Using Mobile Mapping for Geospatial Data Collection and Analysis

This report provides a comprehensive review of geospatial data collection methods, with a focus on mobile mapping techniques. It begins by introducing the background of the study, highlighting the Dublin City Council's need for efficient spatial data collection methods. The report discusses traditional methods like digitizing maps, GPS technology, coordinate geometry, remote sensing, and Lidar, while pointing out their limitations. Mobile mapping is presented as a superior alternative, offering broader coverage, faster data acquisition, and efficient processing through integrated sensors. The study addresses the challenges in mobile mapping, such as sensor calibration and data processing, and emphasizes the importance of accurate navigation sensors and data integration. The literature review explores real-time mapping and its limitations, followed by an introduction to client-server architecture and mobile GIS for efficient data analysis. The report outlines the methodology, including research design, sampling techniques, and data collection methods, ultimately aiming to assess the adoption of modern geospatial data collection methods by institutions like the Dublin City Council Georeferencing team.

USING MOBILE MAPPING FOR GEOSPATIAL DATA COLLECTION
Name
Institution
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Table of Contents
Abstract......................................................................................................................................................3
CHAPTER ONE: INTRODUCTION......................................................................................................4
Background of the Study..........................................................................................................................4
Statement of the Problem..........................................................................................................................5
Objectives of the Study..............................................................................................................................7
Scope of the Study.....................................................................................................................................7
Significance of the Study...........................................................................................................................7
CHAPTER TWO: LITERATURE REVIEW.........................................................................................8
The Client-server Architecture.................................................................................................................9
The Process of Mobile Mapping.............................................................................................................10
Direct Georeferencing.............................................................................................................................10
Mapping Sensors.....................................................................................................................................11
Positioning the Sensor’s Accuracy.........................................................................................................11
Theoretical Framework...........................................................................................................................12
Theory of Reasoned Action.....................................................................................................................13
Conceptual Framework..........................................................................................................................13
The Mobile Mapping System: A Concept Design..................................................................................14
CHAPTER THREE: METHODOLOGY..............................................................................................16
Research Design.......................................................................................................................................16
Sampling Techniques..............................................................................................................................16
Data Collection Techniques....................................................................................................................17
Data Processing and Analysis.................................................................................................................17
Conclusion................................................................................................................................................18
References................................................................................................................................................20
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Abstract
Different surveying technologies are used to collect data. With the ongoing advancements
in technology, much more techniques are being developed. These new methods provide an easy
way of collecting data when an extensive survey is carried out. These technologies comprise of
digital terrestrial photogrammetry, Remote Sensing Satellites or Light Detection and Ranging
(Lidar), digital aerial photogrammetry, laser scanning, Global Positioning Systems (GPS),
digitizing and scanning data, coordinate geometry, and mobile mapping. Mobile mapping is the
most efficient technique of collecting spatial data in an extensive area. Mobile mapping involves
the capturing of data by the use of a mobile vehicle fitted with navigation and mapping sensors.
Navigation sensors track the vehicle and provide it with mapping sensors on the landscape
information.
On the other hand, mapping sensors sense the target objects directly, therefore, reducing
complexities in the calculation that may arise when using other methods. This makes the method
highly flexible and affordable in the calculation of data especially since this method utilizes little
time and effort to capture big data. This report reviews the different techniques of geospatial data
collection and compares them to mobile mapping. All methods discussed in this study will have
their strengths and weaknesses that will be solved with the use of mobile mapping. These
methods will be suggested to the Dublin City Council Georeferencing team to test and illustrate
the difference between the new methods and the first techniques of data collection that were
used.
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CHAPTER ONE: INTRODUCTION
Background of the Study
The Dublin City Council is on the verge of creating a spatial database for the city’s street
assets. There are different ways of data collection and database creation that the team can use.
