Deep Learning Predicts California Plant Distributions

This is a California news story, published by EurekAlert!, that relates primarily to * University of California, Berkeley news.

California news

For more California news, you can click here:

more California news

biology news

For more biology news, you can click here:

more biology news

EurekAlert! news

For more news from EurekAlert!, you can click here:

more news from EurekAlert!

About the Otherweb

Otherweb, Inc is a public benefit corporation, dedicated to improving the quality of news people consume. We are non-partisan, junk-free, and ad-free. We use artificial intelligence (AI) to remove junk from your news feed, and allow you to select the best science news, business news, entertainment news, and much more. If you like biology news, you might also like this article about

plant data

. We are dedicated to bringing you the highest-quality news, junk-free and ad-free, about your favorite topics. Please come every day to read the latest deep learning news, deep learning model news, biology news, and other high-quality news about any topic that interests you. We are working hard to create the best news aggregator on the web, and to put you in control of your news feed - whether you choose to read the latest news through our website, our news app, or our daily newsletter - all free!

citizen science data

EurekAlert!

•

Using AI and iNaturalist, scientists build one of the highest resolution maps yet of California plants

Summary

Nutrition label

91% Informative

University of California, Berkeley , scientists have created the highest resolution maps yet of plant distributions in California .

iNaturalist is a widely-used cellphone app that allows people to upload photos and the location data of plants, animals or any other life they encounter.

The researchers used a type of artificial intelligence called a convolutional neural network, which is a deep learning model, to correlate the citizen science data for plants in California with high-resolution satellite or airplane images of the state.

The network discovered correlations that were then used to predict the current range of plant species.

It predicted with 81.4% accuracy the location of redwoods in Redwood National Park in Northern California .

It accurately captured (with R2=0.53 ) the burn severity caused by the 2013 Rim Fire in Yosemite National Park .

"The next question, once we understand the geographic impacts, is, “Are plants going to adapt?’".

VR Score

Informative language

Neutral language

Article tone

semi-formal

Language

English

Language complexity