Spatial representation of geographical information

Spatial representation of geographical information

Table of Contents

Table of Contents 1

1. Introduction 3
2. Remote Sensing 4

2.1 What is Remote Sensing? 4

2.2 Remote Sensing process 4

2.3 Electromagnetic Sensing Process 5

2.4 Microwave Sensing 6

2.5 Remote Sensing platforms 7

3. Geographic Information Science 7

3.1 GI Science – What and Why? 7

3.2 Disciplines contributing to GI Science 8

3.3 The 4 Ms of GI Science 9

3.4 GIS Workflow 10

3.5 GIS Architecture 11

4. Spatial Data – the nexus between GI Science and RS 12

4.1 Stages of GIS modeling 12

4.2 Graphic Representation of Spatial Data 12

4.3 Raster Images 12

4.4 Vector Images 12

4.5 Comparison of Raster and Vector images 12

5. RS and GIS Integration 12

5.1 Strategies for Integration 12

5. 2 Requirements for Integration 12
6. Applications 12
7. Conclusion 12

List of Tables

Table 1 Major Remote Sensing Platforms 7

Table 2 Functional Elements of GI Science 11

List of Figures

Figure 1 Discrete Energy Levels, Absorption and Emission Spectra. 5

Figure 2 Remote Sensing – Electromagnetic Sensing Process 6

Figure 3 Overview of Information Systems 8

Figure 4 GIS – Multidisciplinary Science 9

Figure 5 The Four Ms of GIS 10

Figure 6 Geographic Information Science Work Flow 11

Introduction

Spatial representation of geographical information has been in practice for a very long time now. Even the most ancient civilizations kept track of the spatial information of their land, boundaries. Information about the immediate neighborhood as well as about distant places, travel routes were collected and maintained for trading and sea navigation. Apart from spatial information, there was also data on the population, a number of birth and death, count of cattle, etc. was regularly maintained since very early days. The same data and a lot more of information are being collected by various government agencies, in every country on earth, today. This geographical information might be in the form of cartographer’s map, weather, and meteorological information, census data, etc. Natural resources management, urban and rural development, tourism and infrastructure development, environmental management, etc. require all this data.

With digitization and computers everywhere, translating all the geographical data from paper to digital form is challenging. Geographical data when digitized can be utilized for better planning, facility management and e-governance such digitization is essential. Geographic Information Science, also known as GI Science, in short, aims at creating error-free geographical data and maintenance of the data such that it could be easily retrieved and updated. Numerous advancements in IT sector have facilitated storing and retrieval of textual or formatted data, but storing spatial data is still a major challenge in GI Science.

Another major technological advent that helps collection of geographers and cartographers is Remote Sensing. Remote Sensing is a technology that facilitates collecting information about various parts of the earth without even being in contact with it. Images from high-flying drones, satellites, etc. are some examples of remotely sensed data. But these imageries have to be projected on to planar surfaces using various map projections like Mercator, Transverse Mercator, and Oblique Mercator, etc. Another challenge is in converting the digitizing units into real world coordinates. Remote Sensing data is used in two ways: a) It is used as a source of geographic data, and b) It is used to enhance the functionality of geographic data.

The ability to store and view geographic data has several different applications, some of which are listed below:

No one is a stranger in the city. Even people who are new to the city can navigate all by themselves.

In the case of emergencies, the nearest helping agency, such as a fire station or a police station or a hospital could be located easily. Further the shortest route, could be found,

Disaster management, in time of earthquakes, land-slides, etc.,

Satellite imagery helps by providing information about weather, traffic flow, etc. in real time,

Farmers monitor their farms and crops from a distance,

Various resources such as water, forestry, mineral ores, etc. are identified and managed,

Nearby restaurants, coffee shops, etc. can be located.

These applications require the integration of both Geographic Information Sciences and Remote Sensing technologies. One technology without the other would not be just as useful. Say there is no remote sensing component, then the data obtained would not be real time. All these applications solve the purpose only when the data is real time. Static data is not useful. Location and distance are the two most important features in any geographical application and must be very specific and accurate, failing which the applications become unusable. Much the same way, if Geographic Information Sciences is not supportive enough to receive, store, retrieve and manipulate the remotely sensed data in real-time, the objective is not met as well. Thus with these examples, it is evident that Remote Sensing and Geographic Information Systems have a synergistic relationship and eventually, the boundaries between GIS, GPS, and Remote Sensing are becoming blurred.

