GIS FINAL.pdf

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Imperial College of Engineering and Research, Wagholi, Pune.
(Approved by AICTE, Delhi & Govt. of Maharashtra, affiliated to SavitribaiPhule Pune University)
Gat.No.720,Pune-Nagar road,Wagholi,Pune,412207
NAAC accredited with “A” Grade
Department of Civil Engineering
Class: TE Civil
Remote Sensing and GIS (2019 Pattern-301014)
LAB MANUAL
PRACTICAL 1: STUDY OF FUNDAMENTAL TOOLS OF SOFTWARE FOR DATA
PROCESSING
Access GIS software remotely
• Virtual Lab (vLab)
The vLab has ArcGIS, GeoDa, and Google Earth Pro. To access vLabon your personal
computer, see the instructions provided via the link above.
• ArcGIS Online
Use your browser to find, explore, and analyze spatial data. Use the Enterprise login.
When prompted for the URL enter uchicago.
• ArcGIS for Desktop
The ResearchComputing Center provides student copies of ArcGIS for Desktop.
Free desktop GIS software
GeoDa
The premier free and open-source tool for spatial analysis.
QGIS
Free and open-source GIS for creating, editing, visualizing, analyzing, and publishing
spatial data.
Google Earth Pro
Free desktop software for viewing, creating, and displaying spatial data and information.
ggmap for R

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For those familiar with R/RStudio, install the ggmap package to visualize and analyze
spatial data.
Web GIS
Popular online tools:
• ArcGIS Online
Use your browser to find, explore, and analyze spatial data. Use the Enterprise login. When
prompted for the URL enter uchicago.
• Carto
Create maps and apps for business decision making.
• Google MyMaps
A fun tool for getting started with web mapping.
• OpenStreetMap
The world's leading map made from crowd-sourced, local knowledge. Sign up to start editing
your hometown, or get involved with humanitarian mapping.
• Scribble Maps
Like MS Paint for maps. Easy to make a simple map and export as an image.
• StoryMap JS
From the creators of the popular TimelineJS, present a timeline on a map.
Programming libraries for creating web maps:
• D3
A Javascript library following modern web standards. Use to create interactive, data driven web
maps and charts.
• Leaflet
A JavaScript library for creating web maps.

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GIS Software in the Market
 ArcGIS for Desktop 10.6.1
 ArcGIS Pro 2.2.0
 ArcGIS Maps for Office (Excel, PowerPoint)
 GeoDa
 QGIS
 Google Earth Pro
 Adobe Creative Suite 5 (Illustrator, Photoshop, InDesign)
 R & R Studio
 Stata
Major Geoprocessing Tools for GIS
1. The Buffer Tool
Buffers are proximity functions. When you use this geoprocessing tool, it creates a polygon at
a set distance surrounding the features.For example, a buffer is a polygon or collection of cells
that are within a specified proximity of a set of features. The buffer tool can have fixed and
variable distances. Also, they can be set to geodesic which accounts for the curvature of the
Earth.
2. The Clip Tool

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The clip tool is an overlay function that cuts out an input layer with the extent of a defined
feature boundary. The result of this tool is a new clipped output layer. If you can picture a
cookie cutter, this is like using the clip tool. And carving out vectors and rasters is one of the
most common operations in GIS. To clip data, you need points, lines, or polygons as input and
a polygon as the clipping extent. The preserved data is the result of a clip.
3. The Merge Tool
The merge tool combines data sets that are the same data type (points, lines, or polygons).
When you run the merge tool, the resulting data will be merged into one. Similar to the clip
tool, we use the merge tool regularly. For merging, data sets have to be the same type. For
example, you can’t merge points and polygons into one data set.
4. The Dissolve Tool
The dissolve tool unifies boundaries based on common attribute values. In other words,
dissolve merges neighboring boundaries if the neighbors have the same attributes. For
example, if you want to remove the borders of countries to form a continent, the dissolve tool
is the tool to use. But you would need an attribute for each country and the continent it belongs
to.

