Using Mobile Mapping for Geospatial Data Collection

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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