show only very general results in three levels of categories as are:
- Decrease
- No change
- Increase
• Yield maps – raster maps derived from yield monitoring. These data are sorted by
years
• Conductivity maps - - raster maps derived from conductivity measurements. This data
are sorted by years
• N sensor maps - - raster maps derived from N sensor measurement. This data are
sorted by years
• Maps derived from satellite and aerial picture data as NDVI (normalised vegetation
index
Outputs data
The data in raster form, which are generated by Premathmod system.
• VRA recommendation - data Variable Rate Application or Multi Variable Rate
Application by table or variable color map for following nutrients:
- A phosphorus (in fertilizer)
- A potassium (in fertilizer)
- A calcium (in fertilizer)

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• Price map helps to make a short overview of prices per hectare (including
application) for following nutrients:
- A potassium
- A phosphorus
- A calcium
- Both potassium and phosphorus
Supporting data
Here could be included also, other data layers supporting easier orientation, it could be:
• Cadastral maps
• Basic maps
Both types of data could be used in vector or raster form. Data could be accessed from
distant servers using WMS services.

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7. DATA INTERPOLATION
The working raster maps are generated from original input maps using interpolation
methods. There are three interpolation methods used in GRASS:
• Inverse distance weighted. It fills a raster matrix with interpolated values generated
from a set of irregularly spaced data points using numerical approximation
(weighted averaging) techniques. The interpolated value of a cell is determined by
values of nearby data points and the distance of the cell from those input points. In
comparison with other methods, numerical approximation allows representation of
more complex surfaces (particularly those with anomalous features), restricts the
spatial influence of any errors, and generates the interpolated surface from the data
points. This method is rather usable for data of different types than DEM, like
chemical component spreading etc. This method is available in Premathmod
analysis tool for nutrient content computations. Detailed program description is in
[1].
• Regularized spline with tension and smoothing. This method has been originally
developed for DEM computation. It is derived from known methods of bicubic
splines interpolation, but involves smoothing factor, which permits resulting DEM
not to go exactly through original points. This is very usable for DEM derived from
contours for removing artificial waves. Program enables computing surface from
point as well as contour (vector) input data. Resulting DEM may be affected by
many user-set parameters. Detailed description of algorithms used, and of program
parameters are in [2], [3], [4].
• Recently new Kriging interpolation algorithm was introduced into GRASS. It was
originally developed for element ore content evaluation, so we considered it useful
for nutrient components interpolation. More detailed description of algorithms used
and of program parameters is in [5].

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8. WMS DATA ACCESS
The technological background WMS is based on OGC standards. This approach ensures
base conditions for interoperability of used geodata services. Until this date OGC
published several standards for sharing geodata over WWW. To the most developed and
also adopted by wide community belongs WMS.
The WMS is a Web Map Service (specifically an OGC Web Map Service). A WMS is
capable of producing maps drawn into a standard image format (PNG, JPEG, etc) based
on a standard set of input parameters. The resulting map can contain "transparent" pixels
where there is no information and thus several independently drawn maps can be laid on
top of each other to produce an overall map. This is possible even when the maps come
from different Web Map Servers.
The exact specification of the WMS can be found at the following web address:
http://www.opengis.org/techno/specs/01-047r2.pdf. The last specification has version
number 1.1.0, but majority of contemporary solutions support previous version 1.0.0.
WMS is based on a capability to answer several formalized requests. To the requests
belong:
GetCapabilities first allows a client (or client proxy) to instruct a server to expose its
mapping content and processing capabilities.
GetMap enables a client to instruct multiple servers to independently craft "map layers"
that have identical spatial reference system, size, scale, and pixel geometry. The client
then can display these overlays in specified order and transparency such that the
information from several sources is rendered for immediate human understanding and
use.
GetFeatureInfo enables a user to click on a pixel to inquire about the schema and
metadata values of the feature(s) represented there.
WMS is based on a transfer of georeferenced maps i.e. images or drawings generated on
request. In some cases a transfer of geofeatures is demanded instead of maps. This
possibility belongs to communication among geodata sources. Such type of the service is
covered by another OGC specification WFS – Web Feature Service. This specification
defines formal request over WWW, which returns OGC simple features in GML format.
GML is an XML dialect for geographic features. Specification consist of two requests:
GetFeatue
DescribeFeatureType
Self-WFS isn’t still in version 1.0, but GML is well defined. Specification GML ver. 2.1.1
can be found at http://www.opengis.net/gml/02-009/GML2-11.pdf. Because of very few
implementations of WFS, for needs of Premathmod we try to develop simple web feature
broker, which cover the necessary subset of WFS capabilities (more in [8]).

