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Towards Explainable Machine Learning...

Sarkar, Argho,

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Towards Explainable Machine Learning Models for Remote Sensing: Multi-modal and Uni-Modal Applications for Natural Disaster /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Explainable Machine Learning Models for Remote Sensing: Multi-modal and Uni-Modal Applications for Natural Disaster // Argho Sarkar.
Author:	Sarkar, Argho,
Description:	1 electronic resource (158 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Geographic information science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31242104
ISBN:	9798382748610

Towards Explainable Machine Learning Models for Remote Sensing: Multi-modal and Uni-Modal Applications for Natural Disaster /
Sarkar, Argho,

Towards Explainable Machine Learning Models for Remote Sensing: Multi-modal and Uni-Modal Applications for Natural Disaster /Argho Sarkar. - 1 electronic resource (158 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Natural disasters leave a path of devastation that must be managed efficiently to minimize their impact on human lives. Estimation of the damages and taking action based on those assessments are the most important two-step processes in post-disaster management efforts. With the recent progress of Artificial Intelligence (AI), many machine learning algorithms are utilizing to assess the damage. However, existing methods are less efficient and provide limited scene information. This thesis proposes a unique Vision-Language-Based multi-modal task namely Visual Question Answering (VQA) for efficient and comprehensive damage assessment. VQA enables the extraction of diverse information from images through natural language queries. This high-level scene information has the potential to optimize decision support systems, leading to increased efficiency and a reduction in the time required for search and rescue operations. On the other hand, when incorporating machine learning models into smart decision support systems, the issue of explainability in model outcomes becomes significant. In remote sensing, visual content is complex, and the available contextual information is often limited compared to the overall size of the images. In such scenarios, the model's performance may be susceptible to shortcut learning and lead to misleading results. Thus, ensuring proper explanations for model outputs becomes crucial.Motivated by the above issues, this thesis is dedicated to addressing two crucial aspects of remote sensing applications. Firstly, this thesis focused on developing an image-based question-answering framework for efficient damage assessment on remote sensing imagery. Secondly, this thesis focused on enhancing the trustworthiness of model outcomes by developing novel machine learning frameworks designed for remote sensing applications in multi-modal and uni-modal contexts. To achieve the first goal, two unique large-scale benchmark visual question-answering datasets for damage assessment namely FloodNet-VQA and RescueNet-VQA are proposed. For proper visual explanations of model outcomes, this thesis proposed novel supervised attention modules for the VQA. Proposed supervised attention modules provide auxiliary supervision in the attention-obtaining process so that the model can learn where to focus in the image content for a question to provide a rational answer. Proposed approaches showed improved explanations and achieved higher accuracy compared to the state-of-the-art VQA algorithms. Finally, for consistent and robust visual explanations in a uni-modal remote sensing task (e.g., image classification) a novel strategy is proposed. Within this framework, two distinct losses have been proposed to ensure consistency and robustness in visual explanations.

English

ISBN: 9798382748610Subjects--Topical Terms:

3432445
Geographic information science.
Subjects--Index Terms:

Computer vision

Towards Explainable Machine Learning Models for Remote Sensing: Multi-modal and Uni-Modal Applications for Natural Disaster /
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500 $a Advisors: Gangopadhyay, Aryya; Rahnemoonfar, Maryam Committee members: Wang, Jianwu; Gani, Md. Osman; Mohsenin, Tinoosh.
502 $b Ph.D. $c University of Maryland, Baltimore County $d 2024.
520 $a Natural disasters leave a path of devastation that must be managed efficiently to minimize their impact on human lives. Estimation of the damages and taking action based on those assessments are the most important two-step processes in post-disaster management efforts. With the recent progress of Artificial Intelligence (AI), many machine learning algorithms are utilizing to assess the damage. However, existing methods are less efficient and provide limited scene information. This thesis proposes a unique Vision-Language-Based multi-modal task namely Visual Question Answering (VQA) for efficient and comprehensive damage assessment. VQA enables the extraction of diverse information from images through natural language queries. This high-level scene information has the potential to optimize decision support systems, leading to increased efficiency and a reduction in the time required for search and rescue operations. On the other hand, when incorporating machine learning models into smart decision support systems, the issue of explainability in model outcomes becomes significant. In remote sensing, visual content is complex, and the available contextual information is often limited compared to the overall size of the images. In such scenarios, the model's performance may be susceptible to shortcut learning and lead to misleading results. Thus, ensuring proper explanations for model outputs becomes crucial.Motivated by the above issues, this thesis is dedicated to addressing two crucial aspects of remote sensing applications. Firstly, this thesis focused on developing an image-based question-answering framework for efficient damage assessment on remote sensing imagery. Secondly, this thesis focused on enhancing the trustworthiness of model outcomes by developing novel machine learning frameworks designed for remote sensing applications in multi-modal and uni-modal contexts. To achieve the first goal, two unique large-scale benchmark visual question-answering datasets for damage assessment namely FloodNet-VQA and RescueNet-VQA are proposed. For proper visual explanations of model outcomes, this thesis proposed novel supervised attention modules for the VQA. Proposed supervised attention modules provide auxiliary supervision in the attention-obtaining process so that the model can learn where to focus in the image content for a question to provide a rational answer. Proposed approaches showed improved explanations and achieved higher accuracy compared to the state-of-the-art VQA algorithms. Finally, for consistent and robust visual explanations in a uni-modal remote sensing task (e.g., image classification) a novel strategy is proposed. Within this framework, two distinct losses have been proposed to ensure consistency and robustness in visual explanations.
546 $a English
590 $a School code: 0434
650 4 $a Geographic information science. $3 3432445
650 4 $a Remote sensing. $3 535394
650 4 $a Computer science. $3 523869
653 $a Computer vision
653 $a Damage assessment
653 $a Explainability
653 $a Multi-modal machine learning
653 $a Visual Question Answering
690 $a 0800
690 $a 0984
690 $a 0799
690 $a 0370
710 2 $a University of Maryland, Baltimore County. $b Information Systems. $e degree granting institution. $3 3825427
720 1 $a Gangopadhyay, Aryya $e degree supervisor.
720 1 $a Rahnemoonfar, Maryam $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0434
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31242104