TransRes: A Deep Transfer Learning Approach to Migratable Image Super-Resolution in Remote Urban Sensing

Zhang, Yang; Zong, Ruohan; Han, Jun; Zhang, Dongke; Rashid, Tahmid; Wang, Dong

doi:10.1109/secon48991.2020.9158410

Cited by 13 publications

(7 citation statements)

References 35 publications

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“…Existing literature on social sensing has proposed methods to overcome the noise from social data platforms with techniques such as machine learning (ML) (Nur'Aini et al, 2015), artificial neural networks (ANNs) (Jagannatha and Yu 2016), estimation theory (Wang et al, 2019a), and adaptive sampling (Zhang et al, 2018h). Studies on physical sensors have proposed methods to reduce sensor noise using approaches like image enhancement with super-resolution (Zhang et al, 2020e), deep learning-driven noise reduction (Lai et al, 2018), and graph neural network-based data extrapolation (Wang et al, 2014d). However, such standalone approaches fail to address the intrinsic interdependence between the noise from social and physical signals in SPS, which is non-trivial to quantify and model.…”

Section: Data Collection Challengementioning

confidence: 99%

“…Locating raw sensor data from numerous social and physical sensors (Nur'Aini et al, 2015;Wang et al, 2019a;Zhang et al, 2018h;Jagannatha and Yu 2016;Zhang et al, 2020e;Lai et al, 2018;Wang et al, 2014d;Zhang et al, 2020aZhang et al, , 2011Heydon and Hunn 2012;Johnsen et al, 2018;Hull et al, 2003) • How to systematically locate useful data from inherently noisy social and physical signals?…”

Section: Data Collection Challengementioning

confidence: 99%

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A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

Rashid

Wei

Wang

2023

Collective Intelligence

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing’s effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the “state of the world,” both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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Section: Data Collection Challengementioning

confidence: 99%

Section: Data Collection Challengementioning

confidence: 99%

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

Rashid

Wei

Wang

2023

Collective Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing literature on social sensing has proposed methods to overcome the noise from social data platforms with techniques such as machine learning (ML) [162], artificial neural networks (ANNs) [163], estimation theory [20], and adaptive sampling [164]. Studies on physical sensors have proposed methods to reduce sensor noise using approaches like image enhancement with super-resolution [165], deep learning driven noise reduction [166], and graph neural network-based data extrapolation [167]. However, such standalone approaches fail to address the intrinsic interdependence between the noise from social and physical signals in SPS, which is non-trivial to quantify and model.…”

Section: Figure 14: Illustration Of Data Collection Challenge In Sps ...mentioning

confidence: 99%

“…In particular, the physical sensing applications rely on the capabilities of hardware sensing devices (e.g., cameras, UAVs, and robots), while the social sensing applications obtain observations from human sensors through crowdsensing and social media by implicitly leveraging user devices (e.g., connected tablets, laptops, and smartphones). Such devices have distinct characteristics in terms of sensing and computation capabilities, sensitivity, power requirements, frequency of data capture, communication protocols, access control and authentication methods, and runtime environments [25], [181], [183]- [185], which often presents a unique difficulty in managing them in SPS applications. For example, in the SAS application of Figure 2 in Section I [16], smartphones capture human observations and send them to social media platforms which are then used to dispatch UAVs to recover the veracity of the reports.…”

Section: Device and Data Heterogeneity Challengementioning

confidence: 99%

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

Rashid¹,

Wei²,

Wang³

2021

Preprint

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting the real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the occurrences in the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates the social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing innovative and practical SPS systems, which require careful scrutiny and research to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and the road-map for future work. While existing survey papers related to sensing have discussed sensing schemes that are partly related to SPS, they do not explicitly provide an extensive overview of the SPS paradigm or the state-of-the-art SPS schemes. This paper intends to bridge this knowledge gap by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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“…While data collection is an intrinsic challenge in using social sensing for tracking the COVID-19 spread, a greater difficulty exists in processing the rapidly generated incoming signals consisting of multitudes of features or dimensions . This challenge is identified as data modality in social sensing where large amounts of unfiltered and unstructured data with multiple modalities need to be processed (Chu et al 2016;Zhang et al 2019dZhang et al , 2020bShang et al 2019a). Specifically, data modality refers to the different variety or types of data prevalent in the social media such as text, image, location, audio, and video (Birke et al 2014).…”

Section: Data Modality Challengementioning

confidence: 99%

CovidSens: a vision on reliable social sensing for COVID-19

Rashid

Wang

2020

Artif Intell Rev

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With the spiraling pandemic of the Coronavirus Disease 2019 , it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

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TransRes: A Deep Transfer Learning Approach to Migratable Image Super-Resolution in Remote Urban Sensing

Cited by 13 publications

References 35 publications

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

CovidSens: a vision on reliable social sensing for COVID-19

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