Digital image processing, Raster to Vector conformation, standards in geocoding procedures, Aerial photogrammetry, etc. are some factors which have led to the synergy between Geographic Information Sciences and Remote Sensing. In this paper, we detail on Remote Sensing in detail in the first subsection, followed by an equally detailed description of Geographic Information Systems. The next section entails a deeper and elaborate discussion on the integration between these two. Various strategies, technical requirements, and challenges in the integration are presented in this section. A detailed overview of various applications of integrating these two technologies is presented and is followed by concluding remarks.

Remote Sensing

2.1 What is Remote Sensing?

Remote sensing is the technology that enables us to obtain information about an entity without being in physical contact with the observed entity (Aggarwal, 2004; Zhu et al., 2014)⁠. The sensing means information acquiring. Remote means without being in physical contact. So, whether the observed object is very close or very far from the observer, the information about the object is collected, without coming in physical contact with the object. There must not be any carrying medium between the observed entity and the observer, that carries the sensed data, must not exist. The absence of any matter between the object and the observer is another important distinguishing feature of Remote Sensing.

Usually, the sensors are mounted on aircraft or satellites. These sensors are capable of detecting electromagnetic waves that are either emitted or reflected from the observed surface. Multiple measurements are made at multiple points. These points can be on a straight line (for covering long regions) and give a one-dimensional profile of thee observed area. Sometimes, a two dimensional data covering a wider area can also be obtained by using sensors like a) Nadir Looking sensors and b) Straight looking sensors.

2.2 Remote sensing process

The remote sensing process, in general, depends on the electromagnetic spectrum. Electromagnetic waves have a dual nature, the wave nature, and the particle nature. The particle nature (quanta) of the wave has a very important process in the electromagnetic sensing process. As energy is discretized into well-defined, discrete energy levels, which differ from material to material, the transitions between these energy levels are available and can be used to identify and characterize the material (Aggarwal, 2004; Zhu et al., 2014)⁠. The same principles are used in spectroscopic studies as well. Figure 1. Describes that when energy level goes from a lower level to a higher level, there must have been absorption and when there is a fall from a higher energy level to a lower energy level, there must have been emission.

The interaction of energy with the atmosphere is a major source of noise in the sensed data. Atmospheric scattering and absorption can change the speed, intensity, and spectral distribution, etc. of the incoming radiation from the source, causing noisy data. These effects are most critical in the visible and Infra – Red regions of the Electromagnetic spectra. Further, the clarity of the sensed data depends heavily on the weather conditions prevailing at the time of sensing. Imagery obtained on foggy weather are blurry and cannot be used for analysis. Microwave sensing process is robust in this regard and also uses time delay and other digital signal processing techniques to cover the wider range and finer details with a narrower band. Hence Microwave Remote Sensing is a favored technology.

Figure 1 Discrete Energy Levels, Absorption and Emission Spectra.

Usually, Reference images taken at distinct time points are taken to support the analysis. These reference data is called as Ground Truth data or field check data. These are valuable not only analyzing the data but also used to calibrate sensors and to validate the accuracy of images obtained. Reference data can either be Time – Critical Data or Time – Stable data. Time – Critical data is the data collected from time to time to observe phenomena that change over time such as water depletion, pollution, afforestation/deforestation, etc. Time – Stable data is data that would have almost no or very minimal changes over time, such as geology of an area.

2.3 Electromagnetic Sensing Process

Electromagnetic energy is hence used as the communication medium or link connecting the observed entity and the observer. It carries the information sensed about the object to the observer using variations in intensity, frequency, magnitude and phase of the electromagnetic wave. Measurement and subsequent recording of electromagnetic energy reflections or emissions from the surface of the earth and the atmospheric layer from various points on either a single line or across an area, provides a lot of information, about the monitored surface or region (Aggarwal, 2004)⁠.

The major components of various components of an EM spectrum are given in Figure 2. The major components of the Electromagnetic Sensing process are:

The source of energy,

The energy transmission

The interaction of transmitted energy with the earth’s surface

The retransmission of the absorption/emission spectrum

The sensors that could be mounted either on Satellites, Aeroplanes, drones or Air balloons.

The sensing products, which can be a cartographer’s map, pictorially drawn on paper or digital data, stored in CDs or floppy disks or on any digital storage media.

Interpretation and Analysis can be done, visually by inspecting or by using Digital signal and image processing techniques

Various information such as watershed, slope, etc. can be represented as spatial data, using points, lines, areas, and surfaces.

