We introduce a feature descriptor based on stochastic differences between random paths in spatially extended data, like an image. This descriptor behaves like a "texture gradient" and it is sensitive to both spatial and data scales. Applications to inferring characteristic scales in remotely sensed data is presented, together with multi-scale image analysis, and color edge-detection. The method is generic and can be applied to other types of vector or structured data.