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Full-Text Articles in Artificial Intelligence and Robotics

Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay Mar 2022

Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay

Centre for AI & Data Governance (2019-2025)

Pivoting on the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the HRI interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed Decision to Trust model considers more extensively the human and situational factors influencing how trust …

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Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler Mar 2022

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

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Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank Mar 2022

Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank

Psychological Science Faculty Research & Creative Works

Artificial agents such as robots, chatbots, and artificial intelligence systems can be the perpetrators of a range of moral violations traditionally limited to human actors. This paper explores how people perceive the same moral violations differently for artificial agent and human perpetrators by addressing three research questions: How wrong are moral foundation violations by artificial agents compared to human perpetrators? Which moral foundations do artificial agents violate compared to human perpetrators? What leads to increased blame for moral foundation violations by artificial agents compared to human perpetrators? We adapt 18 human-perpetrated moral violation scenarios that differ by the moral foundation …

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Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang Mar 2022

Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …

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Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire Mar 2022

Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n …

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Can Lethal Autonomous Weapons Be Just?, Noreen L. Herzfeld, Robert H. Latiff Jan 2022

Can Lethal Autonomous Weapons Be Just?, Noreen L. Herzfeld, Robert H. Latiff

Computer Science Faculty Publications

In 2018 the United States Department of Defense (DoD) created a new Joint Artificial Intelligence Center to study the adoption of AI by the military. Their strategy, outlined in a document entitled, “Harnessing AI to Advance Our Security and Prosperity,” proposes to accelerate the adoption of AI in the military by fostering a culture of experimentation and calculated risk taking, noting that AI will change the character of the future battlefield and, even more, the pace of battle. Is there any way to ensure that this future battlefield will be just? Can the age-old precepts of just warfare help guide …

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Leveraging Developmental Psychology To Evaluate Artificial Intelligence, David S. Moore, Lisa M. Oakes, Victoria L. Romero, Koleen C. Mccrink Jan 2022

Leveraging Developmental Psychology To Evaluate Artificial Intelligence, David S. Moore, Lisa M. Oakes, Victoria L. Romero, Koleen C. Mccrink

Pitzer Faculty Publications and Research

Artificial intelligence (AI) systems do not exhibit human-like common sense. The principles and practices of experimental psychology – specifically, work on infant cognition – can be used to develop and test AIs, providing insight into the building blocks of common sense. Here, we describe how the evaluation team for DARPA’s Machine Common Sense program is applying conceptual content, experimental design techniques, and analysis tools used in the field of infant cognitive development to the field of AI evaluation.

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A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit Jan 2022

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit

Engineering Technology Faculty Publications

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …

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Exclusion Cycles: Reinforcing Disparities In Medicine, Ana Bracic, Shawneequa L. Callier, Nicholson Price Jan 2022

Exclusion Cycles: Reinforcing Disparities In Medicine, Ana Bracic, Shawneequa L. Callier, Nicholson Price

Articles

Minoritized populations face exclusion across contexts from politics to welfare to medicine. In medicine, exclusion manifests in substantial disparities in practice and in outcome. While these disparities arise from many sources, the interaction between institutions, dominant-group behaviors, and minoritized responses shape the overall pattern and are key to improving it. We apply the theory of exclusion cycles to medical practice, the collection of medical big data, and the development of artificial intelligence in medicine. These cycles are both self-reinforcing and other-reinforcing, leading to dismayingly persistent exclusion. The interactions between such cycles offer lessons and prescriptions for effective policy.

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Modeling And Visualization Of Long-Term Public Opinion On Covid-19 Vaccine, Ashiqur Rahman Jan 2022

Modeling And Visualization Of Long-Term Public Opinion On Covid-19 Vaccine, Ashiqur Rahman

Graduate Research Theses & Dissertations

The coronavirus pandemic created significant dependence on social media. While the social web was crucial in spreading timely information and informing the public, misinformation has also spread with little to no oversight. Several works have focused on identifying misinformation and topic analysis in COVID-19 (SARS-COV-2) tweets. While most of the previous studies focus on a shorter time frame, we analyzed a larger dataset starting from the beginning of the pandemic until the end of December 2021. Our work focuses on a novel area that identifies the motivating and demotivating topics of COVID-19 vaccination and analyzes these topics based on time, …

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Theory Entity Extraction For Social And Behavioral Sciences Papers Using Distant Supervision, Xin Wei, Lamia Salsabil, Jian Wu Jan 2022

Theory Entity Extraction For Social And Behavioral Sciences Papers Using Distant Supervision, Xin Wei, Lamia Salsabil, Jian Wu