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5. The Intersect Tool
The intersect tool is very similar to the clip tool because the extents of input features define
the output. The only exception is that it preserves attributes from all the data sets that overlap
each other in the output. The intersect tool performs a geometric overlap. All features that
overlap in all layers will be part of the output feature class – attributes preserved. Add multiple
inputs. The tool accepts different data types (points, lines, and polygons). When features
overlap each other, they will be in the output. The intersect tool preserves the attribute values
in both input layers.
Pro Tip: Run the Intersect Tool on a single feature and you can find overlaps.
6. The Union Tool
Some say the union tool should come with a bottle of antacid. The union tool gets a bad
reputation because it creates a lot of features. The union tool maintains all input features
boundaries and attributes in the output feature class. After running this geoprocessing tool, it
does get a bit messy especially when there are more overlaps. But it’s really not so bad. The
Union tool spatially combines two data layers. It preserves features from both layers to the
same extent.

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7. The Erase (Difference) Tool
The erase tool because is always helpful to erase things! The input layer is what will be erased.
The erase feature determines what to erase. Simple as that. The Erase Tool removes features
that overlap the erase features. This geoprocessing tool maintains portions of input features
falling outside the erase features extent. The result is a new feature with the erase feature extent
removed.

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PRACTICAL II: IMPORT AND EXPORT DATA GIS SOFTWARE TO
THE AUTO-CAD OR REVIT SOFTWARE AND MENTION ALL THE
NECESSARY STEPS USED
Import and export GIS data betweendrawing objects and external databases in AutoCADMAP 3
In “Importing GIS data," you convert GIS data into AutoCAD objects with a rich set ofattributes attached. It
is not necessaryto retain the data file after importing.
1. Use map import to select trail data from an ESRIA shape file.
2. In the Import dialog, specify layer names and the import coordinate system.
3. Use Attribute Data to add object data and import.
4. In the drawing, browse the imported objects and properties.
In “Exporting GIS data," you share GIS data with other systems, which completes the design
cycle.
1. In the drawing, select street lines and use properties to browse object data.
2. Use mapdwgtosdf and specify layers and features to export.
3. In the Export Location dialog, specify columns for object data and export.
NECESSARY STEPS
STEP1: Use the Add Data button to add each of the datasets you wish to convert to the map
document.
STEP2: In order for your data layers to display properly in AutoCAD, they will all need to
be projected into the appropriate coordinate system. (Unsure of what the appropriate
projection is for your area of interest? Refer to this help document, or ask a staff member for
assistance in helping you determine it.) You can find out what coordinate system each layer
is associated with by right-clicking on the layer name in the Table of Contents, selecting
Properties… and clicking on the Source tab in the Layer Properties window.

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STEP3: If your datasets are correctly projected, skip to step 5. If any of your layers need to be
projected, open the ArcToolbox window by clicking on the red toolbox.
Expand Data Management Tools, then Projections and Transformations, then Feature, then
double-click on Project.
STEP4: In the Input Dataset or Feature Class box, use the dropdown menu to select the dataset
you are looking to project. (If it has not yet been loaded into the map document, click on the folder
icon to browse to the appropriate folder and select it.) The software should auto-detect the
coordinate system of this dataset and list it in the Input Coordinate System box.
In the Output Dataset or Feature Class box, browse to the location where you would like to save
the projected dataset and specify the filename. It is a good idea to indicate that these are the
projected versions of other datasets, e.g. by adding “_projected” to the end of each filename.
Click the box next to the Output Coordinate System box to select the projection that you would
like to assign to the dataset. (Tip: if another one of your layers already has the correct projection
selected, you can use the Import tool to find the right projection faster.) If the transformation
involves a change in datum, you will be prompted to select a geographic transformation in the

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following box – a list of applicable transformations will be found in the Geographic
Transformation dropdown menu. Click OK to project your dataset.
STEP5: When all of your datasets are projected into the same coordinate system, start a new map
document by going to File > New. Add all of the newly projected datasets to the map. Right-click
on the name of one of them in the Table of Contents and go
to Data > Export to CAD

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STEP6: In the Input Features list, you can add datasets to be included as individual layers in the
exported .dwg file by selecting them via the dropdown menu or by dragging them into the box
below from the Table of Contents. Ensure that the file type specified in the Output Type dropdown

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menu is compatible with the software installed on the machine you are working on (just in case an
older version of AutoCAD is installed) if you want to check that your export worked correctly
before taking your files with you.