Figure 2 Remote Sensing – Electromagnetic Sensing Process

2.4 Microwave Sensing

Microwaves provide an alternative strategy for sensing information remotely. The microwaves are a part of the electromagnetic spectrum and have a wavelength between 1 mm and 1 m. This spectral band of the Electromagnetic spectrum is called the Microwave Band. While this band is also a part of the Electromagnetic spectrum, the sensing process differs from the Electromagnetic sensing process in a few key aspects. The source of energy, in the case of electromagnetic sensing, was the sun. But in the case of microwave sensing the source will be either an independent illumination or the heat originating from the surveyed surface (Ulaby et al., 1982)⁠. The former is called as Active Microwave Sensing, and the latter is Passive Microwave Sensing.

Microwave sensors can either be imaging or non-imaging sensors. Most of the imaging microwave sensors are Side Looking Airborne RADARs or simply called SLARs. RADAR stands for Radio Detector and Ranging. It is a technology that has an active illumination source sending bursts of energy to the survey object, i.e., the target. The echoes received carry information about the terrain or the target. The illumination source and the reception unit together, guide the process of obtaining geographic information. Surface roughness is related to scattering by Rayleigh’s criterion:

.

Speckle noise and backscattering give raise to junk artifacts in the data collected. Resolution of the RADAR depends on the resolution of antenna beamwidth and ground range. To improve resolution with a shorter antenna, signal processing strategies are used. In these, the time delay in echo reception is utilized to cover a wider area, with a low-resolution antenna. This technology is called as Synthetic Aperture RADARs or simply SARs. These facilitate sensing of finer details with narrower beams and hence are advantageous over SLARs.

The advantages of microwave sensing technology are that they are more robust and can provide data in most weather conditions. Microwaves can penetrate through fog and mist. Another advantage is the clarity and reliability of the data. The major drawbacks of microwave sensing technology are: a) aspect, b) backscattering, c) Layover and foreshortening, and d) Radar shadow. While some noise artifacts like speckle noise can be easily removed by digital image processing techniques, others require manual curation.

2.5 Remote sensing platforms

The satellites are the primary source of remotely sensed imagery and hence called as platforms of remote sensing (Acker et al., 2014; Toth and Jóźków, 2016)⁠. Based on the purpose or application, the remote sensing platforms are classified into various categories as tabulated in Table 1.

Remote Sensing Platforms

Description and Examples

Earth resources Satellites

These sense in the visible and near visible spectrum and are used to locate various natural resources including mineral exploration. E.g. Landsat; SPOT; IRS; AEM.

Meteorological Satellites

These are used to observe the climate and weather conditions, soil and dust analysis, moisture conditions, change in vegetation patterns, etc. E.g. NOAA; GOES; NIMBUS; Meteosat.

Microwave Satellites

While the above to groups use electromagnetic remote sensing, satellites in this group are predominantly SLARS and SARs using microwave technology. They are often used to monitor seas, oceans, ocean color, coastal lines, Ice-caps, huge mountains prone to land-slides, volcanoes, monitoring global weather and land use, etc. E.g. SeaSAT; ERS-1; OceanSAT; RADARSAT.

High-Resolution Satellites

With frequent revisits, these satellites provide a lot of information than other satellites. E.g. IKONOS; Quick Bird; CartoSat -1; ResourceSat -1.

Table 1 Major Remote Sensing Platforms

Geographic Information Science

3.1 GI Science – What and Why?

Geographic Information Science deals with the creation, storage and retrieval of digital geographical information (Bethel et al., 2014; Kawabata et al., 2010)⁠. For instance, questions like:

What is the soil type present in a specific land area?

Is there a trend where people are moving towards cities?

Is the population evenly distributed in cities and villages?

Different types of information received from different sources are all collected, structured and integrated to a consistent format. This process of collecting and formatting unstructured raw data into an organized, consistent information is the key aspect of Geographic Information Science. Roger Tomlin, made a cost-benefit analysis to show that despite the difficulties and humongous initial cost, the benefits of digitizing geographic information is worthy. Many innovations such as Automatic Digitiser free-pencil etc. made the process of digitization possible. The advancements in input, output and storage facilities have also greatly favored the development of GIS.

