Geoinformatics is the science and technology dealing with geographic information and related data. It involves acquiring, storing, processing, analyzing and disseminating geospatial data. Key branches of geoinformatics include remote sensing, geographic information systems, cartography, global navigation satellite systems, photogrammetry and database management systems. Remote sensing uses sensors on platforms like satellites and aircraft to collect imagery and geospatial data of the Earth without direct contact.

1. What Is Geoinformatics:
Geoinformatics is the science and the technology
which develops and uses information science,
infrastructure to address the problems of
geography, geosciences and related branches of
engineering.
“The art, science or technology dealing with
acquisition, storage, processing, production,
presentation and dissemination of geoinformation“

2. Branches of geoinformatics
include
1. Remote Sensing
2. Geographic Information Systems (GIS)
3. Cartography
4. Global Navigation Satellite
Systems(GPS)
5. Photogrammetry
6. DBMS- Data Base Management System

3. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
2/11/2016
1
Remote Sensing Overview
&
Earth Observation Data for
Urban Planning

4. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
 REMOTE SENSING (Areal and Space based)
‐ Range of satellite data available: IRS series (Resourcesat, Cartosat,
RISAT, etc.), Geoeye, Quickbird, Worldview, IKONOS…
 PHOTOGRAMMETRY
‐ Areal
‐ Digital (Cartosat-1, Pleiades, ALOS PALSAR, SPOT...)
‐ Close range Photogrammetry
 LiDAR-Terrestrial Laser Scanner
 GEOGRAPHICAL INFORMATION SYSTEM (GIS)
‐ Facilitates data generation, integration and analysis
 GLOBAL POSITIONING SYSTEM (GPS)
‐ Facilitates geo-referencing, asset mapping..
 UNMANNED AERIAL VEHICLE (UAV)- mapping of urban areas
 GROUND PENETRATING RADAR (GPR)- underground utilities..
 …
Innovative Technologies for Urban Planning

5. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Remote Sensing
Science and
obtaining
art of
information
about an object, area, or
phenomenon through the
analysis of data acquired
by a device that is not in
contact.
-Lillesand and Kiefer (1994)

6. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Brief history of Remote Sensing
1909 Wilbur Wright and a motion picture
photographer are first to use an aircraft
as a platform - over Centocelli, Italy
1950s “Remote sensing” coined by Evelyn
Pruitt in mid-1950s
1972 ERTS (Landsat) 1 launched
1970-80Rapid advances in digital image
processing due to powerful micro-
processor based computers available
1988 Indian Remote Sensing Satellite (IRS)
launched.
…….

7. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Sun
Satellite
Ground Station
Data Archival
Data Products
Interpretation & Analysis Final Product
Remote Sensing Process
A
B
C
D
E
F G

8. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Remote Sensing Process
Energy Source or Illumination (A)
 Illuminates or provides electromagnetic energy to target
of interest.
Radiation and the Atmosphere (B)
 As energy travels from its source to target, it will come
in contact with and interact with atmosphere it passes
through. This interaction may take place a second time
as energy travels from target to sensor.

9. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Satellite
D
Remote Sensing Process
Earth
C

10. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Interaction with the Target (C)
 Once energy makes its way to target through
atmosphere, it interacts with target depending on
properties of both target and the radiation.
Recording of Energy by the Sensor (D)
 After energy has been scattered by, or emitted from
target, we require a sensor (remote - not in contact with
the target) to collect and record electromagnetic
radiation.
Remote Sensing Process

11. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Ground Station
Data Archival
Data Products
Interpretation & Analysis
E
F
Remote Sensing Process

12. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Transmission, Reception, and Processing (E)
 Energy recorded by sensor has to be transmitted, often in
electronic form, to a receiving and processing station
where data are processed into an image (hardcopy and/or
digital form).
Interpretation and Analysis (F)
 Processed image is interpreted, visually and/or digitally or
electronically, to extract information about target which
was illuminated.
Remote Sensing Process

13. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Final Product
G
Remote Sensing Process

14. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Platforms and Sensors
from <1 m
to 36,000 km height
PLATFORMS
Stage to mount camera or sensor to acquire information about
a target under investigation. Based on its altitude above earth
Ground borne, ii) Airsurface, platforms may be classified a i)
borne and iii) Space borne.
Ground-based platforms
Mainly used for collecting ground truth or for laboratory
simulation studies.
Air-borne platforms
Used to acquire aerial photographs for photo-interpretation
and Photogrammetry purposes. Scanners are tested against
their utility and performance from these platforms before
these are flown onboard satellite missions.
Space-borne platforms
Platforms in space are not affected by the earth's atmosphere.
These platforms are freely moving in their orbits around the
earth, and entire earth or any part of the earth can be covered
at specified intervals. The coverage mainly depends on the
orbit of the satellite.

15. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Orbits
Geostationary satellites
An equatorial west to east
satellite orbiting the earth
at an altitude of 36000
km, the altitude at which
it makes one revolution in
24 hours, synchronous
with the earth's rotation.
These are mainly used for
communication and
meteorological appl. viz.,
GOES, METEOSAT,
INTELSAT, INSATsatellites,
etc.
36,000 km
~800 km
1. Geostationary
2. Polar orbiting or Sun-synchronous
Sun-Synchronous satellites
An earth satellite orbit in
which the orbital plane is
near polar and the
altitude is such that the
satellite passes over all
places on earth having the
same latitude twice in
at the sameeach orbit
local sun-time. LANDSAT
SPOT series, IRS
NOAA, SEASAT,
series,
series,
TIROS, HCMM, SKYLAB,
SPACE SHUTTLE

16. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Passive Vs. Active Remote Sensing
 Most remote-sensing systems are
passive
 They use energy provided by the sun,
and Earth. e.g. Aerial photographs and
most satellite systems
 Used for earth resources mapping and
monitoring
 Some systems are active
 They generate their own energy e.g.
RADAR (radio detection and ranging),
LIDAR (light detection and ranging) and
SONAR(Sound navigation ranging)
 Used for altimetry and imaging.

17. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Advantages of remote sensing
1. Global
coverage
2. Synoptic
view
4.
Cost
3. Repeatability

18. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Limitations of Remote Sensing
Data acquisition
 Remote sensing instruments needs calibration
 Systematic and non-systematic errors
Data interpretation
 Need to have some knowledge of the phenomena being
studied
 Need to understand measurement uncertainties
Error correction (Human or process induced)

19. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Resolution
 Ability to distinguish objects which are spatially
near or spectrally similar.
 We often specify the resolution in terms of linear size of
smallest feature that we can discriminate (often expressed
in meters).
 Remote sensors measure differences and variations of
objects that are often described in terms of four main
resolutions, each of which affect the accuracy and
usefulness of remote sensors for urban mapping.
Resolution
Spatial Spectral Radiometric Temporal

20. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
 Spatial Resolution: higher spatial resolution desirable as most of
urban areas are densely built and features are comparatively small in
size.
 Minimum of four pixels within an object to identify (one-half the width of the
smallest dimension)
 Role of shape, size, texture, orientation, pattern, shadow, association, etc.
 Land use vs. land cover?
 Spectral Resolution: Multispectral data enhances ability to
discern features but interpreter’s intervention is must to
discriminate among various urban features.
 Hyperspectral data to distinguish urban features
 Temporal Resolution: e.g., land use transformations, urban
sprawl, change in socio-economic characteristics.
 Radiometric Resolution: enhances capability to distinguish
features and interpretation
Requirements for Urban Mapping

21. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
. EVERY 30 MIN IMAGING
. 1M+ SCALES
. CLIMATE/ WEATHER
Indian Remote Sensing Satellite Systems
Choice of Resolution

22. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
. EVERY 22 DAYS IMAGING
. 1:50K SCALES
. DETAILED RESOURCES
. LOCAL AREA IMAGING
. 1:2000/4000/8000 SCALES
. STEREO CAPABILITY
Choice of Resolution.. Contd.

23. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
2012
RISAT-1

24. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Spatial Resolution
Spatial resolution refers to the size of the smallest
object that can be resolved on the ground.
 HIGH SPATIAL RESOLUTION (0.6m - 4m)
‐ GeoEye-1, WorldView-1, WorldView-2, QuickBird,
IKONOS, FORMOSAT-2, ALOS, CARTOSAT-1, CARTOSAT-2,
2A, 2B, SPOT-5, IRS-P6 LISS-IV…
 MEDIUM SPATIAL RESOLUTION (4m - 30 m)
‐ ASTER, LANDSAT, Resourcesat…
 LOW SPATIAL RESOLUTION (30m - > 1000 m)
‐ SeaWiFS, GOES, Resourcesat AWiFS, Oceansat OCM, etc.

25. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
High Resolution Satellite Data
Satellite Sensor No. of Bands Spatial Resolution [m] Archive from
Resourcesat-1,2 LISS-IV 1 5.8 2011
CartoSat-1 ( Stereo) PAN 1 2.5 2005
Cartosat-2, 2A, 2B PAN 1 0.8 2007
IRS-1C, 1D PAN 1 5.8 1997
Pléiades 1A , 1B MS - HiRI 4 2.8 2012
Pléiades 1A, 1B (Stereo) PAN - HiRI 1 0.5 2012
SPOT-6 NAOMI MS 4 6 2013
SPOT-6 NAOMI PAN 1 1.5 2013
KOMPSAT-3 MS 4 2.8 2012
KOMPSAT-3 PAN 1 0.7 2012
WorldView-1 PAN 1 0.5 2007
WorldView-2 MS 8 1.84 2009
WorldView-2 PAN 1 0.5 2009
GeoEye-1 MS 4 2 2008
GeoEye-1 PAN 1 0.5 2008
ALOS (Stereo) PRISM 1 2.5 2006
EROS-B EROS B 1 0.7 2006
KOMPSAT-2 MS 4 4 2006
KOMPSAT-2 PAN 1 1 2006
FORMOSAT-2 PAN 1 2 2005
OrbView-3 MS 4 4 2003
OrbView-3 PAN 1 1 2003
QuickBird MS 4 2.4 2001
QuickBird PAN 1 0.6 2001
EROS-A EROS A 1 1.8 2000
IKONOS MS 4 4 1999
IKONOS PAN 1 1 1999

26. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Some medium-resolution satellites
Platform Orbit Sensor # of
bands
Spatial
Res.
Revisit
Landsat-4 & 5 TM 30 m 7 185 km 16 days
IRS-1C & 1D LISS-3 24 m 4 142 km 24 days
Landsat-7 ETM+ 15 m (PAN) 8 185 km 16 days
SPOT 1-3 HRV 10 m (PAN) 3 60 km 4-6 days
SPOT-4 HRVIR 10 m (PAN) 4 60 km 4-6 days
CBERS HRCC+ 20 m 9 120 km 3 days
Terra (EOS AM-1) ASTER 15 m 14 60 km 5 days

27. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Some low-resolution satellites
Platform Orbit Sensor Res. # of
bands
Swath Revisit
Meteosat GEO VISSR 2.5 km 3 ½Earth 30 min
NOAA Polar AVHRR 1 km 7 3000 km Daily
Resurs-O1 S-sync MSU-SK1 200 m 4 760 km 3-5 days
SeaStar S-sync SeaWiFS 1.1 km 8 2800 km Daily

28. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
spectral bands. The characteristic spectrum of the
target pixel is acquired in a hyperspectral image.
Example:- NASA's EO-1 satellite
[ Hyperspectral image of Benthic
habitats around Virgin islands, national
Park, St. John, U.S ]
 Panchromatic Image
B&W image (one band) ranging from 0.4-0.7 µ, visible
 Multispectral Image
Features detected using several discrete bands. Width of
these bands that spectral resolution refers too.
Example:- A multispectral IKONOS image consists of four
bands: Blue, Green, Red and Near Infrared, while a Landsat
TM multispectral image consists of seven bands: blue, green,
red, near-IR bands, two SWIR bands, and a thermal IR band.
 Hyperspectral Image
Consists of about a hundred or more continuous
Panchromatic
Multi
Spectral
Hyper Spectral
Spectral Resolution
ability to resolve spectral features and bands into separate components
Spectral
Resolution

29. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Electromagnetic Spectrum
Visible: 0.4-0.7; blue: 0.4-0.5 green: 0.5-0.6 red: 0.6-0.7

30. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Spectral Reflectance Curve

31. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Typical high resolution image

