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Development of Linear Feature Based Non-Contact Bridge Deflection Monitoring System
Title
Development of Linear Feature Based Non-Contact Bridge Deflection Monitoring System.
Creator
Khamaru, Satarupa, Nagarajan, Sudhagar, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description
In any infrastructure project, monitoring and managing the built assets is an important task. Structural Health Monitoring (SHM) is meant for continuous assessment of safety and serviceability of a structure and its elements. SHM has taken a leading role in the field of structural engineering and has become very popular in recent age. Bridge deflection is the basic evaluation index to examine the health status of a bridge structure. The existing bridge monitoring systems have several...
Show moreIn any infrastructure project, monitoring and managing the built assets is an important task. Structural Health Monitoring (SHM) is meant for continuous assessment of safety and serviceability of a structure and its elements. SHM has taken a leading role in the field of structural engineering and has become very popular in recent age. Bridge deflection is the basic evaluation index to examine the health status of a bridge structure. The existing bridge monitoring systems have several drawbacks. Hence, a new methodological approach has been proposed to overcome the limitations of traditional contact-based bridge deflection monitoring system and other non-contact based system. This study developed a non-contact linear feature based Deflection Monitoring System (DMS) using Terrestrial Laser Scanning (TLS) and cameras for timber railroad bridges. The process and detailed workflow of building the DMS, its components and sensors involved are discussed here. The efficiency of this DMS is validated against a deflectometer.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013081
Subject Headings
Structural health monitoring., Bridges--Evaluation., Deflection.
Format
Document (PDF)
Development of a Mobile Mapping System for Road Corridor Mapping
Title
Development of a Mobile Mapping System for Road Corridor Mapping.
Creator
Sairam, Nivedita, Nagarajan, Sudhagar, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description
In any infrastructure project, managing the built assets is an important task. In the case of transportation asset inventories, a significant cost and effort is spent on recording and storing the asset information. In order to reduce the time and cost involved in road corridor mapping, this paper proposes a low cost MMS (Mobile Mapping System) using an equipped laser scanner and cameras. The process of building the MMS, components and sensors involved and calibration procedures are discussed....
Show moreIn any infrastructure project, managing the built assets is an important task. In the case of transportation asset inventories, a significant cost and effort is spent on recording and storing the asset information. In order to reduce the time and cost involved in road corridor mapping, this paper proposes a low cost MMS (Mobile Mapping System) using an equipped laser scanner and cameras. The process of building the MMS, components and sensors involved and calibration procedures are discussed. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. The paper also provides a framework to extract road assets both automatically and manually using stateof- the-art techniques. The efficiency of this method is compared with traditional field survey methods. Quality of collected data, data integrity and process flow are experimented with a sample asset management framework and a spatial database structure for mapping road corridor features.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004629, http://purl.flvc.org/fau/fd/FA00004629
Subject Headings
Transportation engineering., Electronics in engineering., Geographic information systems--Software., Internetworking (Telecommuniation), Geospatial data.
Format
Document (PDF)
OBJECT-BASED LAND COVER CLASSIFICATION OF UAV TRUE COLOR IMAGERY
Title
OBJECT-BASED LAND COVER CLASSIFICATION OF UAV TRUE COLOR IMAGERY.
Creator
Castillo, Stephen M., Nagarajan, Sudhagar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Land cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover...
Show moreLand cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover classification. In addition to the objective of land cover classification, comparison of varying spatial resolutions of the imagery will be analyzed in the accuracy assessment of the output thematic maps. These resolutions will also be compared at varying training sample sizes to see which configuration performed best.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013454
Subject Headings
Land cover, Unmanned aerial vehicles, Drone aircraft in remote sensing, Images, Classification
Format
Document (PDF)
MACHINE LEARNING APPROACH FOR VEGETATION CLASSIFICATION USING UAS MULTISPECTRAL IMAGERY
Title
MACHINE LEARNING APPROACH FOR VEGETATION CLASSIFICATION USING UAS MULTISPECTRAL IMAGERY.
Creator
Kesavan, Pandiyan, Sudhagar Nagarajan, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for...
