DeepAI AI Chat
Log In Sign Up

Predicting Landscapes from Environmental Conditions Using Generative Networks

09/23/2019
by   Christian Requena-Mesa, et al.
Max Planck Society
Friedrich-Schiller-Universität Jena
26

Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the beginning of Earth sciences and cast into conceptual models describing the interdependencies of climate, geology, vegetation and geomorphology. Here, we ask whether landscapes, as seen from space, can be statistically predicted from pertinent environmental conditions. To this end we adapted a deep learning generative model in order to establish the relationship between the environmental conditions and the view of landscapes from the Sentinel-2 satellite. We trained a conditional generative adversarial network to generate multispectral imagery given a set of climatic, terrain and anthropogenic predictors. The generated imagery of the landscapes share many characteristics with the real one. Results based on landscape patch metrics, indicative of landscape composition and structure, show that the proposed generative model creates landscapes that are more similar to the targets than the baseline models while overall reflectance and vegetation cover are predicted better. We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies. We envision the application of machine learning as a tool to forecast the effects of climate change on the spatial features of landscapes, while we assess its limitations and breaking points.

READ FULL TEXT

page 6

page 7

page 8

page 12

page 13

04/10/2021

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization

As climate change increases the intensity of natural disasters, society ...
02/16/2023

A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)

We propose a new Tipping Point Generative Adversarial Network (TIP-GAN) ...
04/03/2019

Deep Landscape Features for Improving Vector-borne Disease Prediction

The global population at risk of mosquito-borne diseases such as dengue,...
11/22/2022

Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds

Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme w...
10/22/2019

Establishing an Evaluation Metric to Quantify Climate Change Image Realism

With success on controlled tasks, generative models are being increasing...
09/26/2022

Deep generative model super-resolves spatially correlated multiregional climate data

Super-resolving the coarse outputs of global climate simulations, termed...
10/16/2020

Physics-informed GANs for Coastal Flood Visualization

As climate change increases the intensity of natural disasters, society ...