Biodiversity

From Climate Change AI Wiki
Jump to navigation Jump to search

This page is about the intersection of ecosystems and machine learning in the context of climate change adaptation. For an overview of biodiversity monitoring as a whole, please see the Wikipedia page on this topic.

Changes in climate are increasingly affecting the distribution and composition of ecosystems. This has profound implications for global biodiversity, as well as agriculture, disease, and natural resources such as wood and fish. Machine learning can help by supporting efforts to monitor ecosystems and biodiversity.

Machine Learning Application Areas

Ecology

  • Camera trap image classification
  • Analysis of citizen science data
  • Environmental sensor network analysis

Background Readings

Primers

  • "AI empowers conservation biology" (2019)[1]: A high-level overview of this problem space. Available here.
  • "Climate, biodiversity, and land: using ML to protect and restore ecosystems" (2020)[2]: An overview talk, with pointers to practical starting points. Available here.

Textbooks

  • "Artificial Intelligence and Conservation" (2019)[3]: A curated collection of case studies. Book description here.

Other

  • Natural Solutions Toolkit[4]: Example in-the-field implementations. Available here.

Online Courses and Course Materials

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and Tools

Data

References

  1. Kwok, Roberta (2019-03-04). "AI empowers conservation biology". Nature. 567: 133–134. doi:10.1038/d41586-019-00746-1.
  2. Morris, Dan (April 216, 2020). "Climate, Biodiversity, and land: Using ML to protect and restore ecosystems". SlidesLive. Retrieved 2020-09-24. Invalid |url-status=https://slideslive.com/38926837/tackling-climate-change-with-ml?time=40751s (help); Check date values in: |date= (help)
  3. Fang, Fei; Tambe, Milind; Dilkina, Bistra; Plumptre, Andrew J., eds. (2019-02-28). Artificial Intelligence and Conservation (1 ed.). Cambridge University Press. doi:10.1017/9781108587792. ISBN 978-1-108-58779-2.CS1 maint: date and year (link)
  4. "AI for Conservation | Coastal Resilience". Retrieved 2020-09-24.