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PRACTICAL III: GEO-REFERENCE AND GEO-TAG USING GOOGLE
EARTH/ BASE MAP.
To learn to use the Georeferencertool in ArcGIS Pro
ArcGIS Pro does not provide the functionality to directly use Google Earth images as basemaps,
and it is not available in the provided default list of basemaps. This article provides a
workaround to use a Google Earth image as a basemap in ArcGIS Pro
Procedure
1. In Google Earth, zoom in to the desired map extent.
2. Add at least three placemarks within the extent as ground control points, and note the
latitude and longitude coordinates of eachplacemark.
3. Save the Google Earth map as an image. To do this, refer to Google Earth Support: Save
your favorite map image. The image below shows a Google Earth image with four
placemarks created as ground control points.
4. In a new, blank ArcGIS Proproject, set the coordinate system to WGS 1984.

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a) In the Contents pane, right-click Map, and click Properties.
b) In the Map Properties dialog box, select Coordinate Systems.
c) Under XY Coordinate Systems available, click Geographic coordinate system >
World > WGS 1984.
5. Add the Google Earth image to the project, and georeference the image by entering the
latitude and longitude coordinates of all the ground control points. Refer to ArcGIS Pro:
Georeferencing a raster entering x,y coordinates for steps to do this. Click Apply to apply
the georeference before saving. The image below shows the georeferenced Google Earth
image in ArcGIS Pro.
6. To use the Google Earth image as a basemap and display other operational layers on top
of it, move the operational layers above the Google Earth image in the Contents pane.

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To learn to use the Georeferencer tool in QGIS
Step1: Scan the map
The first task you will have to do is to scan your map. If your map is too big, then you can scan
it in different parts but keep in mind that you will have to repeat preprocessing and
georeferencing tasks for eachpart. So possibly, scan the map in as few parts as possible.
If your map has colors, scan the image in color so that you can later use those colors to separate
information from your map into different layers (for ex., forest stands, contour lines, roads...).
Step2: Follow Along: Georeferencing the scanned map
Open QGIS and set the project’s CRS to ETRS89 / ETRS-TM35FIN in Project ‣ Project Properties ‣ CRS, which is the currently
used CRS. Make sure that Enable ‘on the fly’ CRS transformation is checked, since we will be working with old data that is another
CRS

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Save the QGIS project as map_digitizing.qgs.
You will use the georeferencing plugin from QGIS, the plugin is already installed in QGIS.
Activate the plugin using the plugin manager. The plugin is named Georeferencer GDAL.
Step3: To georeference the map:
Open the georeference tool, Raster ‣ Georeferencer ‣ Georeferencer.
Add the map image file, xyz_map.tif, as the image to georeferenciate, File ‣ Open raster.
When prompted find and select the KKJ / State
Click OK.
Next you should define the transformation settings for geo referencing the map:
Open Settings ‣ Transformation settings.
Click the icon next to the Output raster box, go to the folder and create the folder

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exercise_dataforestrydigitizing and name the file as xyz_georef.tif.
Set the rest of parameters as shown below.

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Click OK.
The map contains several cross-hairs marking the coordinates in the map, we will use those to
georeferenciate this image. You can use the zooming and panning tools as you usually do in
QGIS to inspect the image in the Georeferencer’s window.
Zoom in to the left lower corner of the map and note that there is a cross-hair with a coordinate
pair, x and y, that as mentioned before are in KKJ / Finland zone 2 CRS. You will use this point
as the first ground control point for the georeferencing your map.
Select the Add point tool and click in the intersection of the cross-hairs (pan and zoom as
needed).
In the Enter map coordinates dialogue write the coordinates that appear in the map (X: 2557000
and Y: 6786000).
Click OK.
The first coordinate for the georeferencing is now ready.
Look for other cross-hairs in the black lines image, they are separated 1000 meters from each
other both in North and East direction. You should be able to calculate the coordinates of those
points in relation to the first one.
Zoom out in the image and move to the right until you find other cross-hair, and estimate how
many kilometres you have moved. Try to get ground control points as far from each other as
possible. Digitize at least three more ground control points in the same way you did the first one.
You should end up with something similar to this:

