Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Fracture and Damage Characteristics of Granite under Uniaxial Disturbance Loads

To investigate the mechanical properties and damage characteristics of granite under frequent disturbance loads in the process of underground engineering construction, laboratory uniaxial compression tests were conducted on granite under combined dynamic and static loading conditions. The following conclusions were reached: (1) under a dynamic disturbance, the failure stress of granite grows gradually as the initial stress and disturbance load rise due to the coupling of damage and strain-rate effect; (2) the characteristic stresses of granite specimens grow with the increasing amplitude of disturbance Δσ under the disturbing loads; with the same Δσ, the characteristic stresses show an increase trend with the increasing initial stress σm; (3) the particle size distribution of rock fragments broken under the disturbance load follows the fractal law, and the fractal dimension F gradually enlarges with the growth of Δσ, indicative of an increased degree of fragmentation; and (4) the damage variable grows rapidly at first, then steadily, and, finally, shows a rapid growth trend again under the disturbance loads. The Δσ significantly influences the number of cycles and rate of change of the damage variable during the steady increase. This research has certain theoretical significance and engineering guidance value for dynamic disaster recognition and control

Mobility Data Science (Dagstuhl Seminar 22021)

This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum