Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23)

Shahroz Tariq; Simon Woo; Daewon Chung; Youjin Shin

doi:10.1145/3583780.3615306

Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23)

Shahroz Tariq
, Simon Woo
, Daewon Chung
, Youjin Shin

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23.

Original language	English
Title of host publication	CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	5294-5295
Number of pages	2
ISBN (Electronic)	9798400701245
DOIs	https://doi.org/10.1145/3583780.3615306
State	Published - 21 Oct 2023
Event	32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom Duration: 21 Oct 2023 → 25 Oct 2023

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/Territory	United Kingdom
City	Birmingham
Period	21/10/23 → 25/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

aerial data
anomaly detection
artificial intelligence
clustering
data mining
deep learning
drones
geospatial analysis
novelty detection
satellite
telemetry data

Access to Document

10.1145/3583780.3615306

Cite this

Tariq, S., Woo, S., Chung, D., & Shin, Y. (2023). Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23). In CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 5294-5295). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3583780.3615306

Tariq, Shahroz ; Woo, Simon ; Chung, Daewon et al. / Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23). CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2023. pp. 5294-5295 (International Conference on Information and Knowledge Management, Proceedings).

@inproceedings{da882c2026ea41fcbe59fd69f72758ca,

title = "Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23)",

abstract = "In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23.",

keywords = "aerial data, anomaly detection, artificial intelligence, clustering, data mining, deep learning, drones, geospatial analysis, novelty detection, satellite, telemetry data",

author = "Shahroz Tariq and Simon Woo and Daewon Chung and Youjin Shin",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 ; Conference date: 21-10-2023 Through 25-10-2023",

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month = oct,

day = "21",

doi = "10.1145/3583780.3615306",

language = "English",

series = "International Conference on Information and Knowledge Management, Proceedings",

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Tariq, S, Woo, S, Chung, D & Shin, Y 2023, Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23). in CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 5294-5295, 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, 21/10/23. https://doi.org/10.1145/3583780.3615306

Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23). / Tariq, Shahroz; Woo, Simon; Chung, Daewon et al.
CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2023. p. 5294-5295 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23)

AU - Tariq, Shahroz

AU - Woo, Simon

AU - Chung, Daewon

AU - Shin, Youjin

PY - 2023/10/21

Y1 - 2023/10/21

N2 - In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23.

AB - In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23.

KW - aerial data

KW - anomaly detection

KW - artificial intelligence

KW - clustering

KW - data mining

KW - deep learning

KW - drones

KW - geospatial analysis

KW - novelty detection

KW - satellite

KW - telemetry data

UR - https://www.scopus.com/pages/publications/85178089378

U2 - 10.1145/3583780.3615306

DO - 10.1145/3583780.3615306

M3 - Conference contribution

AN - SCOPUS:85178089378

T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 5294

EP - 5295

BT - CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023

Y2 - 21 October 2023 through 25 October 2023

ER -

Tariq S, Woo S, Chung D, Shin Y. Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23). In CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery. 2023. p. 5294-5295. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/3583780.3615306

Anomaly and Novelty detection for Satellite and Drone systems (ANSD'23)

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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