Computer Science Faculty Publications

Theories and models, which are common in scientific papers in almost all domains, usually provide the foundations of theoretical analysis and experiments. Understanding the use of theories and models can shed light on the credibility and reproducibility of research works. Compared with metadata, such as title, author, keywords, etc., theory extraction in scientific literature is rarely explored, especially for social and behavioral science (SBS) domains. One challenge of applying supervised learning methods is the lack of a large number of labeled samples for training. In this paper, we propose an automated framework based on distant supervision that leverages entity mentions …

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A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles Jan 2022

A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles

Computer Science Faculty Publications

[First paragraph] Concerns about the replicability, robustness and reproducibility of findings in scientific literature have gained widespread attention over the last decade in the social sciences and beyond. This attention has been catalyzed by and has likewise motivated a number of large-scale replication projects which have reported successful replication rates between 36% and 78%. Given the challenges and resources required to run high-powered replication studies, researchers have sought other approaches to assess confidence in published claims. Initial evidence has supported the promise of prediction markets in this context. However, they require the coordinated, sustained effort of collections of human experts …

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Exploring Algorithmic Literacy For College Students: An Educator’S Roadmap, Susan Gardner Archambault Jan 2022

Exploring Algorithmic Literacy For College Students: An Educator’S Roadmap, Susan Gardner Archambault

LMU Theses and Dissertations

Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general …

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Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li Jan 2022

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a …

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Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu Dec 2021

Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu

Dissertations

During the past decade, drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug­abuse risk behavior at a population scale, such as among the population of Twitter users, can help to monitor the trend of drug­abuse incidents. However, traditional methods do not effectively detect drug­abuse risk behavior in tweets, mainly due to the sparsity of such tweets and the noisy nature of tweets. In the first part of this dissertation work, the task of classifying tweets as containing drug­abuse risk behavior or not, is studied. Millions of public …

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Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport Dec 2021

Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.

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Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim Dec 2021

Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim

Electronic Theses, Projects, and Dissertations

Automobile collisions occur daily. We now live in an information-driven world, one where technology is quickly evolving. Blockchain technology can change the automotive industry, the safety of the motoring public and its surrounding environment by incorporating this vast array of information. It can place safety and efficiency at the forefront to pedestrians, public establishments, and provide public agencies with pertinent information securely and efficiently. Other industries where Blockchain technology has been effective in are as follows: supply chain management, logistics, and banking. This paper reviews some statistical information regarding automobile collisions, Blockchain technology, Smart Contracts, Smart Cities; assesses the feasibility …

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Analysis And Strategy Of Ai Ethical Problems, Zhaoxiang Zhang, Jiyu Zhang, Tieniu Tan Nov 2021

Analysis And Strategy Of Ai Ethical Problems, Zhaoxiang Zhang, Jiyu Zhang, Tieniu Tan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is the core of the fourth industrial revolution, and it has brought challenges to ethics and social governance. On the basis of explaining the current ethical risks of artificial intelligence, the study furtherly analyzes the current consensus on ethics, governance principles, and governance approaches of artificial intelligence. Moreover, the study also proposes to take "co-construction, co-governance and sharing" as the guiding theory to gradually build a multi-dimensional ethical governance system, including education reform, ethical norms, technical supports, legal regulations, and international cooperation.

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Understanding The Dynamics Of Human Reliance And Trust On Automation, Carlos E. Bustamante Orellana, Lucero Rodriguez Rodriguez, Jordy Cevallos Chavez, Yun Kang Nov 2021

Understanding The Dynamics Of Human Reliance And Trust On Automation, Carlos E. Bustamante Orellana, Lucero Rodriguez Rodriguez, Jordy Cevallos Chavez, Yun Kang

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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Fighting Mass Diffusion Of Fake News On Social Media, Abdallah Musmar Nov 2021

Fighting Mass Diffusion Of Fake News On Social Media, Abdallah Musmar

USF Tampa Graduate Theses and Dissertations

Fake news has been considered one of the most challenging problems in the last few years. The effects of spreading fake news over social media platforms are widely observed across the globe as the depth and velocity of fake news reach far more than real news (Vosoughi et al., 2018). The plan for the following dissertation is to investigate the mass spread of fake news across social media and propose a framework to fight the spread of fake news by mixing preventive methods that could hinder the overall percentage of fake news sharing. We plan to create a study on …

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Fleet Sizing And Allocation For On-Demand Last-Mile Transportation Systems, Karmel Shehadeh, Hai Wang, Peter Zhang Nov 2021

Fleet Sizing And Allocation For On-Demand Last-Mile Transportation Systems, Karmel Shehadeh, Hai Wang, Peter Zhang