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With already three digitized ground control points you will be able to see the georeferencing error
as a red line coming out of the points. The error in pixels can be seen also in the GCP table in the
dX[pixels] and dY[pixels] columns. The error in pixels should not be higher than 10 pixels, if it is
you should review the points you have digitized and the coordinates you have entered to find what
the problem is. Once you are happy with your control points save your ground control points, in
case that you will need them later, and you will:
File ‣ Save GCP points as....
In the folder exercise_dataCivildigitizing, name the file xyz_map.tif.points.
Finally, georeference you map:
File ‣ Start georeferencing.
Note that you named the file already as xyz_georef.tif when you edited the Georeferencer settings.

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Now you can see the map in QGIS project as a georeferenced raster. Note that the raster seems to
be slightly rotated, but that is simply because the data is KKJ / State
To check that your data is properly georeferenced you can open the aerial image in the
exercise_dataCivil folder, named xyz_aerial.tif. Your map and this image should match quite
well. Set the map transparency to 50% and compare it to the aerial image.

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PRACTICAL IV: DIGITIZE THE GIVEN PART OF TOPOSHEET USING
SOFTWARE & ATTRIBUTE (NAME, AREA, LENGTH, AS PER
REQUIREMENTS).
Digitization in QGIS – Exploring tools for Digitizing. Digitization is one of the important tasks
for a GIS specialist. Digitization is a process of converting raster data to vector data. For this task
QGIS provides many tools for efficient digitization. Digitization (or vectorization) should be clean
and a copy of the raster data so that the information of the map does not change.
 Check if Digitizing Toolbox is activated
Explore tools provided by QGIS for efficient digitization. First check your digitizing tool box
is activated, if not then you can activate it using following process.
Click view tab in the menu bar and click toolbar and then check digitizing and advance
digitizing tool box.

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 Create Layer:
Now add some layers for drawing. Click layer in the menu bar, select create layer and select new
spatialite layer.
You can select new shapefile layer if you have to digitize a single feature like some places or roads
or buildings. Select new spatialite layer because will be drawing more than one feature in single
file and it is easyto transfer this file.
Click ‘…’ browse button and save your database. Give name to your layer, select type of layer
and specify attributes and their type such as text or numerals and click add attributes to the list
and click OK. Specify CRS of the layer same as the CRS of Raster data

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 Snapping Tool :
Snapping is an operation to move, match and coincide exactly between two features.
To set snapping option, Click setting in the menu bar and click options then select digitizing and
in the box of snapping select default snapping mode, snapping tolerance and search radius for
vector edits.
 Draw Create and Edit Features:
a.) lets draw some feature. If there are more than one layer then select the layer which you
want to edit and Click toggle editing in digitization toolbox. As you click a pencil will
appear with the name of the layer which you are editing.
b.) Click on add feature tool in digitization tool box.

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c.) After drawing your feature right click to stop drawing, as you stop a pop up will ask you
the value for the attributes, give appropriate value to the attributes and click OK. Click
on save layer edits to save your edits.
 Move Feature:
You canmove your feature completely by selecting the tool image provided below.
 Delete or Join Node feature:
To Delete any node or you want to Join two nodes then you can use node tool as shown in
the image below. Click on the node tool in the digitization tool box.
Now select the feature you want to edit.
All the vertex (nodes) are highlighted with red color. Select the vertex or node you want
to delete. Simply press the delete button.
To join two nodes select a node and drag the node to the other node and it will snap to the
other node. If snapping was not set it would never snap to the feature properly. You can also snap
to any segment of line also.

