A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models

Seyed Mohamad Ali Tousi, Ramy Farag, Jacket Demby's, Gbenga Omotara, John A. Lory, G. N. DeSouza; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 469-478

Ephemeral gullies are a primary cause of soil erosion and their reliable accurate and early detection will facilitate significant improvements in the sustainability of global agricultural systems. In our view prior research has not successfully addressed automated detection of ephemeral gullies from remotely sensed images so for the first time we present and evaluate three successful pipelines for ephemeral gully detection. Our pipelines utilize remotely sensed images acquired from specific agricultural areas over a period of time. The pipelines were tested with various choices of Visual Language Models (VLMs) and they classified the images based on the presence of ephemeral gullies with accuracy higher than 70% and a F1-score close to 80% for positive gully detection. Additionally we developed the first public dataset for ephemeral gully detection labeled by a team of soil- and plant-science experts. To evaluate the proposed pipelines we employed a variety of zero-shot classification methods based on State-of-the-Art (SOTA) open-source Vision-Language Models (VLMs). In addition to that we compare the same pipelines with a transfer learning approach. Extensive experiments were conducted to validate the detection pipelines and to analyze the impact of hyperparameter changes in their performance. The experimental results demonstrate that the proposed zero-shot classification pipelines are highly effective in detecting ephemeral gullies in a scenario where classification datasets are scarce.