Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning

It is important for aquaculture monitoring, scientific planning, and management to extract offshore aquaculture areas from medium-resolution remote sensing images. However, in medium-resolution images, the spectral characteristics of offshore aquaculture areas are complex, and the offshore land and seawater seriously interfere with the extraction of offshore aquaculture areas. On the other hand, in medium-resolution images, due to the relatively low image resolution, the boundaries between breeding areas are relatively fuzzy and are more likely to ‘adhere’ to each other. An improved U-Net model, including, in particular, an atrous spatial pyramid pooling (ASPP) structure and an up-sampling structure, is proposed for offshore aquaculture area extraction in this paper. The improved ASPP structure and up-sampling structure can better mine semantic information and location information, overcome the interference of other information in the image, and reduce ‘adhesion’. Based on the northeast coast of Fujian Province Sentinel-2 Multispectral Scan Imaging (MSI) image data, the offshore aquaculture area extraction was studied. Based on the improved U-Net model, the F1 score and Mean Intersection over Union (MIoU) of the classification results were 83.75% and 73.75%, respectively. The results show that, compared with several common classification methods, the improved U-Net model has a better performance. This also shows that the improved U-Net model can significantly overcome the interference of irrelevant information, identify aquaculture areas, and significantly reduce edge adhesion of aquaculture areas.

Depth Estimation for Monocular Image Based on Convolutional Neural Networks

Perceiving the three-dimensional structure of the surrounding environment and analyzing it for autonomous movement is an indispensable element for robots to operate in scenes. Recovering depth information and the three-dimensional spatial structure from monocular images is a basic mission of computer vision. For the objects in the image, there are many scenes that may produce it. This paper proposes to use a supervised end-to-end network to perform depth estimation without relying on any subsequent processing operations, such as probabilistic graphic models and other extra fine steps. This paper uses an encoder-decoder structure with feature pyramid to complete the prediction of dense depth maps. The encoder adopts ResNeXt-50 network to achieve main features from the original image. The feature pyramid structure can merge high and low level information with each other, and the feature information is not lost. The decoder utilizes the transposed convolutional and the convolutional layer to connect as an up-sampling structure to expand the resolution of the output. The structure adopted in this paper is applied to the indoor dataset NYU Depth v2 to obtain better prediction results than other methods. The experimental results show that on the NYU Depth v2 dataset, our method achieves the best results on 5 indicators and the sub-optimal results on 1 indicator.

A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images

Building segmentation is widely used in urban planning, disaster prevention, human flow monitoring and environmental monitoring. However, due to the complex landscapes and highdensity settlements, automatically characterizing building in the urban village or cities using remote sensing images is very challenging. Inspired by the rencent deep learning methods, this paper proposed a novel end-to-end building segmentation network for segmenting buildings from remote sensing images. The network includes two branches: one branch uses Widely Adaptive Spatial Pyramid (WASP) structure to extract multi-scale features, and the other branch uses a deep residual network combined with a sub-pixel up-sampling structure to enhance the detail of building boundaries. We compared our proposed method with three state-of-the-art networks: DeepLabv3+, ENet, ESPNet. Experiments were performed using the publicly available Inria Aerial Image Labelling dataset (Inria aerial dataset) and the Satellite dataset II(East Asia). The results showed that our method outperformed the other networks in the experiments, with Pixel Accuracy reaching 0.8421 and 0.8738, respectively and with mIoU reaching 0.9034 and 0.8936 respectively. Compared with the basic network, it has increased by about 25% or more. It can not only extract building footprints, but also especially small building objects.

Seroprevalence and immunity of SARS-CoV-2 infection in children and adolescents in schools in Switzerland: design for a longitudinal, school-based prospective cohort study

Objectives This longitudinal cohort study aims to assess the extent and patterns of seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in school-attending children, and their parents and school personnel. It will examine risk factors for infection, the relationship between seropositivity and symptoms, and temporal persistence of antibodies. Methods The study (Ciao Corona) will enroll a regionally representative, random sample of schools in the canton of Zurich, where 18% of the Swiss population live. Children aged 5–16 years, attending primary and secondary schools, and their parents and school personnel are invited. Venous blood and saliva samples are collected for serological testing in June/July 2020, in October/November 2020, and in March/April 2021. Bi-monthly questionnaires will cover SARS-CoV-2 symptoms and tests, health, preventive behavior, and lifestyle information. Hierarchical Bayesian logistic regression models will account for sensitivity and specificity of the serological tests in the analyses and complex sampling structure, i.e., clustering within classes and schools. Results and conclusions This unique school-based study will allow describing temporal trends of immunity, evaluate effects of preventive measures and will inform goal-oriented policy decisions during subsequent outbreaks. Trial registration ClinicalTrials.gov Identifier: NCT04448717, registered June 26, 2020. https://clinicaltrials.gov/ct2/show/NCT04448717.

3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction

3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited in comparison with known RNA sequences up to now. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. In recent years, 3dRNA has been greatly improved by adding several important features, including structure sampling, structure ranking and structure optimization under residue-residue restraints. Particularly, the optimization procedure with restraints enables 3dRNA to treat pseudoknots in a new way. These new features of 3dRNA can greatly promote its performance and have been integrated into the 3dRNA v2.0 web server. Here we introduce these new features in the 3dRNA v2.0 web server for the users.

Filtering Method Based on Symmetrical Second Order Systems

This study presents a filtering and sampling structure based on symmetrical second order systems working on half-period. It is shown that undamped second order oscillating systems working on half-period could provide: (i) a large attenuation coefficient for an alternating signal (due to the filtering second order system), and (ii) a robust sampling procedure (the slope of the generated output being zero at the sampling time moment). Unlike previous studies on the same topics, these results are achieved without the use of an additional integrator.