Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Neural architectures for open-type relation argument extraction

In this work, we focus on the task of open-type relation argument extraction (ORAE): given a corpus, a query entity Q, and a knowledge base relation (e.g., “Q authored notable work with title X”), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, for example, X: the title of a book or a work of art) from the corpus. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger yields the best results. We release a data set to train and evaluate ORAE, based on Wikidata and obtained by distant supervision

Identificação automática de aves a partir de áudio

Bird classification from audio is mainly useful for ornithologists and ecologists. With growing amounts of data, manual bird classification is time-consuming, which makes it a costly method. Birds react quickly to environmental changes, which makes their analysis an important problem in ecology, as analyzing bird behaviour and population trends helps detect other organisms in the environment. A reliable methodology that automatically identifies bird species from audio would be a valuable tool for the experts in the area. The main purpose of this work is to propose a methodology able to identify a bird species by its chirp. There are many techniques that can be used to process the audio data, and to classify the audio data. This thesis explores the deep learning techniques that are being used in this domain, such as using Convolutional Neural Networks and Recurrent Neural Networks to classify the data. Audio problems in deep learning are commonly approached by converting them into images using feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. Multiple deep learning and feature extraction combinations are used and compared in this thesis in order to find the most suitable approach to this problem.Classificação de pássaros a partir de áudio é principalmente útil para ornitólogos e ecologistas. Com o aumento da quantidade de dados disponível, classificar a espécie dos pássaros manualmente acaba por consumir muito tempo. Os pássaros reagem rapidamente às alterações climáticas, o que faz com que a análise de pássaros seja um problema interessante na ecologia, porque ao analisar o comportamento das aves e a tendência populacional, outros organismos podem ser detetados no meio ambiente. Devido a estes factos, a criação de uma metodologia que identifique a espécie dos pássaros fiavelmente seria uma ferramenta bastante útil para os especialistas na área. O objetivo principal do trabalho nesta dissertação é propor uma metodologia que identifique a espécie de uma ave através do seu canto. Existem diversas técnicas que podem ser usadas para processar os dados sonoros que contêm os cantos das aves, e que podem ser usadas para classificar as espécies das aves. Esta dissertação explora as principais técnicas de deep learning que são usadas neste domínio, tais como as redes neuronais convolucionais e as redes neuronais recorrentes que são usadas para classificar os dados. Os problemas relacionados com som no deep learning, são normalmente abordados por converter os dados sonoros em imagens utilizando técnicas de extração de atributos, para depois serem classificados utilizando modelos de deep learning tipicamente utilizados para classificar imagens. Dois exemplos destas técnicas de extração de atributos normalmente utilizadas são os Espectrogramas de Mel e os Coeficientes Cepstrais da Frequência de Mel. Nesta dissertação, são feitas múltiplas combinações de técnicas de deep learning com técnicas de extração de atributos do som. Estas combinações são utilizadas para serem comparadas com o âmbito de encontrar a abordagem mais apropriada para o problema

BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets

The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure