Abstract Understanding the connection between topography and the processes and properties that shape landscapes both helps us better understand landscape evolution, and use topography to infer information relevant for a wide range of human activities. Deep learning methods, like convolutional neural networks (CNNs) have been successfully applied to topographic data in geomorphic contexts but primarily for classification problems. This raises the question: could a CNN be trained to ‘learn’ the parameters responsible for forming a particular kind of terrain? As a first test, we train a convolutional neural network to invert topography generated from a landscape evolution model into the model parameters, and interpret the model’s learning. We find that the trained network is able to accurately infer the parameter ratio that governs the degree of landscape dissection, performs better when given topographic derivatives, and has encoded a meaningful relationship between the model parameters and both valley spacing and drainage density.