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9. DATA SECURITY
Every user can have different data access to the map application layers according to his
data access rights. With this mechanism it is possible to publish some of the maps or data
of the focused application, which was impossible in the previous solution. Now the
registered user can edit only layers of every application reserved for him. Thus layers with
protected information are available only for the registered user.
Every login and user passwords are saved now into proper file in the encrypted form.
All graphic data are saved off the www server tree.
All configuration files of map applications are saved off the www server tree.
The settings enable to open certain applications from the central computer to Internet
environment. This modification brings to a network administrator and to users these
advantages:
In case the data are placed at the more powerful internet server, connected to the
spinal internet network, it enables several times faster data access comparing to
situation, when the data are stored at the company’s central computer and the data
access is available via mobile users (using GSM, GPRS or HSCSD technology) to the
database.
This new solution significantly decreases call fees to all the system users (shorter
connect time of a user via provider to GSM+GPRS (HSCSD))

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10. PREMATHMOD ANALYSIS TOOL
The application is developed for precision farming experts as a tool for management zones creation
and fertilizer application recommendation. Thus most of the functions are not targeted for farmers,
but there is a set of functions to be accessible by both groups. A farmer will be able to introduce his
parameters to application, e.g. crop rotation plan and will be able to see resulting application maps
as well as economic calculations as seen in of-line Premathmod application. Expert can use this tool
for an interactive check, to analyze and correct input data, to combine and analyze different types of
data to set potential management zone estimation, to discover potential problems and to compute
application maps. We presume, that this tool will enable a farmer to find the optimal algorithms of
application map formulas and then in the second step these formulas will be implemented as a
“black box”. Currently the application enables a farmer or a manager to upload, analyze and
visualize data in 2D and 3D view.
Currently supported functions:
Data import.
Currently only soil samples and field boundary shapefiles are allowed to be uploaded.
Other (mainly raster) data may be imported with ftp access. The structure of data is
described on the application Help page. For faster upload the data are compressed in the
zip archive. User has to set data cartographic system.
After upload these actions are performed on the server side:
• Unzipping an archive
• Filenames checking
• Required data structure checking
• Transformation from uploaded data cartographic to target system (it is set by
administrator in the process of creating new farm)
• Importing data into GRASS internal format (sites for soil samples, raster for field
boundaries)
Map
Shows the map of farm. It enables a user to display the results of an analysis as well as
original data. The topographic map is accessible via WMS (web mapping services) from
different server. Map application enables a user to edit points, lines and polygons including
multipart ones and many other functions (length/area measuring, GPS support,
searching/location, data filtering etc.). Training areas for supervised classification may be
added/edited.

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Analysis
Three forms are on the analysis page:
1) Nutrient content computation. It computes the nutrient content raster maps from soil
samples point data. A user has to select an appropriate year of soil sampling and
an appropriate year of field boundaries data (the farm property may vary in time). Two
interpolation methods are currently supported:
• Inverse distance weighted. It is simply common method for interpolation. The
GRASS function s.surf.idw is used.
• Regularized splines with tension and smoothing. This special method was
developed originally for elevation models created by Lubos Mitas and
Mitasova [3]. The GRASS command r.surf.rst is used for interpolation. This
computation is better but slower.
2) Management zones estimation. This allows a user to use unsupervised or supervised
classification on an arbitrarily chosen set of raster maps. User may check normalizing
values to preprocess data to have the same weight (or scale) in consequent
computations. For unsupervised classification the desired number of clusters has to be
set. Classification methods used:
• Unsupervised. The GRASS i.cluster is used to compute signatures. Then
maximum- likelihood algorithm is performed (i.maxlik).
• Classification report is stored as the part of metadata.
• Supervised. The GRASS i.gensig is used to determine signatures for supervised
classification. Then maximum-likelihood algorithm is performed (i.maxlik).
• Supervised+unsupervised. Initial signatures are determined by training areas
with GRASS i.gensig. A clustering algorithm (i.cluster) is used to set the result
signatures. Maximum-likelihood algorithm is then performed (i.maxlik).
• Supervised-texture. This algorithm has not been implemented yet.

