  Text-to-image (TTI) generative models can be used to generate photorealistic
images from a given text-string input. These models offer great potential to
mitigate challenges to the uptake of machine learning in the earth sciences.
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 over-represented scenic
locations, such as famous rivers and waterfalls, and showed serious under- and
over-representation of many morphological and environmental terms. Despite
biased 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.
