{'fraction_non_alphanumeric': 0.026241799437675725, 'fraction_numerical': 0.01218369259606373, 'mean_word_length': 5.102857142857143, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Image-based Sea/Land Map Generation from Radar Data

2D radars are efficient sensors used for e.g. coastal or shipborne surveillance. However, the recorded data contains echoes from all its surroundings, without any discrimination of land, sea or occluded terrain, which degrades the performance of target detectors and trackers. We assume that a complete 360◦ radar scan can be used as an image and thereby exploit its spatial information with a multi-scale feature-connected convolutional autoencoder to perform image-based radar segmentation. Our method is compared against the reimplementation of a temporal-based classifier when using unfiltered radar data. The conducted experiments display that our framework can overcome the noise problems inherit in 2D radar data and discriminate the different surfaces by outperforming the temporal-based implementation with a 20% increase in mean pixel-wise accuracy, with a mAP of 67%, and a mean IoU of 58.67%. This is a promising approach towards the application of deep learning for segmentation of radar-based images.