Automatic Crater Classification Using a Deep‐Learning‐Based Pipeline

Abstract

Abstract Identifying and classifying impact craters on Mars is crucial for understanding the planet's geological history and surface evolution. Traditional crater classification relies on manual annotation methods, which are often limited by human biases and the difficulty of interpreting geomorphological features. Classifying crater is both necessary and challenging. This task is necessary because planetary surfaces ages are estimated by crater density, but some crater types have to be discarded from the analysis to avoid large estimation errors. For instance, secondary impact craters or buried craters should not be counted for a given surface unit. This task is challenging because the different crater classes share a lot of common features. In this study, we present a deep‐learning approach for automated crater classification on Mars using a YOLOv11 architecture. Our method is trained on downsampled (50 m/pixel) Context Camera images (CTX camera) and a human‐annotated crater database of craters 1 km in diameter. This pipeline is one of the first automated tools capable of classifying craters 1 km, addressing a critical gap in high‐resolution analyses. Validated across diverse Martian terrains, it shows potential for planetary dating and comparative geology. Incorporating a pre‐processing step to reduce false positives, we train and test our model globally on separate data sets. Our results demonstrate 81% accuracy, providing a reliable tool for large‐scale planetary surface analysis. This pipeline advances planetary geosciences by enabling systematic, feature‐based crater classification and highlights deep learning's potential in planetary exploration.