A risk index methodology for potentially polluting marine sites

Masetti, Giuseppe; Calder, Brian R.

doi:10.1007/s10669-014-9504-7

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…MacIntyre (2014) applies risk analysis and epidemiology to the recent outbreak of Middle East respiratory syndrome coronavirus in an attempt to characterize the nature of the source of the infections. Masetti and Calder (2014) describe a risk-based index approach that was developed to assess the risks of potentially polluting marine sites. Next, Birisan and Beling (2014) apply a multiinstance learning algorithm to image retrieval with the goal of filtering and processing images that will be of higher interest to users, thus reducing the cognitive burden caused by searching through large data sets.…”

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Introduction to the inaugural general issue of environment systems and decisions

2014

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confidence: 99%

Introduction to the inaugural general issue of environment systems and decisions

2014

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A Bayesian marine debris detector using existing hydrographic data products

Masetti

Calder

2015

OCEANS 2015 - Genova

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Natural disasters can represent a massive source of marine debris deposition along the coastline due to strong winds, heavy rainfall, and storm surge. Such an extensive amount of debris of different size, shape, and materials could threaten navigation, natural resources, and/or human safety. After a disaster (e.g., hurricane, typhoon, tsunami, etc.), effectively and quickly processing large amounts of hydrographic data, collected using commercial systems for detection and classification of marine debris, would be highly advantageous to the necessary remediation operations. Such a task involves some degree of modeling and approximation to make the analysis computationally attractive and sufficiently effective in practice (i.e., an approximate solution with a wellstructured model is preferred to an exact solution with a suboptimal model).For this reason, a target model was built postulating a simplified description of the object properties, and a detector was specifically outlined for marine debris, detecting discrete objects which differ (e.g., protrude) from the surrounding seafloor, being close or connected to the bottom. The scope of the detector was also constrained to analyze products commonly available in existing post processing software (mainly, bathymetric digital terrain models (DTM) and backscatter mosaics with several associated data sets, such as statistics derived from the core data, or during construction) so that the technique may be quickly inserted into existing workflows, which eases resource management in a response situation.For the backscatter mosaic, as in many existing algorithms, target detection is based on the observation that denser material (often anthropogenic) makes debris returns much stronger than the surrounding background.However, the often used approach to object detection through simple thresholding (e.g., based on the premise that on a mosaic the object return is brighter than the background) was modified since it tends to fail when the background is textured (i.e., detectors are not aware of image correlation). For the bathymetric DTM, a few

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A risk index methodology for potentially polluting marine sites

Cited by 2 publications

References 27 publications

Introduction to the inaugural general issue of environment systems and decisions

Introduction to the inaugural general issue of environment systems and decisions

A Bayesian marine debris detector using existing hydrographic data products

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