Research Collection School Of Computing and Information Systems

The last-mile problem refers to the provision of travel service from the nearest public transportation node to home or other destination. Last-Mile Transportation Systems (LMTS), which have recently emerged, provide on-demand shared transportation. In this paper, we investigate the fleet sizing and allocation problem for the on-demand LMTS. Specifically, we consider the perspective of a last-mile service provider who wants to determine the number of servicing vehicles to allocate to multiple last-mile service regions in a particular city. In each service region, passengers demanding last-mile services arrive in batches, and allocated vehicles deliver passengers to their final destinations. The passenger …

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Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan Oct 2021

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …

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Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers Sep 2021

Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers

Faculty Publications

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …

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Exploring The Personality Of Virtual Tutors In Conversational Foreign Language Practice, Johanna Dobbriner, Cathy Ennis, Robert J. Ross Sep 2021

Exploring The Personality Of Virtual Tutors In Conversational Foreign Language Practice, Johanna Dobbriner, Cathy Ennis, Robert J. Ross

Conference papers

Fluid interaction between virtual agents and humans requires the understanding of many issues of conversational pragmatics. One such issue is the interaction between communication strategy and personality. As a step towards developing models of personality driven pragmatics policies, in this paper, we present our initial experiment to explore differences in user interaction with two contrasting avatar personalities. Each user saw a single personality in a video-call setting and gave feedback on the interaction. Our expectations, that a more extroverted outgoing positive personality would be a more successful tutor, were only partially confirmed. While this personality did induce longer conversations in …

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Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng Aug 2021

Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when …

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Integrating Empirical Analysis Into Analytical Framework: An Integrated Model Structure For On-Demand Transportation, Yuliu Su, Ying Xu, Costas Courcoubetis, Shih-Fen Cheng Aug 2021

Integrating Empirical Analysis Into Analytical Framework: An Integrated Model Structure For On-Demand Transportation, Yuliu Su, Ying Xu, Costas Courcoubetis, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

On-demand transportation services have been developing in an irresistible trend since their first launch in public. These services not only transform the urban mobility landscape, but also profoundly change individuals’ travel behavior. In this paper, we propose an integrated model structure which integrates empirical analysis into a discrete choice based analytical framework to investigate a heterogeneous population’s choices on ownership, usage and transportation mode with the presence of ride-hailing. Distinguished from traditional discrete choice models where individuals’ choices are only affected by exogenous variables and are independent of other individuals’ choices, our model extends to capture the endogeneity of supply …

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How Do You Visit: Identifying Addicts From Large-Scale Transit Records Via Scenario Deep Embedding, Canghong Jin, Dongkai Chen, Zhiwei Lin, Zemin Liu, Minghui Wu Aug 2021

How Do You Visit: Identifying Addicts From Large-Scale Transit Records Via Scenario Deep Embedding, Canghong Jin, Dongkai Chen, Zhiwei Lin, Zemin Liu, Minghui Wu

Research Collection School Of Computing and Information Systems

Identification of individuals based on transit modes is of great importance in user tracking systems. However, identifying users in real-life studies is not trivial owing to the following challenges: 1) activity data containing both temporal and spatial context are high-order and sparse; 2) traditional two-step classifiers depend on trajectory patterns as input features, which limits accuracy especially in the case of scattered and diverse data; 3) in some cases, there are few positive instances and they are difficult to detect. Therefore, approaches involving statistics-based or trajectory-based features do not work effectively. Deep learning methods also suffer from the problem of …

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Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones Jul 2021

Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones

Computer Science Theses & Dissertations

Collections are the tools that people use to make sense of an ever-increasing number of archived web pages. As collections themselves grow, we need tools to make sense of them. Tools that work on the general web, like search engines, are not a good fit for these collections because search engines do not currently represent multiple document versions well. Web archive collections are vast, some containing hundreds of thousands of documents. Thousands of collections exist, many of which cover the same topic. Few collections include standardized metadata. Too many documents from too many collections with insufficient metadata makes collection understanding …

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Methods For Detecting Floodwater On Roadways From Ground Level Images, Cem Sazara Jul 2021

Methods For Detecting Floodwater On Roadways From Ground Level Images, Cem Sazara

Computational Modeling & Simulation Engineering Theses & Dissertations

Recent research and statistics show that the frequency of flooding in the world has been increasing and impacting flood-prone communities severely. This natural disaster causes significant damages to human life and properties, inundates roads, overwhelms drainage systems, and disrupts essential services and economic activities. The focus of this dissertation is to use machine learning methods to automatically detect floodwater in images from ground level in support of the frequently impacted communities. The ground level images can be retrieved from multiple sources, including the ones that are taken by mobile phone cameras as communities record the state of their flooded streets. …

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Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam Jul 2021

Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam

Electrical & Computer Engineering Theses & Dissertations

Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It …

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