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PRACTICAL V: GENERATION OF THEMATIC MAPS (CONTOUR,
DRAINAGE, ROAD ETC.) IN SOFTWARE.
A thematic map is a type of map that portrays the geographic pattern of a particular subject matter
(theme) in a geographic area. This usually involves the use of map symbols to visualize selected
properties of geographic features that are not naturally visible, such as temperature, language, or
population. In this, they contrast with general reference maps, which focus on the location (more
than the properties) of a diverse set of physical features, such as rivers, roads, and
buildings. Alternative names have been suggested for this class, such as special-subject or special-
purpose maps, statistical maps, or distribution maps, but these have generally fallen out of
common usage. Thematic mapping is closely allied with the field of Geovisualization.
A thematic map is a specialized map made to visualize a particular subject or theme about
a geographic area. Thematic maps can portray physical, social, political, cultural, economic,
sociological, or any other aspects of a city, state, region, nation, continent, or the entire globe. A
thematic map is designed to serve a special purpose or to illustrate a particular subject, in contrast
to a general map, on which a variety of phenomena appear together, such as landforms, lines of
transportation, settlements, and political boundaries. This is in direct contrast to a reference map
or Topographic map, which are designed to show the location of visible features of the landscape
with minimal interpretation and intended to be used for a wide variety of purposes. Thematic maps
also portray basic features such as coastlines, boundaries and places, but they are only used as a
point of locational reference for the phenomenon being mapped.
Thematic Map Design
The primary purpose of a thematic map is to visually portray a non-visual phenomenon, usually
the attributes of geographic features (e.g., the median income of a county). A good thematic map
clearly shows geographic patterns that mirror patterns in the real-world phenomenon. For example,
if a map reader looks at a map of income distribution, he or she should be able to quickly and
intuitively identify geographic concentrations of wealth and poverty that would be the same as
those seenin the field. Aesthetics is also an important goal: potential map readers are more likely

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to look at an attractive map and to spend enough time reading it to understand the patterns in the
phenomena being represented.
A thematic map will typically consist of three types of information:
Primary theme: the geographic phenomena that represent the topic being discussed. In a map of
population density of a city, this would be population density. Most thematic maps have a single
primary theme. Multivariate maps are also possible but are typically more difficult to design well.
Supporting theme: a layer of information that helps to tell the story, such as those that offer possible
explanations for the patterns found in the primary theme. For a city population density map, this
could be population attractors such as shopping districts or highways, or exclusionary features
such as water bodies or mountains.
Reference theme: a layer of geographic features that usually have little to do with the theme of the
map, but help map readers locate the thematic information in a context of recognizable geography.
Roads, administrative boundaries, terrain, and latitude/longitude graticules are common reference
layers
Isarithmic or isoline (Contour line)
Isarithmic maps, also known as contour maps or isoline maps, depict continuous
quantitative fields (sometimes conceptualized as "statistical surfaces" by cartographers), such as
precipitation or elevation by partitioning space into regions, each containing a consistent range of
values of the field. The boundary of each region, an isoline, thus represents the set of locations of
constant value. For example, on a topographic map, each contour line indicates an area at the listed
elevation
1. On the Create ribbon, click Themes.
2. Choose a set from the drop-down menu.
3. Choose a field from the drop-down menu.
4. Check Use Label check box (optional).
The Use Label displays labels for the geography boundaries.
5. Define the label options:
o Label Color-adjusts the font color by selecting a color from the color palette or entering a
Hex color code

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o Label Font Size-adjusts the font size of the label
6. Check the Show Lines check box (optional).
The Show Lines displays the geography outlines on the selected thematic map. Showing the lines
allows you to optimally visualize the thematic color polygons.
7. Check the Color Areas check box (optional).
The Color Areas displays the geography based on the thematic breaks. When this option is
unchecked, the thematic fill is no longer visible on the map. For the thematic map legend to display,
the color areas option needs to be enabled.
8. Check the Theme by Percentage check box (optional).
The Use Percent check box displays the selected variable as a percent on the thematic map.
The Use Percent check box is disabled when the Universe set is selected.
9. Click Update.
The thematic map is applied to the map. The color ramp is displayed and distinguishes the value
range for each color of the thematic map. Each geography on the map is labeled with the Set,
Fields, and any other options you have defined.

