A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Genetics and metabolomics of elite athletes: Genome-wide association study and Metabolomics profiling of elite athletes

AIM: The outstanding performance of elite athletes is a product of a complex interaction between genetic and environmental factors. The aims of this study was to compare differences in genetic and metabolic profiles among different classes of elite athletes and to identify genetically-influenced metabolic profiles (metabotypes) underlying these differences. METHODS: Genome-wide association study (GWAS) was conducted in 1259 elite athlete samples using Drug core BeadChip arrays, followed by non-targeted metabolomics of 692 serum samples. Genotype distribution, differences in metabolic levels and genetically-influenced metabotypes were compared between high and moderate endurance and power sports as well as among sports with different cardiovascular demands (CVD). RESULTS: Out of 341385 SNPs, two novel associations are reported for endurance status including rs56330321 in ATP2B2 (p=1.47E-7) and rs2635438 in SYNE1 (p=2.54E-7). A meta-analysis confirmed the association of rs56330321 and rs2635438 with endurance athlete status at GWAS level of significance. Metabolomics analysis of 740 metabolites was performed in in 191 (discovery cohort) and 500 (replication cohort) elite athletes. These studies revealed changes in various metabolites involved in steroid biosynthesis, fatty acid oxidation, oxidative stress response, xenobiotics and various mediators of cell signaling among different groups of endurance, power and CVD athletes. By combining GWAS with metabolomics profiling data (mGWAS), 19 common variant metabolic quantitative trait loci (mQTLs) were identified, of which 5 were novel. When focusing on metabolites associated with endurance, power and CVD, 4 common variant mQTLs were found, of which one novel mQTL linking 4-androsten-3alpha,17alpha-diol monosulfate and SULT2A1 involved in steroid sulfation was identified in association with endurance. CONCLUSIONS: GWAS, metabolomics and mGWAS of elite athletes identified novel markers associated with elite athletic performance with a potential application in biomarker discovery in relation to elite athletic performance

Deep learning & remote sensing : pushing the frontiers in image segmentation

Dissertação (Mestrado em Informática) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Brasília, 2022.A segmentação de imagens visa simplificar o entendimento de imagens digitais e métodos de aprendizado profundo usando redes neurais convolucionais permitem a exploração de diferentes tarefas (e.g., segmentação semântica, instância e panóptica). A segmentação semântica atribui uma classe a cada pixel em uma imagem, a segmentação de instância classifica objetos a nível de pixel com um identificador exclusivo para cada alvo e a segmentação panóptica combina instâncias com diferentes planos de fundo. Os dados de sensoriamento remoto são muito adequados para desenvolver novos algoritmos. No entanto, algumas particularidades impedem que o sensoriamento remoto com imagens orbitais e aéreas cresça quando comparado às imagens tradicionais (e.g., fotos de celulares): (1) as imagens são muito extensas, (2) apresenta características diferentes (e.g., número de canais e formato de imagem), (3) um grande número de etapas de préprocessamento e pós-processamento (e.g., extração de quadros e classificação de cenas grandes) e (4) os softwares para rotulagem e treinamento de modelos não são compatíveis. Esta dissertação visa avançar nas três principais categorias de segmentação de imagens. Dentro do domínio de segmentação de instâncias, propusemos três experimentos. Primeiro, aprimoramos a abordagem de segmentação de instância baseada em caixa para classificar cenas grandes. Em segundo lugar, criamos um método sem caixas delimitadoras para alcançar resultados de segmentação de instâncias usando modelos de segmentação semântica em um cenário com objetos esparsos. Terceiro, aprimoramos o método anterior para cenas aglomeradas e desenvolvemos o primeiro estudo considerando aprendizado semissupervisionado usando sensoriamento remoto e dados GIS. Em seguida, no domínio da segmentação panóptica, apresentamos o primeiro conjunto de dados de segmentação panóptica de sensoriamento remoto e dispomos de uma metodologia para conversão de dados GIS no formato COCO. Como nosso primeiro estudo considerou imagens RGB, estendemos essa abordagem para dados multiespectrais. Por fim, melhoramos o método box-free inicialmente projetado para segmentação de instâncias para a tarefa de segmentação panóptica. Esta dissertação analisou vários métodos de segmentação e tipos de imagens, e as soluções desenvolvidas permitem a exploração de novas tarefas , a simplificação da rotulagem de dados e uma forma simplificada de obter previsões de instância e panópticas usando modelos simples de segmentação semântica.Image segmentation aims to simplify the understanding of digital images. Deep learning-based methods using convolutional neural networks have been game-changing, allowing the exploration of different tasks (e.g., semantic, instance, and panoptic segmentation). Semantic segmentation assigns a class to every pixel in an image, instance segmentation classifies objects at a pixel level with a unique identifier for each target, and panoptic segmentation combines instancelevel predictions with different backgrounds. Remote sensing data largely benefits from those methods, being very suitable for developing new DL algorithms and creating solutions using top-view images. However, some peculiarities prevent remote sensing using orbital and aerial imagery from growing when compared to traditional ground-level images (e.g., camera photos): (1) The images are extensive, (2) it presents different characteristics (e.g., number of channels and image format), (3) a high number of pre-processes and post-processes steps (e.g., extracting patches and classifying large scenes), and (4) most open software for labeling and deep learning applications are not friendly to remote sensing due to the aforementioned reasons. This dissertation aimed to improve all three main categories of image segmentation. Within the instance segmentation domain, we proposed three experiments. First, we enhanced the box-based instance segmentation approach for classifying large scenes, allowing practical pipelines to be implemented. Second, we created a bounding-box free method to reach instance segmentation results by using semantic segmentation models in a scenario with sparse objects. Third, we improved the previous method for crowded scenes and developed the first study considering semi-supervised learning using remote sensing and GIS data. Subsequently, in the panoptic segmentation domain, we presented the first remote sensing panoptic segmentation dataset containing fourteen classes and disposed of software and methodology for converting GIS data into the panoptic segmentation format. Since our first study considered RGB images, we extended our approach to multispectral data. Finally, we leveraged the box-free method initially designed for instance segmentation to the panoptic segmentation task. This dissertation analyzed various segmentation methods and image types, and the developed solutions enable the exploration of new tasks (such as panoptic segmentation), the simplification of labeling data (using the proposed semi-supervised learning procedure), and a simplified way to obtain instance and panoptic predictions using simple semantic segmentation models