However, due to limitations in the main methods of data collection, the team can use other
methods. The first method is digitizing or scanning the data which is already on the designed
maps. The data collection team can also use GPS technology which has tools that are capable of
locating an area and using it for mapping and analysis. The land surveyors, geologists, and civil
engineers can also rely on Coordinate Geometry for mapping and analyzing their data. This is a
crucial tool for them to draw curves, maps, and different objects that they will use in their data
collection.
The use of remotely sensed data is another method that the team can use. In this method,
sensors embedded to satellites are used to capture on ground images and accurately record data
— for example, weather conditions or the presence of minerals in an area. Satellite operates
remotely above in the skies. The team can also rely on the use of Lidar, which relies on laser
light to measure distances and angles. Lidar is applicable in the fields of archaeology, geology,
and meteorology to capture data. For example, the process of capturing the climate of an area by
meteorologists. Photogrammetric techniques can also be relied upon as a technique of data
collection. Photogrammetry relies on the use of digital data that the cartographers will use for
photo interpretation. This process gives clear and accurate results (Cui et al., 2017).
However, due to the unreliability of most of the methods discussed above, there is a need
to get an optimal method for data collection. One particular technique is the use of a mobile
means of geospatial data collection. Mostly, spatial data collectors use aerial photography as the
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primary way of collecting data (Delair, 2017). However, this method has limitations such as lack
of details in the photographs, i.e., the images may not portray horizontal features such as utility
holes or raised footpaths. Therefore, the photographs captured in the process are blurry or
unclear due to the camera’s perspective, focal length, and field of view. Another technique of
mobile data collection is physically surveying of the area. This method can produce accurate
results. However, this method has many challenges such as consuming too much time (Li, 2016).
In some cases, the survey is rendered to be impractical like in an instance when the surveyors
stop the traffic on a highway to complete their study.
The use of mobile mapping methods strives to solve all of the above problems. The
technique not only offers broader coverage of the area, but it also provides a faster way of
acquiring and processing the mapped data. This is possible by the use of many integrated sensors
aids.
Statement of the Problem
Since GPS is widely used by civilians, the need for mobile mapping is on the verge of
rising especially since mobile mapping relies on a mobile platform. When mapping vehicles
there is the need to use robust and high-quality sensors with a high resolution and the ability to
cover a large area. The navigation sensor is another essential requirement of mobile mapping
(He, 2000). The need to be highly accurate to determine the orientation and the position of the
vehicle is one of the significant factors to consider when choosing tools for mobile mapping. The
method may sometimes require the use of GPS technology to determine both the vehicle and the
objects.
Vehicle mapping on land is easy with the use of GPS since the objects under mapping are
close to the sensors. Processing the data gathered from mobile mapping is an important task
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since this data is always huge. Therefore, there is a need to map all sensors step-by-step so that
they can handle all the data and capture them all at once (Tatem, 2017). However, some of the
techniques in mobile mapping are automated by default. These techniques processing an image
to match the points in the sequence to the actual image and extracting raised points read from a
captured image. The vehicle’s position will change as it moves. In this process, the vehicle
acquires data of the objects to be mapped. For the sensors to assign an object, the object has to be
within the vehicle’s view (Chavez-Garcia, 2014).
When referencing, there are two critical factors to be considered. These are determining
the position of the vehicle at any given time and deriving data from the area to the vehicle. At
this point, the main challenge that is experienced is the difficulty in describing the relationship
between the vehicle’s sensors to the ground and the vehicle itself to the objects in the area. The
vehicle’s location coordinates will be found in the local coordinate system. Each sensor in the
vehicle contains a local system of coordinates that is related to the vehicle’s system (Chavez-
Garcia, 2014).
The coordinate system of the vehicle connects to the global system. For example,
consider a street light asset that is within the sensor’s field of view. This sensor represents the
sensor of the vehicle. The aim will be to calculate the area around the street light in relation to
the coordinates of the globe. Once the results of the calculation are recorded, an explanation will
be made by detecting the object by the use of several sensors and determining the position of the
street light in vis-a-vis the coordinate of the area. The system will then transform the local
coordinates of the sensor to the global coordinates. This makes it possible for any device that can
read the global coordinate system to be able to access the street light object (Maurya et al.,
2012).
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Objectives of the Study
This study will aim at finding out the extent to which different institutions of data
collection like the Dublin City Council Georeferencing team will employ the new and modern
methods of geospatial data collection. This research will also look at the influence of the new
techniques on the regular activity of the institution.
Scope of the Study
Methods of data collection are increasingly becoming used in different aspects of
surveying due to their availability, reliability, timeliness, and affordability. However, not all of
the available techniques can provide results quickly as mobile mapping. This is because mobile
mapping employs the use of readily available GPS sensors fitted in modern smartphones and
other portable gadgets. This makes mobile mapping a quicker go-to option as it is accessible
from any place and any time. Mobile mapping is also a cheaper alternative to some methods like
lidar and photogrammetry which rely on the use of expensive equipment. Therefore, this study
will be conducted in an area that has people and building structures that have access to GPS
enabled portable gadgets.
Significance of the Study
This study will provide a detailed review of methods of geospatial data collection. It will look at
how the different methods work and compare them to mobile mapping to provide the
georeferencing institutions with an idea of how mobile mapping is better over other techniques.
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CHAPTER TWO: LITERATURE REVIEW
In a recent study by Limei and others (2017) it was concluded that the main objective of
mobile mapping is to acquire data to gather information about space during surveying. The group
found out that capturing and processing data are two different tasks that do not happen at the
same time. The system will first capture data separately and then process it at a different time.
Real-time mapping is dependent on the delivery of maps and databases during mapping. Here,
information is processed in real time. Therefore, there is no need for processing this data later.
Since mobile mapping is fast and efficient, it saves time because there is no pre-processing of the
acquired data. For example, when capturing highway data, the mobile mapping van would not
cause congestion on the highway because it will be capturing data as it moves. This illustrates
that real-time mapping saves time as it provides instant processing of the spatial data. In case
there is no previously acquired spatial data, the mobile mapping system may not be viable. This
also applies to situations where the system is required to capture data at the present time. In these
two scenarios, real-time mapping is the appropriate option of collecting spatial data (Sairam et
al., 2016).
Jende and others (2016) found out that real-time mapping is affected by some issues. The
first issue occurs when GPS cannot be corrected before the master and rover stations have
produced their observations. This means that the position of the vehicle at the present time
cannot be determined. Another issue occurs when there is a need to integrate the data collected.
The integration of data from the available sensors will require that the information has to have
been gathered over a more extended period and not just the present moment. This is similar to
the process of capturing data inside a tunnel and then integrating the data in real time to show
both ends of the tunnel. The system uses a semiautomatic and interactive procedure to measure
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most features (Lwin et al., 2014). Gathering this information is not possible in real time even
though some simple objects such as the track of the vehicle and centerlines on roads can be
determined in a short time. Mobile mapping system operations are in real time for example data
completeness check. The system will process the other functions in a later time.
The Client-server Architecture
An integrated mobile software can be used to help the Dublin georeferencing team to
efficiently collect and analyze geospatial data. This software is called a Geographic Information
System (GIS). A mobile GIS is a software that is based on the framework of mobile computing
together with the internet. In other words, the mobile GIS extends the action of the Web GIS to
portable internet such as wireless and cellular (Fangxiong & Zhiyong, 2004). However, mobile
GIS have limited capabilities to the portable devices, the network to be transferred, and the
bandwidth (Drummond et al., 2006).
To address these three problems, a reliable client-server architecture can be established.
The server will enable the movement of data to a separate computer to be served to the client by
a GIS server software as figure 2.1 below presents.
GIS application GIS server Data
Figure 2.1 Client-server architecture.
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The Process of Mobile Mapping
Direct Georeferencing
Direct georeferencing involves determining the external orientation of the sensor.
However, this method does not involve the use of triangulation or control points in the ground.
For example, stamping any image with the parameter of the georeferencing process is possible
when using a camera sensor. The parameters used include both positional and attitude
parameters. There are three distinct modes to perform direct Georeferencing. These are stand-
alone, integrated, and combined georeferencing techniques (Palm et al., 2018).
In the stand-alone georeferencing mode, objects are capable of blocking GPS signals.
This step makes the GPS unreliable for providing accurate information. Although system-based
radio navigation is available, it cannot be depended upon to deliver a higher accuracy required in
mobile mapping. This mode of georeferencing is, therefore, not suitable for use in a mobile
mapping system. On the other hand, the integrated georeferencing mode involves the use of
external positioning systems. This is currently the most used approach in georeferencing because
different sensor combinations can be customized thus increasing the accuracy of the system. The
last technique of georeferencing, the combined mode, is a high-performance method of direct
georeferencing. The combined mode is widely used because it is both reliable and economical.
Mobile mappers use the combined mode together with GPS and photogrammetry to enable the
resolution of GPS errors (Masiero et al., 2017).
Mapping Sensors
These are the sensors that focus on the feature. Mapping sensors provide information
about the position of the feature in relation to the vehicle’s local coordinate system (Cui et al.,
2017). The sensors also provide the description information of the features. Mapping sensors
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include passive imaging sensors such as digital cameras, multi and hyper spectrum scanners and
active imaging sensors such as Synthetic Aperture Radar (SAR) and laser range finders. For
these mapping sensors to work in harmony, an accurate system synchronization has to be
conducted. Mobile mapping system requires a good choice of sensors and an efficient integration
of multiple sensors. The other factors to consider include accuracy, reliability, cost, portability,
and power consumption. Among the five elements, accuracy and cost are the most crucial ones
to contemplate.
Positioning the Sensor’s Accuracy
Mobile mapping relies on multiple sensors and methods that process integrated sensors.
The types of sensors used are position sensors and mapping sensors. Absolute position sensors
are focused on the vehicle. Absolute positioning sensors depend on external impulses, (EPS)
from a transmitter such as GPS. They determine the location of the platform performing mobile
mapping and relay that information to the global coordinate system. The other type of absolute
positioning sensors depends on inertial impulses, (IPS) such as the radio system which is used
for navigation. Self-dependent internal sensors such as compasses, barometers, gyroscopes, and
odometers are examples of position sensors.
A reliable positioning system should combine both internal and external signals. Table 1
below compares the internal and external signals necessary for the selection of absolute
positioning sensors.
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System Measurement Accuracy
EPS Absolute GPS C/A code 30-100 m
Differential GPS Pseudo-ranges 1-5 m
Kinematic GPS Carrier-phase 1-10 m
IPS Strap-down inertial
system
Acceleration 10 cm
Gyros & wheel
counter
Angular changes and
distances
1-3 m
Table 1: Inertial and external positioning sensors and their accuracies
The most widely used method of geospatial data collection is GPS technology as it is
readily available in most handheld devices. Data collectors use GPS to determine data about the
are to be studied as it is mobile, reliable, and provides high performance. Table 2.1 shows a
variety of modes used for a GPS operation and their different accuracies both horizontally and
vertically.
The main significance of direct georeferencing in the mobile mapping process is that it
enables a faster turnaround time for processing data and reduces the costs incurred in ground
surveys.
Theoretical Framework
This theory will be used to measure the rate at which survey companies like the Dublin
City Council Georeferencing team will accept new technologies like mobile mapping.
According to Van Schaik (2000), there are several IT acceptance theories. One such theory is the
Technology Acceptance Model.
Theory of Reasoned Action
This theory is based on Davis’ (1986) Technology Acceptance Model. This theory
proposes that an individual‘s attitude toward behavior is predisposed by his or her belief.
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