32. HY
I
P
N
E
D
R
IA
S
N
PE
IN
C
S
T
T
R
IT
A
U
L
T
R
E
E
O
M
F
O
RE
T
M
E
O
S
T
E
E
N
S
S
EN
IN
SI
G
NG
O
,
F
DE
U
H
R
R
B
A
A
D
N
UN
AREAS
• To develop a spectral library of urban features using ground based analytical spectral deviceand
signatures comparison with various remote sensingsensors
• Understanding the spectral separability of various urban features using continuum removalalgorithm
• Deployment of various image reconstruction and various sub-pixel classification techniques for urban
features mapping
Urban Material Suitable spectral configuration
Brick 1658:1718:1728 and 1335:1476:1486
Concrete roof 1123:1143:1153 and 1002:1073:1083
Road surface 688:699:709 and 658:719:729
Bare soil 688:719:729 and 2031:2041:2051
Sand 992:1083:1093
Samplepointslocation
Satelliteimages:(a)Hyperion(b)ALIand(c)IKONOS
UrbanfeaturesinIKONOSimagebeforeandafter
empiricallinecalibration
• Apart from the visible region, the SWIR region can also improve the urban mapping.
• ALI image covers SWIR band configuration and suitable for urban mapping. Though, spatial
resolution remains the limitation.
• IKONOS has large gap between green and red (595 nm-638 nm) and between 695-757 nm.
Distinction between urban features are prominent in these gap regions.
(Source:Sandhya,P.,2011.Revealingtheanatomyofurbanareasusingspectraldimension.UnpublishedM.Tech.thesis,IIRS,Dehradun)
0.2
0.7
0.6
0.5
0.4
0.3
1050 1250 1450 1650 1850 2050 2250
SWIR Spectral Library
Concrete roof Bare soil Sand

33. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Radiometric Resolution
 Radiometric resolution determines how fine the
sensor can distinguish between objects of similar
reflection. It refers to the smallest change in
intensity level that can be detected by sensing
system.
 Quantization refers to technique of representing
radiometric levels by a limited set of numbers.
For e.g., If a sensor of 8 bit is used to record the
data there would be 28=256 digital values
available, ranging from ‘0’ to ‘255’.

34. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
8-bit quantization
(256 levels)
6-bit quantization
(64 levels)
4-bit quantization
(16 levels)
3-bit quantization
(8 levels)
2-bit quantization
(4 levels)
1-bit quantization
(2 levels)
Example of Radiometric Resolution

35. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Temporal Resolution
Temporal resolution or Repetivity of satellite is the
capability to image same area at different time period.
Application of Temporal Resolution
 Temporal resolution also generally increases at higher latitudes due to
significant side lap between consecutive satellite passes.
 Persistent cloud offers limited clear views of the earth’s surface.
 Short lived phenomenon need to be imaged (e.g. flood, oil slicks etc.)
 Multi-temporal comparisons are required (e.g. urban sprawl)
 Repeat cycle is the time in days, between two successive identical
orbits.
 Revisit time, the time between two subsequent images of same area, is
determined by repeat cycle together with pointing capability of sensor.

36. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Stages of
Development:
Seen through
Satellite
Remote
Sensing

37. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Mapping Scale vs. Resolution
 Resolution is a function of latitude, zoom level, and
a constant.
 Scale is dependent on screen resolution or DPI.
Map scale = 1
(pixel ground size [m/pixel] × pixel density[pixels/m])
so that 1 m on the map represents x m on ground
Source:https://blogs.esri.com/esri/arcgis/2009/12/04/mathematical-relationships-among-map-scale-
raster-data-resolution-and-map-display-resolution/

38. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Levelof
Plan
ning
Macro-level(Regional
&Perspective)
Meso-level(District/
Development)
Micro-level(Project,Micro-watershed,
Village)
Lowresolution(80-360M) Mediumresolution(4m–30m) Highresolution(0.6m–4m)
Mappingscale 1:50,000to1:1M 1:10,000to1:50,000 1:1,000to1:5,000
UrbanPlanning Urbansprawlanalysis
Urbanlanduseatlevel-1
Transportationnetwork
(highways,railwaysetc.)
Urbanlandusemapping(upto
level-2)
Urbansuitabilityanalysis
Mappingofmajortransport
network
Updationofcityguidemaps
Urbanlandusemapping(uptolevel4)
Slumtypology
Mappingofstreetlevelurbanroadnetwork
Mappingofpropertyparcels
Inputsforinfrastructuredevelopment
Utilitiesandservicemaps
Populationestimation
Infrastru
cture
Planning
Regionallevelcorridor
planning
Broadsitesuitabilityanalysis
Mappingofmajorroadnetwork
Specificprojectsiteanalysis
Dams,highways,canal,industries,powerplants
Disaster Flood,cyclone,drought,
earthquakeproneareas,
landslidemapping,slope
stabilitymapping
Postdisasterdamage
assessment
Propertyinsurancefornatural
disasters
Postdisasterreliefmanagementsupport
Tracingofapproachroutes
Wastedisposalandsolidwastemanagement
Rural
Develop
ment
Planning
Regionalmaps
Settlementnetwork
Landandwaterresources
developmentmaps
Cadastrallevellandusemaps
Landparcelmaps
Microlevelwatershed/villageplanning
Planning Levels vs. Scale of Mapping

39. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
Metro - 13 towns
Class I – 70 towns
Class II – 15 towns
Class III – 19 towns
Class IV – 17 towns
Class V – 6 towns
Class VI – 12 towns
National Urban Information System (NUIS)
Scope: Large scale Urban Geospatial
database for various levels of Urban
Planning, Infrastructure development and
e-governance.
Primary Layers
1. Urban land use
2. Physiography
3. Geomorphology
4. Geological Structures
5. Lithology
6. Soil
7. Drainage
8. Surface Waterbodies
9. Road
10. Rail
11. Canal
12. Transportation Nodes
Incorporated Layers
1. Administrative Boundaries
2. Forest Boundary
3. Settlement & Village Locations
4. City / Town Boundaries
No. of Towns – 152 (~56 sq.km)
 Urban Geospatial database
for Urban Local Bodies
(ULBs) for Urban Planning,
Infrastructure development
and e-governance
 Bhuvan-NUIS based online
Geospatial solution for
Master Plan Preparation
Datasets for Urban Planning
Source:http://bhuvan.nrsc.gov.in/gis/thematic/index.php

