Stable rivers: A case study in the application of text-to-image generative models for Earth sciences

Abstract

Text-to-image (TTI) generative models can be used to generate photorealistic images from a given text-string input. However, the rapid increase in their use has raised questions about fairness and biases, with most research to date focusing on social and cultural areas rather than domain-specific considerations. We conducted a case study for the Earth sciences, focusing on the field of fluvial geomorphology, where we evaluated subject-area-specific biases in the training data and downstream model performance of Stable Diffusion (v1.5). In addition to perpetuating Western biases, we found that the training data overrepresented scenic locations, such as famous rivers and waterfalls, and showed serious underrepresentation and overrepresentation of many morphological and environmental terms. Despite biassed training data, we found that with careful prompting, the Stable Diffusion model was able to generate photorealistic synthetic river images reproducing many important environmental and morphological characteristics. Furthermore, conditional control techniques, such as the use of condition maps with ControlNet, were effective for providing additional constraints on output images. Despite great potential for the use of TTI models in the Earth sciences field, we advocate for caution in sensitive applications and advocate for domain-specific reviews of training data and image generation biases to mitigate perpetuation of existing biases.