Imprints of the jittering jets explosion mechanism in the morphology of the supernova remnant SNR~0540-69.3

I identify a point-symmetric structure in recently published VLT/MUSE velocity maps of different elements in a plane along the line of sight at the center of the supernova remnant SNR~0540-69.3, and argue that jittering jets that exploded this core collapse supernova shaped this point-symmetric structure. The four pairs of two opposite clumps that compose this point symmetric structure suggest that two to four pairs of jittering jets shaped the inner ejecta in this plane. In addition, intensity images of several spectral lines reveal a faint strip (the main jet-axis) that is part of this plane of jittering jets and its similarity to morphological features in a few other SNRs and in some planetary nebulae further suggests shaping by jets. My interpretation implies that in addition to instabilities, jets also mix elements in the ejecta of core collapse supernovae. Based on the point-symmetric structure and under the assumption that jittering jets exploded this supernova, I estimate the component of the neutron star natal kick velocity on the plane of the sky to be $\simeq 235 \km\s^{-1}$, and at an angle of $\simeq 47^\circ$ to the direction of the main jet-axis. I analyse this natal kick direction together with other 12 SNRs in the frame of the jittering jets explosion mechanism.

Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM

This article aims at demonstrating the feasibility of modern deep learning techniques for the real-time detection of non-stationary objects in point clouds obtained from 3-D light detecting and ranging (LiDAR) sensors. The motion segmentation task is considered in the application context of automotive Simultaneous Localization and Mapping (SLAM), where we often need to distinguish between the static parts of the environment with respect to which we localize the vehicle, and non-stationary objects that should not be included in the map for localization. Non-stationary objects do not provide repeatable readouts, because they can be in motion, like vehicles and pedestrians, or because they do not have a rigid, stable surface, like trees and lawns. The proposed approach exploits images synthesized from the received intensity data yielded by the modern LiDARs along with the usual range measurements. We demonstrate that non-stationary objects can be detected using neural network models trained with 2-D grayscale images in the supervised or unsupervised training process. This concept makes it possible to alleviate the lack of large datasets of 3-D laser scans with point-wise annotations for non-stationary objects. The point clouds are filtered using the corresponding intensity images with labeled pixels. Finally, we demonstrate that the detection of non-stationary objects using our approach improves the localization results and map consistency in a laser-based SLAM system.

Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques

Bridges are an important part of road networks in an emergency period, as well as in ordinary times. Bridge collapses have occurred as a result of many recent disasters. Synthetic aperture radar (SAR), which can acquire images under any weather or sunlight conditions, has been shown to be effective in assessing the damage situation of structures in the emergency response phase. We investigate the backscattering characteristics of washed-away or collapsed bridges from the multi-temporal high-resolution SAR intensity imagery introduced in our previous studies. In this study, we address the challenge of building a model to identify collapsed bridges using five change features obtained from multi-temporal SAR intensity images. Forty-four bridges affected by the 2011 Tohoku-oki earthquake, in Japan, and forty-four bridges affected by the 2020 July floods, also in Japan, including a total of 21 collapsed bridges, were divided into training, test, and validation sets. Twelve models were trained, using different numbers of features as input in random forest and logistic regression methods. Comparing the accuracies of the validation sets, the random forest model trained with the two mixed events using all the features showed the highest capability to extract collapsed bridges. After improvement by introducing an oversampling technique, the F-score for collapsed bridges was 0.87 and the kappa coefficient was 0.82, showing highly accurate agreement.

A CAPSNETS APPROACH TO PAVEMENT CRACK DETECTION USING MOBILE LASER SCANNNING POINT CLOUDS

Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.