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PRACTICAL VI: Visual image interpretation from aerial photos and/or
satellite images.
Aerial photographic and satellite image interpretation, or just image interpretation when in
context, is the act of examining photographic images, particularly airborne and space borne, for
the purpose of identifying objects and judging their significance. This is commonly used in
military aerial reconnaissance, using photographs taken from reconnaissance
aircraft and reconnaissance satellites.
The principles of image interpretation have been developed empirically for more than 150 years.
The most basic are the elements of image interpretation: location, size, shape, shadow, tone/color,
texture, pattern, height/depth and site/situation/association. They are routinely used when
interpreting aerial photos and analyzing photo-like images. An experienced image interpreter uses
many of these elements intuitively. However, a beginner may not only have to consciously
evaluate an unknown object according to these elements, but also analyze each element's
significance in relation to the image's other objects and phenomena.
Visual Image Interpretation With the power of the human visual system, much information in
remote sensor data can be acquired simply by visual inspection. Examples include the spatial
extent of a lake, the location of roads, and the number of houses in a community. These are all
variables that can be "seen" on the terrain and interpreted directly by visualizing the imagery. In
these cases a trained image analyst uses a combination of real-world experience and heuristic rules-
of-thumb to interpret what is seen in the image and to determine its significance. The process of
image interpretation can be broken down into its fundamental elements, including: • absolute
location (coordinates) • relative location • size • shape • shadow • tone/color • texture • pattern • 3-
dimensional characteristics • Color Composites White light from the Sun is composed of EMR
from all wavelengths within the visible spectrum. We can see this clearly when white light passes
through a prism and separates into a rainbow. Combining these colors of the rainbow back together
yields white light. Adding only some portions of the rainbow light will result in a different color.
47 One can create any color by mixing the three primary ones — red, green, and blue (additive
color theory). Each pixel in a computer screen is actually made using three different light "guns,"
one for each of these primary colors. These guns respond to commands by the computer to display

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with various intensities. The addition of the EMR emitted by these three guns determines what
color the user perceives. The initial visualization of remote sensor data is an important aspect of
an effective interpretation effort. Digital remote sensor data is displayed by assigning recorded
brightness values to the three colour guns mentioned above. When the red, green, and blue bands
in the visible spectrum are assigned to their respective red, green, and blue color guns, the
displayed result is said to be a true color composite. However, remote sensor systems often
measure EMR outside the visible range, requiring the creation of false color composites. For
example, near-infrared bands are often displayed using the red color gun. When looking at a false
color composite image, special care needs to be taken to interpret it correctly.
Visualization GIS can provide hardcopy maps, statistical summaries, modeling solutions and
graphical display of maps for both spatial and tabular data. For many types of geographic operation
the end result is best visualized as a map or graph. Maps are very efficient at storing and
communicating geographic information. GIS provides new and exciting tools to extend the art of
visualization of output information to the users.
Elements of Visual Interpretation
As we noted in the previous section, analysis of remote sensing imagery involves the identification
of various targets in an image, and those targets may be environmental or artificial features which
consist of points, lines, or areas. Targets may be defined in terms of the way they reflect or emit
radiation. This radiation is measured and recorded by a sensor, and ultimately is depicted as an
image product such as an air photo or a satellite image.
What makes interpretation of imagery more difficult than the everyday visual interpretation of our
surroundings? For one, we lose our sense of depth when viewing a two-dimensional image, unless
we can view it stereoscopically so as to simulate the third dimension of height. Indeed,
interpretation benefits greatly in many applications when images are viewed in stereo, as
visualization (and therefore, recognition) of targets is enhanced dramatically. Viewing objects
from directly above also provides a very different perspective than what we are familiar with.
Combining an unfamiliar perspective with a very different scale and lack of recognizable detail
can make even the most familiar object unrecognizable in an image. Finally, we are used to seeing
only the visible wavelengths, and the imaging of wavelengths outside of this window is more
difficult for us to comprehend.
Recognizing targets is the key to interpretation and information extraction. Observing the

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differences between targets and their backgrounds involves comparing different targets based on
any, or all, of the visual elements of tone,shape,size, pattern, texture,shadow, and association.
Visual interpretation using these elements is often a part of our daily lives, whether we are
conscious of it or not. Examining satellite images on the weather report, or following high speed
chases by views from a helicopter are all familiar examples of visual image interpretation.
Identifying targets in remotely sensed images based on these visual elements allows us to further
interpret and analyze. The nature of each of these interpretation elements is described below, along
with an image example of each.
Tone refers to the relative brightness or colour of objects in an image. Generally, tone is the
fundamental element for distinguishing between different targets or features. Variations in tone
also allow the elements of shape, texture, and pattern of objects to be distinguished.