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Results are stored with names zones.*, where * is user set extension name.
3) Map calculator. A user is allowed to perform various raster operations with this tool:
to set a name of the result map, description, mask raster if desired and the right-side
formula. Underneath GRASS r.mapcalc command is then performed. It is a very
powerful tool for various analyses.
Some examples:
• K content difference k2002-k1999
• vegetation index float(tm4–tm3)/ float(tm4+tm3)
• making mask if(k2002>400)
Statistics
Some univariate statistics, histograms, correlation matrices and 2D histograms (correlation
graphs) are shown for user chosen raster maps.

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3D view
Perspective view of raster data is allowed. A user can select elevation raster - raster
holding heights in 3D View. Default is ELEVATION but you may use whatever raster, e.g.
nutrient content etc. colour raster - cover raster to be shown on elevation. The colour
depth is limited for the present however.
After confirmation, 3D View is shown in the new window. You may use these parameters
to change the 3D View appearance:
Exaggeration Multiply factor of elevation raster to highlight a terrain dynamics.
Lines density Covering net lines density. If 0, no lines are shown.
View azimuthAzimuth (degrees) of "user eye". If 0, The view from south to north is set.
View
distance
Distance (meters) of "user eye" to the centre of the map.
Vertical
angle
The angle between user view and horizontal plane.
Target height Height of the target of view point (the point on/below the centre of map)
Field of View
Horizontal angle (degrees) of the user's view (changing "camera focal
length").
Metadata
The metadata system based on native GRASS metadata is extended according to
the Premathmod specification. Here a user may delete raster maps he created before as
well as to edit some metadata of maps.

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Settings
It enables a user to set raster resolution of raster maps. To take the effect, all data must
be reloaded to system.
Client system requirements
- MS IE > 4.0
- Java 1.2 or higher virtual machine browser support

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11. FUTURE PLANS:
- Uploading raster data through forms
- Kriging interpolation for soil samples
- Importing and processing yield and N-sensor data
- Implementing ISO TC/211 compatible metadata subset
- Interactive editing management zones via internet browser
- Application recommendation maps computation
- Converting results (e.g. management zones and application recommendation) to vector
format
- Download results back to client

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12. REFERENCES
1. GRASS r.surf.idw manual page (http://www.geog.uni-
hannover.de/grass/gdp/html_grass5/html/s.surf.idw.html). Hannover, 2002.
2. GRASS r.surf.rst manual page (http://www.geog.uni-
hannover.de/grass/gdp/html_grass5/html/s.surf.rst.html). Hannover, 2002.
3. Mitasova H. and Mitas L. 1993: Interpolation by Regularized Spline with Tension: I.
Theory and Implementation, Mathematical Geology 25, 641-655.
4. Talmi, A. and Gilat, G., 1977: Method for Smooth Approximation of Data, Journal of
Computational Physics, 23, p.93-123.
5. GRASS r.surf.krig manual page (http://grass.itc.it/gdp/html_grass5/html/s.surf.krig.html)
Hannover, 2002.
6. GRASS d.3d manual page (http://www.geog.uni-
hannover.de/grass/gdp/html_grass5/html/d.3d.html). Hannover, 2002.
7. Mapserver homepage. (http://mapserver.gis.umn.edu/). Minnesota, 2002.
8. M.Konecny, K.Stanek, M.Vesely, K.Charvat WirelessInfo IST IST 1999-21056 D12.1
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