Rapid detection and mapping of landforms are crucially important to improve our understanding of past and presently active processes across the earth, especially, in complex and dynamic volcanoes. Traditional landform modeling approaches are labor-intensive and time-consuming. In recent years, landform mapping has increasingly been digitized. This study conducted an in-depth analysis of convolutional neural networks (CNN) in combination with geographic object-based image analysis (GEOBIA), for mapping volcanic and glacial landforms. Sentinel-2 image, as well as predisposing variables (DEM and its derivatives, e.g., slope, aspect, curvature and flow accumulation), were segmented using a multi-resolution segmentation algorithm, and relevant features were selected to define segmentation scales for each landform category. A set of object-based features was developed based on spectral (e.g., brightness), geometrical (e.g., shape index), and textural (grey level co-occurrence matrix) information. The landform modelling networks were then trained and tested based on labelled objects generated using GEOBIA and ground control points. Our results show that an integrated approach of GEOBIA and CNN achieved an ACC of 0.9685, 0.9780, 0.9614, 0.9767, 0.9675, 0.9718, 0.9600, and 0.9778 for dacite lava, caldera, andesite lava, volcanic cone, volcanic tuff, glacial circus, glacial valley, and suspended valley, respectively. The quantitative evaluation shows the highest performance (Accuracy > 0.9600 and cross-validation accuracy > 0.9400) for volcanic and glacial landforms and; therefore, is recommended for regional and large-scale landform mapping. Our results and the provided automatic workflow emphasize the potential of integrated GEOBIA and CNN for fast and efficient landform mapping as a first step in the earth's surface management.
© 2022. The Author(s).