A scoping review on the use, processing and fusion of geographic data in virtual assistants

Granell, Carlos; Pesántez-Cabrera, Paola; Vilches‐Blázquez, Luis M.; Achig, Rosario; Luaces, Miguel R.; Cortiñas-Álvarez, Alejandro; Chayle, Carolina; Morocho, Villie

doi:10.1111/tgis.12720

Cited by 10 publications

(7 citation statements)

References 56 publications

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“…Compared with QA, GeoQA is a smaller research topic which starts attracting attentions from QA researchers as well as GIScientists only recently. A recent review on the usage of geospatial information in virtual assistants (Granell et al, 2021) also showed that the usage of different types of geographic data and various spatial methods in virtual assistants is quite limited. How we can show the unique contribution of GeoQA to the general QA community is the golden question needed to be answered for GIScientists.…”

Section: Discussion About the Question Classificationmentioning

confidence: 99%

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Mai

Janowicz

Zhu

et al. 2021

AGILE GIScience Ser.

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Abstract. As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

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Section: Discussion About the Question Classificationmentioning

confidence: 99%

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Mai

Janowicz

Zhu

et al. 2021

AGILE GIScience Ser.

View full text Add to dashboard Cite

show abstract

“…Compared with QA, GeoQA is a smaller research topic which starts attracting attentions from QA researchers as well as GIScientists only recently. A recent review on the usage of geospatial information in virtual assistants (Granell et al, 2021) also showed that the usage of different types of geographic data and various spatial methods in virtual assistants is quite limited. How we can show the unique contribution of GeoQA to the general QA com-14 http://nlpprogress.com/english/question_answering.html munity is the golden question needed to be answered for GIScientists.…”

Section: Discussion About the Question Classificationmentioning

confidence: 99%

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Mai

Janowicz

Zhu

et al. 2021

Preprint

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As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

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“…While there have been advancements in artificial intelligence and geospatial analysis, there is still a need for topographical field data collection by professional surveyors, indicating technological limitations (Levin et al, 2020). Additionally, the study by highlights gaps and limitations in the use of spatial methods, standards, and resources available in spatial data infrastructures to develop intelligent decision systems based on virtual assistants for various application domains, emphasizing the existing technological barriers (Granell et al, 2021). Ethical and legal implications also present challenges in the application of geospatial intelligence for urban security enhancement.…”

Section: Challenges and Limitationsmentioning

confidence: 95%

“…GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data (Li, 2020). This has significant implications for urban security, as it enables the development of intelligent decision systems based on virtual assistants, contributing to the psychological security of urban residents (Granell et al, 2021;.…”

Section: Future Outlookmentioning

confidence: 99%

The Evolving Role of Geospatial Intelligence in Enhancing Urban Security: A Review of Applications and Outcomes

Preye Winston Biu,

Johnson Sunday Oliha,

Ogagua Chimezie Obi

2024

Eng. sci. technol. j.

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Geospatial intelligence (GEOINT) has emerged as a pivotal tool in addressing the complexities of urban security, offering insights crucial for effective decision-making and resource allocation in the face of evolving threats. This review examines the multifaceted applications and outcomes of GEOINT in enhancing urban security, encompassing its utilization in surveillance, risk assessment, disaster management, and infrastructure protection. The proliferation of advanced satellite imaging, aerial photography, and geographic information systems (GIS) has revolutionized urban security efforts. Through the integration of various data sources, GEOINT enables the comprehensive mapping and monitoring of urban landscapes, facilitating the identification of vulnerable areas and potential security risks. This proactive approach empowers authorities to preemptively deploy resources and implement targeted interventions, thereby mitigating potential threats before they escalate. Moreover, GEOINT plays a pivotal role in enhancing situational awareness during emergencies and crises. By providing real-time updates on evolving situations, including natural disasters or terrorist activities, GEOINT facilitates rapid response and coordination among emergency responders. Furthermore, its ability to overlay spatial data with demographic and socio-economic information enables a nuanced understanding of the urban environment, ensuring more effective emergency management strategies tailored to specific community needs. Furthermore, GEOINT is instrumental in safeguarding critical urban infrastructure, such as transportation networks, power plants, and water supply systems, from potential security breaches. Through comprehensive spatial analysis, vulnerabilities within infrastructure networks can be identified, and security measures can be optimized to mitigate risks and ensure resilience against potential threats. This review also explores the emerging trends and future directions in the utilization of GEOINT for urban security, including the integration of artificial intelligence and machine learning algorithms for predictive analytics and anomaly detection. Additionally, the ethical and privacy implications associated with the widespread adoption of GEOINT in urban security are discussed, emphasizing the importance of responsible data governance and transparency. The evolving role of GEOINT in enhancing urban security underscores its indispensability in addressing contemporary security challenges. By leveraging spatial data and advanced analytics, GEOINT offers a comprehensive and proactive approach to safeguarding urban environments, thereby ensuring the safety and resilience of communities in an increasingly complex threat landscape. Keywords: Intelligence, Security, Geospatial, Technology, Review.

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A scoping review on the use, processing and fusion of geographic data in virtual assistants

Cited by 10 publications

References 56 publications

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

The Evolving Role of Geospatial Intelligence in Enhancing Urban Security: A Review of Applications and Outcomes

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