Difference between revisions of "Animal Ecology"

Revision as of 06:46, 20 February 2020

There is increasing demand for efficient ways to process large volumes of data from visual-based remote-technology, such as unmanned aerial vehicles (UAVs) in ecology and conservation, with machine learning methods representing a promising avenue to address varying user demands. Here, we evaluated current trends in how machine learning and UAVs are used to process imagery data for detecting animals and vegetation across habitats, placing emphasis on their utility for endangered species. Importance of machine learning for enhancing ecological studies using information-rich imagery | Antoine M. Dujon, Gail Schofield

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 * [[Case Studies]]
 * [http://www.nextgov.com/emerging-tech/2020/02/noaa-use-microsoft-ai-advance-protection-endangered-species/163208/ NOAA to Use Microsoft AI to Advance Protection of Endangered Species |  Brandi Vincent - Nextgov]
+* [http://deepmind.com/blog/article/using-machine-learning-to-accelerate-ecological-research Using machine learning to accelerate ecological research | S. Petersen, M. Palmer, U. Paquet, and P. Kohli - Deepmind]
-Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. In this competition, you will address when the earthquake will take place. Specifically, you’ll predict the time remaining before laboratory earthquakes occur from real-time seismic data. If this challenge is solved and the physics are ultimately shown to scale from the laboratory to the field, researchers will have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure. This challenge is hosted by Los Alamos National Laboratory which enhances national security by ensuring the safety of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
+There is increasing demand for efficient ways to process large volumes of data from visual-based remote-technology, such as unmanned aerial vehicles (UAVs) in ecology and conservation, with machine learning methods representing a promising avenue to address varying user demands. Here, we evaluated current trends in how machine learning and UAVs are used to process imagery data for detecting animals and vegetation across habitats, placing emphasis on their utility for endangered species. [http://www.int-res.com/abstracts/esr/v39/p91-104/ Importance of machine learning for enhancing ecological studies using information-rich imagery | Antoine M. Dujon, Gail Schofield]
 <youtube>HR0ppRIkq1o</youtube>
 <youtube>7G45uDZVhC8</youtube>

Difference between revisions of "Animal Ecology"

Revision as of 06:46, 20 February 2020

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