40. S.No. City State S.No. City State S.No. City State
AAMBY VALLEY1
4 AHMEDABAD
MAHARASHTRA
GUJARAT
2
5
AGARTHALA TRIPURA
AHMEDNAGAR
3 AGRA
6
UTTAR PRADESH
MAHARASHTRA
AJMER
MAHARASHTRA
UTTARPRADESH
AHMEDPUR
ALWAR7
10 AMBALA
RAJASTHAN
HARYANA
8
11
AKBARPUR
AMRELI
9
12
RAJASTHAN
TAMIL NADU
ARIYALUR
GUJARAT
MAHARASHTRA
ARAKKONAM
AZAMGARH13
16 BAGALKOT
TAMIL NADU
KARNATAKA
14
17
AWALPUR
BAHARAMPUR
15
18
UTTARPRADESH
KARNATAKA
BANSWARA
WESTBENGAL
KARNATAKA
BANGALORE
BARAUT19
22 BARDHAMAN
RAJASTHAN
WESTBENGAL
20
23
BANTWAL
BARSHI
21
24
UTTARPRADESH
MAHARASHTRA
BEAWAR
MAHARASHTRA
KARNATAKA
BASMAT
BELLARY25
28 BHADRAK
RAJASTHAN
MAHARASHTRA
26
29
BELGAUM
BHANDARA
27
30
KARNATAKA
GUJARAT
BHATPARA
MAHARASHTRA
GUJARAT
BHARUCH
BHILWARA31
34 BHIMAVARAM
WEST BENGAL
ANDHRA PRADESH
32
35
BHAVNAGAR
BHOPAL
33
36
RAJASTHAN
ORISSA
BIDAR
MADHYA PRADESH
KARNATAKA
BHUBANESHWAR
BOTAD37
40 BRAHMAPUR
KARNATAKA
ORISSA
38
41
BIRURU
BRAHMAPURI
39
42
GUJARAT
UTTAR PRADESH
BULDHANA
MAHARASHTRA
RAJASTHAN
BULANDSHAHER
BURHANPUR43
46 CHAMRAJNAGAR
MAHARASHTRA
KARNATAKA
44
47
BUNDI
CHANDRAPUR
45
48
MADHYA PRADESH
TAMIL NADU
CHIKKANAYAKAHALLI
MAHARASHTRA
MADHYA PRADESH
CHENGALPATTU
CHITRADURGA49
52 CHITTAURGAH
KARNATAKA
RAJASTHAN
50
53
CHINDWARA
CUTTACK
51
54
KARNATAKA
UT: Daman and Diu
DANTEWADA
ORISSA
DELHI
DAMAN
DEORIA55
58 DHARMAVARAM
CHHATTISGARH
ANDHRA PRADESH
56
59
DELHI
DHAULPUR
57
60
UTTARPRADESH
MAHARASHTRA
DODDABALLAPUR
RAJASTHAN
GUJARAT
DHULE
ELURU61
64 ETAWAH
KARNATAKA
UTTARPRADESH
62
65
DOHAD
GADAG
63
66
ANDHRA PRADESH
MAHARASHTRA
GAJWEL
KARNATAKA
GUJARAT
GADCHANDUR
GANGANAGAR67
70 GANGAWATI
ANDHRA PRADESH
KARNATAKA
68
71
GANDHINAGAR
GAURIBIDANUR
69
72
RAJASTHAN
TAMIL NADU
GOKAK
KARNATAKA
GUJARAT
GINGEE
GONDIA73
76 GUDIVADA
KARNATAKA
ANDHRA PRADESH
74
77
GONDAL
GULBARGA
75
78
MAHARASHTRA
MADHYA PRADESH
HALVAD
KARNATAKA
UTTARPRADESH
GWALIOR
HARIDWAR79
82 HAZARIBAGH
GUJARAT
JHARKHAND
80
83
HARDOI
HINDUPUR
81
84
UTTARAKHAND
MAHARASHTRA
HOSPET
ANDHRA PRADESH
KARNATAKA
HINGANGHAT
HULIYAR85
88 HUMNABAD
KARNATAKA
KARNATAKA
86
89
HUBLI
HYDERABAD
87
90
KARNATAKA
MADHYA PRADESH
JAIPUR
ANDHRA PRADESH
RAJASTHAN
JABALPUR
JALALABAD91
94 JALGAON
RAJASTHAN
MAHARASHTRA
92
95
JAISALMER
JAMMU
93
96
PUNJAB
GUJARAT
JHANSI
JAMMU and KASHMIR
RAJASTHAN
JAMNAGAR
JODHPUR97
100 KADUR
UTTARPRADESH
KARNATAKA
98 JHUNJHUNU
101 KAKINADA
99
102
RAJASTHAN
GUJARAT
KANCHIPURAM
ANDHRA PRADESH
HARYANA
KALOL
KASIBUGAA103
106 KATIHAR
TAMIL NADU
BIHAR
104 KARNAL
107 KHARAGPUR
105
108
ANDHRA PRADESH
MAHARASHTRA
KHURJA
WESTBENGAL
RAJASTHAN
KHOPOLI
KOCHI109
112 KOLAR
UTTARPRADESH
KARNATAKA
110 KISHANGARH
113 KOPARGAON
111
114
KERALA
RAJASTHAN
KOTA
MAHARASHTRA
KARNATAKA
KOTA
KUDLIGI115
118 LATUR
RAJASTHAN
MAHARASHTRA
116 KUDACHI
119 LONAVALA
117
120
KARNATAKA
UTTARPRADESH
MADURAI
MAHARASHTRA
UTTARPRADESH
LUCKNOW
MALAVAHALLI121
124 MALEGAON
TAMIL NADU
MAHARASHTRA
122 MAINPURI
125 MALEGAON
123
126
KARNATAKA
ORISSA
MANGALORE
MAHARASHTRA
KARNATAKA
MALKANGIRI
MANOHARPUR127
130 MARIAMMANAHALLI
KARNATAKA
KARNATAKA
128 MANGALORE
131 MATHURA
129
132
RAJASTHAN
UTTARPRADESH
MEDINIPUR
UTTARPRADESH
GUJARAT
MAUNATHBHAJAN
MERTACITY133
136 MIRZAPUR
WEST BENGAL
UTTARPRADESH
134 MEHSANA
137 MORBI
135
138
RAJASTHAN
KARNATAKA
MUMBAI
GUJARAT
RAJASTHAN
MUDHOL
MURWARA139
142 NAGAUR
MAHARASHTRA
RAJASTHAN
140 MUNDWA
143 NAGPUR
141
144
MADHYA PRADESH
MAHARASHTRA
NASHIK
MAHARASHTRA
RAJASTHAN
NANDED
NIGAHI145
148 NIMBAHERA
MAHARASHTRA
RAJASTHAN
146 NASIRABAD
149 OSMANABAD
147
150
MADHYA PRADESH
RAJASTHAN
151 PARLAKAMIDI ORISSA 152 PEHAL
MAHARASHTRA
RAJASTHAN 153
PALI
PHALODI RAJASTHAN
BHUIVNADNIAVNISIUNASLTIZITATUITOEN:OHFIGRHEMROESTOELUSETINOSNINIGM,ADGEESHORFAD(1UMN)CITIES
Source: http://bhuvan3.nrsc.gov.in/applications/bhuvanstore.php (313 cities as on 11 February 2016)

41. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N

42. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N

43. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
PERSPECTIVE
PLANNING
Level-I
(1:50,000)
DEVELOPMENT
PLANNING
Level-II
(1:12,500)
ZONAL PLANNING
Level-III
(1:4,000 or higher)
PROJECT/SCHEMES
Level-IV
(1:2,000 or higher)
Built up land Rural
Built up land Residential
High rise
Apartments
Flats
Medium rise
Apartments
Flats
Low rise
Apartments
Flats
Row houses
Tenements
Slums
Seasonal
Others
Built up land Industrial
Heavy
Light
Others
Built up land Commercial
Large
Small
Others
Built up land Recreational
Parks
Gardens
Playgrounds
Scale of Planning vs. LULC Details

44. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
PERSPECTIVE
PLANNING
Level-I (1~50,000)
DEVELOPMENT
PLANNING
Level-II(1~12,500)
ZONAL PLANNING
Level-III
(1:4,000 or higher)
PROJECT/SCHEMES
Level-IV
(1:2,000 or higher)
Race Course
Golf course
Planetariums
Aquariums
Historic buildings
Theatres
Exhibition halls
Gymnasium
Swimming pools
Others
Built up land Public & semi-public
Health
Primary Health centre
Hospitals
Dispensaries
Others
Education
Schools
Colleges
Universities
Others
Religious
Public Utilities
Government offices
Cantonment areas
Electric stations and transmissions
Gas storage and transmission
Petroleum Storage and transmission
Solid waste disposal sites
Sewerage Treatment plants
Scale of Planning vs. LULC Details

45. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
PERSPECTIVE
PLANNING
Level-I (1~50,000)
DEVELOPMENT
PLANNING
Level-II (1~12,500)
ZONAL PLANNING
Level-III (1:4,000
or higher)
PROJECT/SCHEMES
Level-IV
(1:2,000 and More)
Grave yards
Built Up Land Transportation/
communications
Transportation
Others
Bus stands
Railway Stations
Airports
Harbour/Ports
Bridges/Berths
Roads
Railway tracks
Communication
Air Strips
Radio station
Radar Station
TV Station
Post office
Telegraphic office
Telephone office
Others
Mixed Built up land
Slum areas
Vacant land
Agriculture
Open Vacant land
Crop land
Fallow land
Plantations
Forest Closed
Open
Scale of Planning vs. LULC Details

46. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N
PERSPECTIVE
PLANNING
Level-I (1~50,000)
DEVELOPMENT
PLANNING
Level-II (1~12,500)
ZONAL PLANNING
Level-III
(1:4000 or higher)
PROJECT/SCHEMES
Level-IV
(1:2,000 or higher)
Blanks
Waste land Salt effected land
Water logged areas
Ravenous land
Undulating land with scrub
Undulating land without
scrub
Mining and Industrial
Waste
Sandy areas
Rock out crop/ barren land
Coastal wetland Wetland with vegetation
Wetland without vegetation
Water bodies Rivers/streams
Reservoirs
Lakes/Ponds
Canals
Salt pans
Scale of Planning vs. LULC Details

47. I N D I AN I N ST I T U T E O F RE MO T E SE N SI N G , D E H RAD U N