LU5M812TGT: An AI-Powered Global Database of impact craters >=0.4 km on the Moon.  (link: LU5M812TGT)

This dataset presents the results of a groundbreaking AI-driven lunar crater mapping project, marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:


The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities. This dataset can serve as a critical tool for both astrophysicists and AI researchers. By providing labelled crater data with precise coordinates, dimensions, and classifications, it offers a benchmark for comparative studies, model validation, and further innovations in celestial object detection. Available on zenodo https://zenodo.org/records/13990480