Artificial Neural Networks (ANNs) promise improvements in autonomous navigation perfor- mances for future robotic space missions in unstructured and irregular planetary environments. This paper describes an AI-Development-Framework (AIDF) based on workflows for developing, training, and validating ANNs to improve the performance of Guidance, Navigation, and Control (GNC)-algorithms by corresponding ANNs. Further, the AIDF includes a high-fidelity dataset generator for 2D image data, 3D point cloud, and 3D distance data (depth images) inspired by 67P/Churyumov-Gerasimenko comet’s surface. For tests of the AIDF and the simulated synthetic high-fidelity dataset, Convolutional Neural Networks (CNNs) for semantic segmentation tasks in grayscale 2D and corresponding depth images were trained and validated, which follows mostly an encoder-decoder structure, such as the YOLOv8 and the Deeplabv3+ with different backbone nets, such as ResNet, ResNet-RS, and the YOLO-NAS. This paper also includes the results of investigations of particular CNN’s behavior against Perlin noise, added to the artificial comet surface generated by the dataset generator.