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Shyam Boriah

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3 papers

1 author row

AAAI Conference 2013 Conference Paper

Multiple Hypothesis Object Tracking For Unsupervised Self-Learning: An Ocean Eddy Tracking Application

James Faghmous
Muhammed Uluyol
Luke Styles
Matthew Le
Varun Mithal
Shyam Boriah
Vipin Kumar

Mesoscale ocean eddies transport heat, salt, energy, and nutrients across oceans. As a result, accurately identifying and tracking such phenomena are crucial for understanding ocean dynamics and marine ecosystem sustainability. Traditionally, ocean eddies are monitored through two phases: identiﬁcation and tracking. A major challenge for such an approach is that the tracking phase is dependent on the performance of the identiﬁcation scheme, which can be susceptible to noise and sampling errors. In this paper, we focus on tracking, and introduce the concept of multiple hypothesis assignment (MHA), which extends traditional multiple hypothesis tracking for cases where the features tracked are noisy or uncertain. Under this scheme, features are assigned to multiple potential tracks, and the ﬁnal assignment is deferred until more data are available to make a relatively unambiguous decision. Unlike the most widely used methods in the eddy tracking literature, MHA uses contextual spatio-temporal information to take corrective measures autonomously on the detection step a posteriori and performs signiﬁcantly better in the presence of noise. This study is also the ﬁrst to empirically analyze the relative robustness of eddy tracking algorithms.

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AAAI Conference 2012 Conference Paper

A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring

James Faghmous
Yashu Chamber
Shyam Boriah
Frode Vikebø
Stefan Liess
Michel dos Santos Mesquita
Vipin Kumar

Swirls of ocean currents known as ocean eddies are a crucial component of the ocean’s dynamics. In addition to dominating the ocean’s kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60 − 80◦ N and 20◦ W − 20◦ E), an area that has received limited attention and has proven to be difficult to analyze using other methods.

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TIST Journal 2011 Journal Article

Monitoring global forest cover using data mining

Varun Mithal
Ashish Garg
Shyam Boriah
Michael Steinbach
Vipin Kumar
Christopher Potter
Steven Klooster
Juan Carlos Castilla-Rubio

Forests are a critical component of the planet's ecosystem. Unfortunately, there has been significant degradation in forest cover over recent decades as a result of logging, conversion to crop, plantation, and pasture land, or disasters (natural or man made) such as forest fires, floods, and hurricanes. As a result, significant attention is being given to the sustainable use of forests. A key to effective forest management is quantifiable knowledge about changes in forest cover. This requires identification and characterization of changes and the discovery of the relationship between these changes and natural and anthropogenic variables. In this article, we present our preliminary efforts and achievements in addressing some of these tasks along with the challenges and opportunities that need to be addressed in the future. At a higher level, our goal is to provide an overview of the exciting opportunities and challenges in developing and applying data mining approaches to provide critical information for forest and land use management.

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