Further another important aspect of GIS is the ability of GIS to operate at various levels and scales and at multiple time periods. In this regard, GIS can be thought of as an IT application, for manipulating and analyzing geographic information. To this aim, the GIS follows the regular process cycle of any IT process. This is cycle is represented as an overview in Figure 3. The user requirements, such as the ability, the user actions, such as to pan, etc. help in planning the data collection, modeling and analysis purposes and vice-versa.

Figure 3 Overview of Information Systems

3.2 Disciplines contributing to GI Science

To make many GIS applications, an understanding of geography alone would not be sufficient. In fact, GI Science is a typical example of interdisciplinary sciences. Maps and cartography lie in the heart of GI Science. Digital image processing, Mathematics, Surveying, and Statistics also are very important for the development of GI Science Applications. This multidisciplinary approach is pictorially represented in Figure 4. Information technology strategies including pre-processing, data cleaning, data normalization, redundancy and dependency analysis, database organization, etc. also contribute significantly to GI Science.

Another very important area that contributes to GI Science is Remote Sensing. Remote sensing provides real-time data for GIS Applications. The ability to survey any location on earth’s surface at a very low cost is made possible only by GI Science. This real time imagery obtained by remote sensing can be used in combination with other geospatial information, and various new applications have been developed with the help of these technologies.

Computer Aided Design and Computer Aided Modelling helps in the development of GI models and maps. The technology, often dubbed as digital cartography is very effective in creating digital maps. Digital Photogrammetry and Surveying provides very high-quality digital maps, where the precise positions of buildings, topography and other cadastral features. High quality, low cost, 3-dimensional views, good spatial resolution, real time data availability, time-freezing ability, etc. are some of the features of GI Science, owing to the integrative approach of diverse disciplines (Coppock, J. T., and Rhind, 1991; Wright et al., 1997a)⁠.

Figure 4 GIS – Multidisciplinary Science

3.3 The 4 Ms of GI Science

The four major activities in any GI science undertaking are listed in Figure 5. Various environmental features such as land use, land cover, cadastral, vegetation, forestry, agriculture, hydrology, etc. have to be covered. These environmental parameters are measured, and maps are developed. The process of developing maps is called cartography or mapping and can either be either digital Changes that occur in the environment are constantly monitored and recorded. Changes across time and space often have to be recorded and used as reference data. These data can be used to model terrains, drainage networks, etc. On the other hand, the models that are manually curated and established can be used to field check any new data obtained.

Further GI Science applications, maps often have to be overlayed, classified and reclassified. For such requirements, developing robust models addressing specific spatial operations are carried out. Apart from these spatial operations, analytic operations involving measurement of angles, distances, areas are also to be calculated and hence both a standard measurement procedure and standard unit.

GRASS, ARC GIS, etc. are popular applications. While ARC-GIS is proprietary, GRASS is open source. Both the software have several modeling spatial data to simple inventory and management.

All the four operations can be carried out:

Ability to pre-process data into a form suitable for analysis,

Support for cartographic

Direct support for analysis and modeling, and

Ability to post-process results, reformat, generate reports, etc.

Figure 5 The Four Ms of GIS

3.4 GIS Workflow

GI Science workflow typically consists of the following

data acquisition,

data pre-processing,

data management,

data analysis and manipulation

result reporting.

The workflow is shown Figure 5. When a map is scanned, there might be a skew. The skew has to be corrected before further processing. Aerial photography, ground photography, satellite imagery, etc. are other sources of input. In any of these cases the data obtained from a spherical non-planar surface is projected on to a planar surface, and hence there may be errors (Wright et al., 1997b)⁠. Scaling provides another major challenge. While actual measurements are made regarding kilometers and acres, the digital representation would be in terms number of dots and pixels. Establishing scales is hence a major challenge, and numerous procedures have been developed for this pre-processing step.

Once digital geographic information is ready, developing a suitable database schema for storing so many diverse categories of data, with minimal redundancy and providing efficient retrieval strategies is another major challenge. Various Database Management Systems like SQL, Oracle, etc. have several functions developed for creating and maintaining GI databases (Xie et al., 2012)⁠.

Abilities to manipulate and analyze the GI data stored, efficient query processing, etc. are also an integral part of GI Workflow. Once the analysis is done, suitable means to present them such as report generation, dashboard presentation, etc. are also required, and these aspects form the last part of the GI workflow.

Figure 6 Geographic Information Science Work Flow

3.5 GIS Architecture

The current GI architecture can be broadly classified into two groups, viz. 1) Functional elements of GI Science (Hojek, 2008) and b) Functional operations of GI Science (Blaschke, 2010)⁠. Based the Functional elements of GIS, the architectural approaches to develop a model are tabulated as Table 2, below.

