BQEL-Net: A bimodal quantitative ensemble learning network with uncertainty-aware for vectorized extraction of martian impact craters from bimodal catalogic-and-quantitative dataset (BCQDS)

Current research on bimodal extraction of Martian impact craters, which integrates satellite imagery and digital elevation models (DEMs), remains limited to the object level, with no existing reports about addressing both pixel-level and irregularly vectorized extraction. Furthermore, there is a lack of open-source bimodal datasets that include statistics (such as crater count, area and volume) for Martian impact craters, and no deep learning approaches have been reported for simultaneous extraction and quantitative analysis of Martian impact craters. To address these gaps, this study is the first to create and release a bimodal catalogic and quantitative dataset (BCQDS), combining DEM and satellite imagery with topographic landscape pattern indices. We also propose an innovative bimodal quantitative ensemble learning network (BQEL-Net) with uncertainty-aware paradigm designed for pixel-level, vectorized and quantitative extraction for Martian impact craters from BCQDS. BQEL-Net uses an uncertainty-aware Bayesian ensemble learning strategy (UBELS) by combining multiple strong segmentors confidence and pixel-level confidence to enhance robustness and accuracy; the proposed bimodal coordinate channel attention (BC 2 AM) aligns bimodal features in the latent space, making them complementary; and a newly developed quantitative statistics module (QSM) with a pioneer quantitative task loss function, enabling the simultaneous extraction of up to 140 quantitative indices. Experiments conducted using MOLA data within the BCQDS demonstrated that BQEL-Net achieves a maximum MIoU of 0.858. Ablation studies confirmed that the bimodal structure, BC 2 AM, UBELS and QSM contribute MIoU gains. This work pioneers a crater catalog with 3D geometric morphology to support quantitative studies of Martian surface evolution.