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Shape refers to the general form, structure, or outline of individual objects. Shape can be a very
distinctive clue for interpretation. Straight edge shapes typically represent urban or agricultural
(field) targets, while natural features, such as forest edges, are generally more irregular in shape,
except where man has created a road or clear cuts. Farm or crop land irrigated by rotating sprinkler
systems would appear as circular shapes.

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Size of objects in an image is a function of scale. It is important to assess the size of a target relative
to other objects in a scene, as well as the absolute size, to aid in the interpretation of that target. A
quick approximation of target size can direct interpretation to an appropriate result more quickly.
For example, if an interpreter had to distinguish zones of land use, and had identified an area with
a number of buildings in it, large buildings such as factories or warehouses would suggest
commercial property, whereas small buildings would indicate residential use.
Pattern refers to the spatial arrangement of visibly discernible objects. Typically an orderly
repetition of similar tones and textures will produce a distinctive and ultimately recognizable
pattern. Orchards with evenly spaced trees, and urban streets with regularly spaced houses are
good examples of pattern.
Texture refers to the arrangement and frequency of tonal variation in particular areas of an image.
Rough textures would consist of a mottled tone where the grey levels change abruptly in a small
area, whereas smooth textures would have very little tonal variation. Smooth textures are most
often the result of uniform, even surfaces, such as fields, asphalt, or grasslands. A target with a
rough surface and irregular structure, such as a forest canopy, results in a rough textured
appearance. Texture is one of the most important elements for distinguishing features in radar
imagery.

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Shadow is also helpful in interpretation as it may provide an idea of the profile and relative height
of a target or targets which may make identification easier. However, shadows can also reduce or
eliminate interpretation in their area of influence, since targets within shadows are much less (or
not at all) discernible from their surroundings. Shadow is also useful for enhancing or identifying
topography and landforms, particularly in radar imagery.
Association takes into account the relationship between other recognizable objects or features in
proximity to the target of interest. The identification of features that one would expect to associate
with other features may provide information to facilitate identification. In the example given
above, commercial properties may be associated with proximity to major transportation routes,
whereas residential areas would be associated with schools, playgrounds, and sports fields. In our
example, a lake is associated with boats, a marina, and adjacent recreational land.

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PRACTICAL VII: Explore utilization of RS and GIS for development of smart
city.
A geographic information system (GIS) has been used in the construction of a large-scale model
of a smart city project in France.
The smart city concept is developing very quickly around the world, because it provides a
comprehensive digital environment that improves the efficiency and security of urban systems and
reinforces the involvement of citizens in urban development. This concept is based on the use of
geospatial data concerning the urban built environment, the natural environment and urban
services. The successful implementation of a smart city project requires the development of a
digital system that can manage and visualise the geospatial data in a user-friendly environment.
The geographic information system (GIS) offers advanced and user-friendly capabilities for smart
city projects. This article shows how a GIS could help in the implementation of smart city projects
and describes its use in the construction of a large-scale model of the smart city.
The ‘smart city’ concept aims at developing a comprehensive system that uses geospatial data to
enhance the understanding of complex urban systems and to improve the efficiency and security
of these systems. This geospatial data concerns (i) the urban built environment such as
infrastructure, buildings and public spaces, (ii) the natural environment such as biodiversity, green
spaces, air quality, soil and water, and (iii) urban services such as transport, municipal waste,
water, energy, health and education. The smart city concept also aims at transforming the ‘silo -
based’ management of cities into a ‘shared’ system that involves urban stakeholders in the design,
realization and evaluation of urban projects.

