Automatic offshore oil slick detection based on deep learning using SAR data and contextual information

2021 ◽  
Author(s):  
Emna Amri ◽  
Hermann Courteille ◽  
Alexandre Benoit ◽  
Philippe Bolon ◽  
Dominique Dubucq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Oil Slick ◽  
Sar Data ◽  
Offshore Oil

scholarly journals Structured (De)composable Representations Trained with Neural Networks

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 79
Author(s):  
Graham Spinks ◽  
Marie-Francine Moens
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning To Learn ◽  
Class Label ◽  
Impact Performance ◽  
Novel Technique ◽  
Language Data ◽  
Concept Classes ◽  
Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals Urban land use mapping using high resolution SAR data based on density analysis and contextual information

2013 ◽  
Vol 38 (6) ◽  
pp. 738-749 ◽  
Author(s):  
Zhaohua Chen ◽  
Ying Zhang ◽  
Bert Guindon ◽  
Thomas Esch ◽  
Achim Roth ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Contextual Information ◽  
Urban Land ◽  
Urban Land Use ◽  
Density Analysis ◽  
Sar Data

scholarly journals A DEEP LEARNING APPROACH TO SOIL MOISTURE ESTIMATION WITH GNSS-R

2021 ◽  
Author(s):  
Max Roberts ◽  
Ian Colwell ◽  
Clara Chew ◽  
Rashmi Shah ◽  
Stephen Lowe
Keyword(s):  
Soil Moisture ◽  
Deep Learning ◽  
Contextual Information ◽  
Additional Information ◽  
Reflection Measurement ◽  
Target Values ◽  
Global Soil ◽  
Data Driven Approach ◽  
Soil Measurements ◽  
Peak Value

GNSS reflection measurements in the form of delay-Doppler maps (DDM) from the CYGNSS constellation can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under these conditions. The application of deep learning based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data was aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2018 which is generally comparable to existing global soil moisture products, but shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well.

scholarly journals Challenges of Sarcasm Detection for Social Network : A Literature Review

2020 ◽  
Vol 8 (2) ◽  
pp. 169
Author(s):  
Afiyati Afiyati ◽  
Azhari Azhari ◽  
Anny Kartika Sari ◽  
Abdul Karim
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
Deep Learning ◽  
Contextual Information ◽  
Unstructured Data ◽  
Learning Models ◽  
Critical Aspect ◽  
Hybrid Approaches ◽  
Data Problem ◽  
The Future

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.

scholarly journals Advancing PICO element detection in biomedical text via deep neural networks

2020 ◽  
Vol 36 (12) ◽  
pp. 3856-3862
Author(s):  
Di Jin ◽  
Peter Szolovits
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Learning Model ◽  
Detection Accuracy ◽  
Clinical Question ◽  
Specific Patient ◽  
Benchmark Datasets ◽  
Deep Learning Model

Abstract Motivation In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components typically reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts. Based on the previous state-of-the-art bidirectional long-short-term memory (bi-LSTM) plus conditional random field architecture, we add another layer of bi-LSTM upon the sentence representation vectors so that the contextual information from surrounding sentences can be gathered to help infer the interpretation of the current one. In addition, we propose two methods to further generalize and improve the model: adversarial training and unsupervised pre-training over large corpora. Results We tested our proposed approach over two benchmark datasets. One is the PubMed-PICO dataset, where our best results outperform the previous best by 5.5%, 7.9% and 5.8% for P, I and O elements in terms of F1 score, respectively. And for the other dataset named NICTA-PIBOSO, the improvements for P/I/O elements are 3.9%, 15.6% and 1.3% in F1 score, respectively. Overall, our proposed deep learning model can obtain unprecedented PICO element detection accuracy while avoiding the need for any manual feature selection. Availability and implementation Code is available at https://github.com/jind11/Deep-PICO-Detection.

scholarly journals AN APPLICATION OF SEGNET FOR DETECTING LANDSLIDE AREAS BY USING FULLY POLARIMETRIC SAR DATA

2019 ◽  
Vol 13 (2) ◽  
pp. 215
Author(s):  
I Made Oka Guna Antara ◽  
Norikazu Shimizu ◽  
Takahiro Osawa ◽  
I Wayan Nuarsa
Keyword(s):  
Deep Learning ◽  
Learning Process ◽  
Training Data ◽  
Memory Usage ◽  
Polarimetric Sar ◽  
Training Images ◽  
Sar Data ◽  
Color Composite

The study location of landslide is in Hokkaido, Japan which occurred due to the Iburi Earthquake 2018. In this study the landslide has been estimated by the fully Polarimetric SAR (Pol-SAR) technique based on ALOS-2 PALSAR-2 data using the Yamaguchi’s decomposition. The Yamaguchi's decomposition is proposed by Yoshio Yamaguchi et.al. The data has been analyzed using the deep learning process with SegNet architecture with color composite. In this research, the performance of SegNet is fast and efficient in memory usage. However, the result is not good, based on the Intersection over Union (IoU) evaluation obtained the lowest value is 0.0515 and the highest value is 0.1483. That is because of difficulty to make training datasets and of a small number of datasets. The greater difference between accuracy and loss graph along with higher epochs represents overfitting. The overfitting can be caused by the limited amount of training data and failure of the network to generalize the feature set over the training images.

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