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network

A Neural Network-Based Situational Awareness Approach for Emergency Response

publishedVersio

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Astronautics and aeronautics, 1969 Chronology on science, technology, and policy

Statistical mechanics for wall shear turbulence in Couette flow based on Brownian motion and comparison with stochastic theory based on Navier-Stokes equatio

Domain-independent method for developing an integrated engineering design tool

Engineering design is a complex, cognitive process requiring extensive knowledge and experience to be done effectively. Successful design depends on appropriate use of available resources. Competitive design cycles mandate convenient and reliable access to engineering tools and information. An integrated engineering design tool (IEDT) has been developed in response to these demands. Further, the tool development efforts have been made systematic by utilizing the engineering design process, which is shown to be a cognitive activity based on Bloom\u27s taxonomy of cognition. The engineering design process consists of six tasks: establishment of objectives, development of requirements, function analysis, creation of design alternatives, evaluation, and improvements to the design. These tasks are shown to map to the six levels of Bloom\u27s cognitive taxonomy: knowledge, comprehension, application, analysis, synthesis, and evaluation. Once engineering design is shown to be a cognitive process it can be employed to make each of the activities required to develop and IEDT, domain investigation, knowledge acquisition, and IEDT design, systematic. Past research has considered these to be largely ad hoc tasks. Application of the engineering design process to each of the three IEDT development tasks is discussed in general terms;A prototype IEDT has been created for the preliminary design of jet transport aircraft wings based on the systematic engineering design approach is used to demonstrate the implementation of the method. The IEDT is embedded in Microsoft Excel 97 with links to other software and executable code. Examples of different implementation strategies are provided. Several wing weight prediction models are included. The incorporation of depth knowledge is done using fuzzy logic. The IEDT is linked to relevant files containing design documentation, parameter information, graphics, drawings, and historical data. The designer has access to trade-off study information and sensitivity analysis and can choose to perform structural analysis or design optimization. The engineer can also consider design issues such as cost analysis. The modular IEDT has been designed to be easily adaptable by design domain experts so that it may continue to be updated and expanded