The 1st International Workshop on
Anomaly and Novelty detection in Satellite and Drones systems
(ANSD '23)
—
co-located with CIKM 2023
Birmingham, UK
October 22, 2023
Alan Walters – RM 103
(2:00PM-5:30PM)
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Overview
Overview
The workshop on Anomaly and Novelty Detection in Drones and Satellite data at CIKM 2023 aims to bring together researchers, practitioners, and industry experts to discuss the latest advancements and challenges in detecting anomalies and novelties in drone and satellite data. With the increasing availability of such data, the workshop seeks to explore the potential of machine learning and data mining techniques to enable the timely and accurate detection of unexpected events or changes. The workshop will include presentations of research papers, keynote talks, panel discussions, and poster sessions, with a focus on promoting interdisciplinary collaboration and fostering new ideas for tackling real-world problems.
Keynote Speakers
Keynote Speakers
We are excited to announce the following keynote talks.
-Dr. Rose Yu, Assistant Professor, UCSD, Department of Computer Science and Engineering
Title: Deep Spatiotemporal Point Processes for Anomaly Detection and Beyond
Abstract: Accurate modeling of spatiotemporal events is critical for disaster response, logistic optimization and human mobility. Compared to traditional sequence data such as texts or time series, spatiotemporal events occur irregularly with uneven time and space intervals. Existing statistical approaches based on Spatiotemporal point processes (STPP) often require strong modeling assumptions, feature engineering, and can be computationally expensive. In this talk, I will describe a framework that integrates fundamental concepts from STPP with deep learning, leading to flexible, interpretable, highly efficient models for spatiotemporal events . I will showcase the applications of our framework to event-based forecasting, inference and anomaly detection tasks.
-Dr. Daewon Chung, Executive Director, National Satellite Operation & Application Center, Korea Aerospace Research Institute (KARI)
Title: Operation, Application and AI of Satellite in NSOAC
Abstract: The KARI(Korea Aerospace Research Institute) was established on October 10, 1989 as a government funded research institute under the Ministry of Science and ICT. Since its inception, The KARI has made remarkable achievements in the area of aerospace over the short period of time. The NSOAC(National Satellite Operation and Application Center) was established to promote the operation of national satellites and the use of satellite information. The NSOAC has been researching lots of works related on anomaly and novelty detection in the area of satellite, ground system and satellite image. Nowadays, many satellites have been launching and operating efficiently. Therefore, data coming from satellites, ground systems and kinds of payload is increasingly becoming bigdata. Focus of research on anomaly and novelty detection has been changing from traditional analysis and modeling into new artificial intelligence technology using bigdata. So, various kinds of AI algorithm have been researching appropriateness and effectiveness of use on anomaly and novelty detection in the area of space application.
Topics
Topics
We invite submissions of original contributions on topics related to Anomaly detection and Novelty detection for Drones or Satellites data and systems. The scope of the workshop includes, but is not limited to, the following areas, including time-series and vision data:
We invite submissions of original contributions on topics related to Anomaly detection and Novelty detection for Drones or Satellites data and systems. The scope of the workshop includes, but is not limited to, the following areas, including time-series and vision data:
Anomaly detection in drone and satellite data: This includes techniques for identifying data points that deviate from the norm or are rare, such as outliers or anomalies.
Novelty detection in drone and satellite data: This involves techniques for identifying data points that are significantly different from the previously seen data, such as emerging events or new patterns.
Classification and clustering of drone and satellite data: These techniques involve grouping similar data points together, which can help to identify patterns and anomalies.
Deep learning for anomaly and novelty detection in drone and satellite data: These techniques involve the use of neural networks to automatically learn representations and identify anomalies and novelties.
Geospatial analysis of drone and satellite data: This includes methods for analyzing the spatial and temporal patterns of drone and satellite data, and for visualizing the results.
Applications of anomaly and novelty detection in drone and satellite data: This includes use cases such as disaster management, precision agriculture, environmental monitoring, urban planning, and others.
Data preprocessing and cleaning for drone and satellite data: This involves techniques for handling the noise, missing values, and outliers that are commonly found in drone and satellite data.
Evaluation and benchmarking of anomaly and novelty detection algorithms: This includes methods for measuring the performance of different algorithms, and for comparing their results.
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