Zobrazeno 1 - 10
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pro vyhledávání: '"DAVIES, ANDREW"'
While advances in machine learning with satellite imagery (SatML) are facilitating environmental monitoring at a global scale, developing SatML models that are accurate and useful for local regions remains critical to understanding and acting on an e
Externí odkaz:
http://arxiv.org/abs/2411.14354
Multimodal aerial data are used to monitor natural systems, and machine learning can significantly accelerate the classification of landscape features within such imagery to benefit ecology and conservation. It remains under-explored, however, how th
Externí odkaz:
http://arxiv.org/abs/2410.04833
Autor:
Gordon, Lucia, Behari, Nikhil, Collier, Samuel, Bondi-Kelly, Elizabeth, Killian, Jackson A., Ressijac, Catherine, Boucher, Peter, Davies, Andrew, Tambe, Milind
Publikováno v:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AI for Good. Pages 5977-5985. 2023
Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhinos' movement is crucial to their protection but has unfortunately pro
Externí odkaz:
http://arxiv.org/abs/2409.18104
Autor:
Xu, Lily, Rolf, Esther, Beery, Sara, Bennett, Joseph R., Berger-Wolf, Tanya, Birch, Tanya, Bondi-Kelly, Elizabeth, Brashares, Justin, Chapman, Melissa, Corso, Anthony, Davies, Andrew, Garg, Nikhil, Gaylard, Angela, Heilmayr, Robert, Kerner, Hannah, Klemmer, Konstantin, Kumar, Vipin, Mackey, Lester, Monteleoni, Claire, Moorcroft, Paul, Palmer, Jonathan, Perrault, Andrew, Thau, David, Tambe, Milind
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-2
Externí odkaz:
http://arxiv.org/abs/2307.08774
Autor:
Tran, Mai Khanh1 (AUTHOR) khanhm@lsbu.ac.uk, Davies, Andrew2 (AUTHOR)
Publikováno v:
Journal of Marketing Management. Dec2023, Vol. 39 Issue 17/18, p1765-1799. 35p.