Approach

Description

Database approach

Development of data structures for complex GI data

Process-oriented approach

User based sequence of system elements for running an application

Application oriented approach

Focusing on end user GI applications

Toolbox approach

Developments of software algorithms for GI data

Table 2 Functional Elements of GI Science

Based the Functional operations of GIS, the architectural approaches to develop a model are:

models using map algebra,

models based on analytical operations.

The analytic operations are reclassification, overlay, distance measurement, connectivity measurement, etc. (Shapiro and Westervelt, 1992)⁠.

Spatial Data – the nexus between GI Science and RS

Human eye and brain work effectively in recognizing spatial features and terrains. Making computers to do so is a mighty feat. Precise instructions are required by computers to process spatial data. Remote Sensing ad plotter means of data collection have to be used to build models and develop GI applications.

4.1 Stages of GIS modeling

Various stages in GI Science modeling are listed below:

1. a) Develop algorithms and software programs on representing, handling and displaying spatial data.
2. B) Design and implement GI data models.
3. C) Development or Selection of a suitable GI data structure.

4.2 Graphic Representation of Spatial Data

Spatial data has to be represented as digital images. Raster and vector formats are two popular formats to store spatial data. The data modeling is proceeded either using Raster approach or Vector Approach. The most important challenge in GI Science is the integration of these two approaches (Winter, 1998)⁠.

4.3 Raster Images

In Raster images, the land is divided into land parcels and regular polygons such as squares and rectangles, are used. These cells are called grid cells limit boundaries. Numbers are used to representing different land forms and each grid with a number devoting the corresponding number (Chiang and Knoblock, 2015; Holroyd and Bell, 1992)⁠. The most important challenge is recognizing the textual information from raster images.

4.4 Vector Images

In the case of vector images, the representation is made regarding points, lines, polygons, angles, and distances. Cartesian coordinates are used to represent the (x,y) positions. Though vector images are very precise obtaining the standard measurement, using standard units, the positioning of tics, etc. are difficult. Also selecting the number of data points in a vector image is also an important factor influencing the size, accuracy, and precision of a vector image (Hojek, 2008)⁠.

4.5 Comparison of Raster and Vector images

Raster images are processed along rows, and east to west direction is preferred for numbering rows. Columns are numbered from north to south. The cell is identified by an ordered pair (row index, column index) (Holroyd and Bell, 1992)⁠. Origin is a usually upper right corner and numbered (0,0) conventionally. While other sources of spatial data use latitude and longitude, the raster representation is distinct in this regard. The three popular Raster models in use today are:

1. a) GRID/LUNAR/MAGI
2. b) IMGRID and
3. c) MAP.

Images, vector images have lines and polygons. Layering and overlaying is easy with Vector images than with Raster images. The three major types of vector data models are:

1. a) Spagetti models,
2. b) Topological models and
3. c) Shape files.

A detailed comparison of raster and vector images for landscaping can be found in (Wade et al., 2003)⁠ This shows that there some GI applications that require raster images while for some tasks, vector models are preferred. A combined approach to use both the models and create a hybrid data model is crucial in many GI applications (Toth and Jóźków, 2016)⁠.

5. RS and GIS Integration

Most of the remote sensing imagery are rasterized easily. Various levels of imagery, multi spectral and hyperspectral images are obtained from remote sensing. Collateral data with this imagery would be in vector format. Hence GI Science aims at integrating these two, failing which using Remote Sensing data for GI sciences would not be so efficient (Joshi et al., 2002; Winter, 1998)⁠.

5. 1 Strategies for Integration

Using distinct data structures and dedicated software that could analyze spatial data in both raster and vector format are required to be developed. Some popular GIS software packages integrate both these formats. Some of them are listed below:

1. a) ARC GIS,
2. b) PAMAP,
3. c) GIS MAPPER
4. d) ANALYSER
5. e) TOPOGRAPHER etc.

5.2 Requirements for Integration

Raster images contain a wealth of information, which has to be extracted from them using various photogrammetric techniques, visual image processing techniques, digital image processing techniques, and statistical analyses. The computing facility required to carry such work is usually very demanding. Various devices and strategies such as PC-based desktop mapping, digital photogrammetry, analytical stereo plotters, orthophoto scopes are used for this data extraction process. The type of computing facility used for this process depends on the nature of the information required, the level of accuracy sought, timeline associated, cost, etc. For extracting information from raster images, the available raster images must be accessible and appropriate. This is one of the major challenges in integration of RS and GI Science.

