攻击警报:面向实时、高分辨率的导航软件,用于躲避鲸鱼

B. Madon, R. David, L. Pendleton, R. Garello, Ronan Fablet
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引用次数: 4

摘要

在过去的几年里,与船只的碰撞已经成为鲸鱼的主要威胁之一。为了减少捕鲸船的袭击,我们已经开始制定计划,确定鲸鱼可能出现的区域,以便为船只制作实时更新的地图。我们的案例研究设置在地中海,我们的目标是收集所有可用的数据,以使用机器学习技术提高我们对鲸鱼分布的了解。各种各样的数据源(例如,机载卫星上的高分辨率传感器、声学测量、卫星标记、商业船只的直接报告、社交媒体以及流地球观测数据)以及实时和流数据的使用将允许开发高精度、实时的鲸鱼遭遇可能性地图。我们的工作旨在通过超越生态/环境模型,利用全面的数据和机器学习技术,大大改善海洋空间工作。驱动的想法不仅仅是创建可能发生袭击的模型,而是利用所有可用的数据源实时开发高分辨率的鲸鱼遭遇概率地图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strike-alert: Towards real-time, high resolution navigational software for whale avoidance
Over the past few years, it has been shown that collisions with ships have become one of the major threats for whales. In order to reduce whale-ship strikes, we have started to develop schemes for identifying areas where whales are likely to be present in order to produce maps updated in real time for ships. Our case study is set in the Mediterranean Sea and our goal is to gather all the data available to improve our knowledge on whale distribution using machine learning techniques. The wide variety of data sources (e.g. very high resolution sensors on­board satellites, acoustical measurements, satellite tagging, direct reports from commercial ships, and social media along with streaming earth observation data) and the use of real time and streaming data will allow the development of high precision, real time maps of the likelihood of whale encounters. Our work seeks to dramatically improve the marine spatial effort by moving beyond ecological/environmental models to harness the full array of data and machine learning techniques. The driving idea is not to just create models of where strikes are likely to be, but to develop high resolution maps of probability of whale encounters in real time using all available data sources.
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