# Machine Learning for Earth Science ## AI for Earth Science * [Dymaxionlabs](https://dymaxionlabs.com/platform/) * [School Mapping Github by DymaxionLabs](https://github.com/dymaxionlabs/school-mapping) ## Climate * [Climate indices](https://github.com/monocongo/climate_indices) (BSD-3-Clause) : Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research. * [MIT Climate CoLab](https://www.climatecolab.org/) : A collaborative online community centered around a series of annual contests that seek out promising ideas for fighting climate change. * [Climate Action Challenge](https://challenge.whatdesigncando.com) : global design competition calling on the creative community to submit bold, innovative solutions to combat the impacts of climate change. * Improving Weather Prediction with CNN: J. A. Weyn, D. R. Durran, and R. Caruana, “Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere,”Journal of Advances in Modeling Earth Systems, vol. 12, no. 9, Sep. 2020,ISSN: 1942-2466.doi:10.1029/2020ms002109.\[Online]. Available:. Code: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://irosyadi.gitbook.io/irosyadi/machine-learning/ml-earth.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.