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
- Camera trap image classification
- Analysis of citizen science data
- Environmental sensor network analysis
- "AI empowers conservation biology" (2019): A high-level overview of this problem space. Available here.
- "Climate, biodiversity, and land: using ML to protect and restore ecosystems" (2020): An overview talk, with pointers to practical starting points. Available here.
- "Artificial Intelligence and Conservation" (2019): A curated collection of case studies. Book description here.
Online Courses and Course Materials
Journals and conferences
- PLOS Collections: A collection featuring work on the ecological impacts of climate change, available here.
Societies and organizations
Past and upcoming events
Libraries and Tools
- Camera Trap ML Survey: An overview of datasets used in current camera trap machine learning projects, available here.
- Kwok, Roberta (2019-03-04). "AI empowers conservation biology". Nature. 567: 133–134. doi:10.1038/d41586-019-00746-1.
- Morris, Dan (2020-04-26). "Climate, Biodiversity, and land: Using ML to protect and restore ecosystems". SlidesLive. Retrieved 2020-09-24.
- 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)
- "AI for Conservation | Coastal Resilience". Retrieved 2020-09-24.