In the case of vector images that are remotely sensed, the GI Science requires several sophisticated software for image processing, that is integrated into the system. Esri’s ARC/INFO, ArcNiew, and ARC/GRID , etc. are some software to accomplish much of this integration (ESRI, 1993)⁠. Raster image processing software is ERDASllmagine, ER Mapper, EASI/PACE, and Intergraph’s MGE Base Imager (MAl) version. Most of this software are commercial (Maguire, 1992; Maus et al., 2009)⁠.

Another important challenge in implementing any rastor or vector based important GI Science is creating the grid interface between the two systems and the file systems. File formats, structures have to be carefully considered while carrying out overlaying and reclassification, etc. because linking raster and vector systems for specific applications. These are major requirements for linking GIS with Remote Sensing.

Applications

Remote sensing and GI Science when integrated provides numerous beneficial applications. Aerial photographs of large farmlands can be obtained by remote sensing and a desktop PC (Sun and Zhu, 2011)⁠. Identifying the spread of diseases is another major application of GIS and RS (Rogers and Randolph, 2003)⁠. Modeling landforms and mapping hydrology have a very useful application of GIS and Remote Sensing (Bocco et al., 2002; Frankenberger et al., 1999; Gong et al., 2007)⁠. Area wide and region wide watershed etc. has been modeled (Dhawale, 2013).

GIS and RS have also found applications in supply chain management and inventory management (Li-juan et al., 2005)⁠. Semantic classification of urban buildings, highways, etc. has been one of the most successful applications of RS an GIS and this technology has almost become inevitable in the day to day lives of many people (Du et al., 2015)⁠. Forest classification and landform classification are also carried out using GIS and RS (Hojek, 2008)⁠.

Monitoring the vegetation patterns (Jianzhong and Fenqin, 2012)⁠, levels of deforestation, and afforestation (Gilliams et al., 2005; Hossain et al., 2003; Kobler et al., 2005)⁠, identification of minerals (Zhang et al., 2011)⁠ , land resource planning (Nyeko, 2012)⁠ etc. are other applications of GIS. Disaster management, land slide management earthquake monitoring and evacuation, etc. on time over a wide area in large scale is made possible through GIS and RS (Li and Tao, 2009; Naghdi et al., 2008; Paper, 2006; Wang et al., 2007)⁠. The advent of Internet of Things is another major technology that adds to the advantage of RS and GIS. Multiple sensors in cell phones and other handheld devices etc. in every common man’s hand also sense data and adds to the GI data (Isikdag, 2015; Liu and Zhu, 2014)⁠. This data when integrated with the aerial imagery data might help in developing several new real time applications from diverse fronts including healthcare, industry, business, ecology, etc. Hence, without a doubt, the new applications of GIS would be more beneficial and more accurate.

Conclusion

Remote sensing is a technology that enables surveying a specific region of the earth’s surface no matter how close or how far away the location is. Geographical information sciences is a technology that facilitates effective storage access retrieval and analysis of geographic information including remotely sensed data. There exists a synergistic relationship between the two technologies in that data acquisition by remote sensing could not be used without an efficient storage or retrieval mechanism. Geographical information systems though may contain the huge volume of data would not be used if everything is just static. Remote sensing provides the most cost effective way for capturing real time data. Hence the relationship between GIS and remote sensing is mutually beneficial and viewing the line that divides these two as two separate sciences have become very blurry.

Various challenges provided by geographical information such as handling raster images and vector images, projections, unit conversions, redundancy reduction, data normalization, etc. are presented in GI Science. Use of multispectral, high-resolution images, covering a wide area, low cost, 3-dimensional view are advantages of remote sensing. Challenges posed by remote sensing are handling of radar shadow, foreshortening, and overlay, etc. GI Science provides a means to overcome these challenges with digital image processing, factorization, etc.

Together these two technologies complement each other in creating numerous applications. Applications in real-time such as traffic management, town planning, vegetation and agriculture management, disaster management such as mass evacuation in times of floods, earthquakes, etc. are made practically realizable owing to the integration of GI Science and Remote sensing. The bloom in computing powers as well as mass sensing abilities provided by Internet of Things are driving GIS and RS to aa new level. Hence, it is perfect to conclude that the synergic integration of these two technologies makes the earth a better place to live.

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