JSPM’s
The emergent technology enables cities to achieve more agile management that improves the
quality of life for citizens, enhances the economic development, improves the attractiveness of the
city and reinforces the involvement of citizens in the city government. Indeed, the smart city
concept provides the city managers with pertinent information about the performance of urban
infrastructure and services, as well as users’ feedback. Analysis of this data allows policymakers
and city managers to improve the efficiency of the urban system as well as the quality of urban
services. This concept is particularly pertinent for the security and resilience of the city. It allows
collection of data concerning how the city infrastructure and stakeholders respond to urban
hazards. Analysis of this data provides greater understanding of the behavior of urban systems
(infrastructure, public services, emergency response, etc.) during urban crises or disasters, and
consequently enables improvements to the city’s capacity to address the resiliency challenges. The
smart city concept offers the possibility to confine a local fault and to prevent its spread to larger
areas.
Use of GIS in smart city projects
The implementation of smart city projects is based on a number of steps (Figure 1) including the
construction of the urban digital model, data collection using the sensing layer, then data analysis,
interactive data visualization and system control. GIS plays a role in these steps, as described
below.
Construction of the urban digital model
The first step in the implementation of smart city projects concerns the construction of the urban
digital model that describes the components of the urban built and natural environments. For each
urban component, the digital model provides the geolocalisation and characteristics (attributes).
GIS is generally used for the construction of the digital model of urban ‘horizontal components’
such as urban networks, transport facilities and natural environment, while building information
modelling (BIM) is used for the description of ‘vertical components’ such as buildings. The
combination of GIS and BIM provides a powerful tool for the construction of the urban digital
model with georeferenced data and the visualization of this data in a user-friendly environment.

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Sensing layer
The second step in smart city projects concerns the construction of the sensing layer that transfers
urban operating data to the smart city information system. This layer includes sensors used for
monitoring urban networks and infrastructures. Data could also be enhanced by images, videos
and audio files resulting in the construction of urban big data. Figure 2 shows examples of sensors
used in monitoring water and energy utilities. The drinking water system uses automatic meter
readers (AMRs) to record water consumption, pressure sensors to record water pressure and water
quality devices to track the water quality (turbidity, pH, chlorine, conductivity). The drainage
system uses sensors to monitor the water level and flow, water quality (turbidity, temperature, pH,
etc.) and pumping equipment. It allows early detection of flood and faults in pumping equipment.
The electrical grid uses sensors to measure the electrical tension, current and frequency. It allows
early detection of faults in the electrical grid. The district heating system is monitored by sensors
to record fluid temperature, pressure and flow as well as the state of the valve. It allows early fault
detection and the improvement of the system performance. GIS offers the possibility to visualize
the monitoring system as well as the sensors’ characteristics and status. It also provides the
possibility to visualize real-time and historical data on GIS maps.

JSPM’s
Data analysis
The third step in implementing a smart city project concerns the development of the analytic
environment, which converts real-time and historical data into operational data that improves the
security, efficiency and quality of urban systems. The analytic environment includes engineering,
management and safety software for urban systems as well as advanced digital tools such as
artificial intelligence (AI). In smart city projects, GIS provides tools for (i) geospatial data analysis
(distance and directional analysis, geometrical processing, grid models), (ii) spatiotemporal
analysis, (iii) spatial statistics (spatial autocorrelation and egression), (iv) surface analysis (surface
form and flow analysis, gridding and interpolation methods) and, (v) location analysis (shortest
path calculation, facility location).
Interactive data visualization
Interactive data visualization allows users to interact with the smart city’s components and the
stakeholders in a user-friendly environment. Web applications are used to create this interactive
environment. The use of HTML popups enables users to access web-based content such as
graphics referenced by URLs. The interactive GIS graphic environment allows the visualizat ion
of urban components and sensors maps. Users and managers can utilize these maps to access static
and dynamic data concerning urban systems as well as to update the data.

JSPM’s
Control layer
Data analysis of historical and real-time data results in commands for the optimal and safe
management of urban systems. These commands are transmitted to the control layer, which
includes different electronic devices such as smart valves, pumps, motors, switches, breakers and
locks. The GIS system allows real-time visualization of these devices as well as their status. It
could also visualise faults in device command.

GIS FINAL.pdf

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