Show moreVegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013768
Subject Headings
Vegetation classification, Machine learning, Multispectral imaging, Unmanned aerial vehicles
Format
Document (PDF)
MULTISPECTRAL UAS BASED COASTAL CHANGE DETECTION METHODS
Title
MULTISPECTRAL UAS BASED COASTAL CHANGE DETECTION METHODS.
Creator
Rajkumar, Monica, Nagarajan, Sudhagar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Coastal landscape plays a vital role in reflecting various natural processes. Vegetation resource management improves the quality of life above the surface of the earth. Due to factors such as climatic change, urban development, and global warming, monitoring the coastal region as well as its vegetation has indeed become a challenge to mankind. The purpose of the study is to propose an effective low-cost methodology to monitor the 120- acre Jupiter Inlet Lighthouse Outstanding Natural Area ...
Show moreCoastal landscape plays a vital role in reflecting various natural processes. Vegetation resource management improves the quality of life above the surface of the earth. Due to factors such as climatic change, urban development, and global warming, monitoring the coastal region as well as its vegetation has indeed become a challenge to mankind. The purpose of the study is to propose an effective low-cost methodology to monitor the 120- acre Jupiter Inlet Lighthouse Outstanding Natural Area (ONA) located in Jupiter, Florida (USA) using Unmanned Aerial Systems (UAS) Imagery deployed with RedEdge Micasense Multispectral sensor having five bands. Since, UAS provides high resolution imagery at lower altitudes, it has a lot of potential for variety of applications. This research aims to (1) Automate the extraction of shoreline and coastline through Modified Normalized Difference Index (MNDI), thereby comparing it with the manually digitized shoreline using transect-based analysis (2) Automate the volume change computation, as the area has been affected due to various natural and anthropogenic factors in the past few decades. (3) Perform shoreline change detection for the time period 1953 to 2021 (4) Develop an algorithm to differentiate ground and non-ground points along the shore region and generate Digital Terrain Model (DTM) (5) Land use and Land cover (LULC) mapping using different band combinations and compare its result using deep learning approach.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013931
Subject Headings
Coastal zone management--Florida, Jupiter Inlet Light (Fla.), Multispectral imaging, Drone aircraft, ArcGIS
Format
Document (PDF)
DEVELOPMENT OF GIS-BASED ONLINE WATERSHED DASHBOARD FOR CHARLOTTE COUNTY, FLORIDA
Title
DEVELOPMENT OF GIS-BASED ONLINE WATERSHED DASHBOARD FOR CHARLOTTE COUNTY, FLORIDA.
Creator
Zare, Saeid Naghadehi, Nagarajan, Sudhagar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
This thesis presents the development of an innovative Geographic Information System (GIS)-based Interactive Online Watershed Dashboard aimed at flood risk assessment and mitigation in Charlotte County, Florida. The research leverages advanced GIS techniques, including flood inundation simulations using CASCADE 2001, integrating LiDAR DEM data and GIS layers such as impervious surfaces, waterbodies, and soil characteristics to model flood behavior in 61 inundation probability scenarios. Key...
Show moreThis thesis presents the development of an innovative Geographic Information System (GIS)-based Interactive Online Watershed Dashboard aimed at flood risk assessment and mitigation in Charlotte County, Florida. The research leverages advanced GIS techniques, including flood inundation simulations using CASCADE 2001, integrating LiDAR DEM data and GIS layers such as impervious surfaces, waterbodies, and soil characteristics to model flood behavior in 61 inundation probability scenarios. Key results include detailed flood inundation probability maps categorizing risk levels based on Z-scores, providing actionable insights for flood risk management and emergency planning. Spatial analysis reveals demographic vulnerabilities, with population density and ethnic compositions intersecting flood vulnerability. The study assesses flood impacts on transportation infrastructure and prioritizes critical facilities for resilience strategies. The dashboard's design integrates diverse datasets and analytical results, allowing users to interactively explore flood risk scenarios, critical infrastructure vulnerabilities, and demographic impacts. This research contributes essential tools for informed decision-making, enhancing flood resilience and disaster preparedness in Charlotte County, Florida.
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Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014437
Subject Headings
Watersheds, Dashboards (Management information systems), Geographic information systems, Floods--Risk assessment
Format
Document (PDF)