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.

文本到图像生成模型在地球科学中具有潜在应用价值，并且需要注意其中的偏见和培养现有偏见的问题。本研究以河流地貌学为案例，评估了训练数据中的主题相关偏见和 Stable Diffusion 模型的性能。虽然训练数据存在偏见，但通过谨慎的提示，Stable Diffusion 模型能够生成保持重要环境和形态特征的逼真合成河流图像。条件控制技术对于提供额外约束也是有效的。然而，在敏感应用中，使用 TTI 模型需要谨慎，并需进行领域特定的训练数据和图像生成偏见评估，以减少现有偏见的固化。

稳定河流：基于文本到图像生成模型的土壤科学应用案例研究