scholarly journals Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 175
Author(s):  
Hiroki Mizuochi ◽  
Yoshihiro Iijima ◽  
Hirohiko Nagano ◽  
Ayumi Kotani ◽  
Tetsuya Hiyama
Keyword(s):  
Surface Water ◽  
Random Forest ◽  
Water Monitoring ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Spatiotemporal Resolution ◽  
Heterogeneous Distribution ◽  
Modis Data ◽  
Adversarial Networks ◽  
The Impact

Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Vol 11 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shicheng Guo ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that MCGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), k-Nearest Neighbor (KNN), random forest classification [RF (C)], random forest regression [RF (R)], logistic regression (LogR), and support vector machine (SVM). To illustrate their applications, the proposed MCGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

scholarly journals A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION

2019 ◽  
Vol IV-2/W7 ◽  
pp. 167-173 ◽  
Author(s):  
P. J. Soto ◽  
J. D. Bermudez ◽  
P. N. Happ ◽  
R. Q. Feitosa
Keyword(s):  
Remote Sensing ◽  
Representation Learning ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Image Categorization ◽  
Absolute Value ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Public Datasets ◽  
The Impact

<p><strong>Abstract.</strong> This work aims at investigating unsupervised and semi-supervised representation learning methods based on generative adversarial networks for remote sensing scene classification. The work introduces a novel approach, which consists in a semi-supervised extension of a prior unsupervised method, known as MARTA-GAN. The proposed approach was compared experimentally with two baselines upon two public datasets, <i>UC-MERCED</i> and <i>NWPU-RESISC45</i>. The experiments assessed the performance of each approach under different amounts of labeled data. The impact of fine-tuning was also investigated. The proposed method delivered in our analysis the best overall accuracy under scarce labeled samples, both in terms of absolute value and in terms of variability across multiple runs.</p>

scholarly journals GENERATIVE ADVERSARIAL NETWORKS TO GENERALISE URBAN AREAS IN TOPOGRAPHIC MAPS

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 15-22
Author(s):  
A. Courtial ◽  
G. Touya ◽  
X. Zhang
Keyword(s):  
Urban Areas ◽  
Topographic Maps ◽  
Generative Adversarial Networks ◽  
Topographic Map ◽  
Generative Adversarial Network ◽  
Inner Cities ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
The Impact

Abstract. This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas. We use as input detailed buildings, roads, and rivers from topographic datasets produced by the French national mapping agency (IGN), and we expect as output of the GAN a legible map of these elements at a target scale of 1:50,000. This level of detail requires to reduce the amount of information while preserving patterns; covering dense inner cities block by a unique polygon is also necessary because these blocks cannot be represented with enlarged individual buildings. The target map has a style similar to the topographic map produced by IGN. This experiment succeeded in producing image tiles that look like legible maps. It also highlights the impact of data and representation choices on the quality of predicted images, and the challenge of learning geographic relationships.

scholarly journals Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali)

2010 ◽  
Vol 14 (2) ◽  
pp. 309-324 ◽  
Author(s):  
J. Gardelle ◽  
P. Hiernaux ◽  
L. Kergoat ◽  
M. Grippa
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Regional Scale ◽  
Aerial Photographs ◽  
Modis Data ◽  
Maximal Area ◽  
Flooded Area ◽  
Shallow Soils ◽  
The Impact ◽  
Flooded Areas

Abstract. Changes in the flooded area of ponds in the Gourma region from 1950 to present are studied by remote sensing, in the general context of the current multi-decennial Sahel drought. The seasonal and interannual variations of the areas covered by surface water are assessed using multi-date and multi-sensor satellite images (SPOT, FORMOSAT, LANDSAT-MSS, –TM, and -ETM, CORONA, and MODIS) and aerial photographs (IGN). Water body classification is adapted to each type of spectral resolution, with or without a middle-infrared band, and each spatial resolution, using linear unmixing for mixed pixels of MODIS data. The high-frequency MODIS data document the seasonal cycle of flooded areas, with an abrupt rise early in wet season and a progressive decrease in the dry season. They also provide a base to study the inter-annual variability of the flooded areas, with sharp contrasts between dry years such as 2004 (low and early maximal area) and wetter years such as 2001 and 2002 (respectively high and late maximal area).The highest flooded area reached annually greatly depends on the volume, intensity and timing of rain events. However, the overall reduction by 20% of annual rains during the last 40 years is concomitant with an apparently paradoxical large increase in the area of surface water, starting from the 1970's and accelerating in the mid 1980's. Spectacular for the two study cases of Agoufou and Ebang Mallam, for which time series covering the 1954 to present period exist, this increase is also diagnosed at the regional scale from LANDSAT data spanning 1972–2007. It reaches 108% between September 1975 and 2002 for 91 ponds identified in central Gourma. Ponds with turbid waters and no aquatic vegetation are mostly responsible for this increase, more pronounced in the centre and north of the study zone. Possible causes of the differential changes in flooded areas are discussed in relation with the specifics in topography, soil texture and vegetation cover over the watersheds that feed each of the ponds. Changes in rain pattern and in ponds sedimentation are ruled out, and the impact of changes in land use, limited in the area, is found secondary, as opposed to what has often been advocated for in southern Sahel. Instead, major responsibility is attributed to increased runoff triggered by the lasting impact of the 1970–1980's droughts on the vegetation and on the runoff system over the shallow soils prevailing over a third of the landscape.

scholarly journals Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali)

2009 ◽  
Vol 6 (4) ◽  
pp. 5047-5083 ◽  
Author(s):  
J. Gardelle ◽  
P. Hiernaux ◽  
L. Kergoat ◽  
M. Grippa
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Aerial Photographs ◽  
Water Volume ◽  
General Context ◽  
Modis Data ◽  
Flood Regime ◽  
Maximal Area ◽  
Shallow Soils ◽  
The Impact

Abstract. Changes in the flood regime of ponds in the Gourma region from 1950 to present are studied by remote sensing, in the general context of the current multi-decennial Sahel drought. The seasonal and interannual variations of the areas covered by surface water are assessed using multi-date and multi-sensor satellite images (SPOT, FORMOSAT, LANDSAT-MSS, -TM, and -ETM, CORONA, and MODIS) and aerial photographs (IGN). Water body classification is adapted to each type of spectral resolution, with or without a middle-infrared band, and each spatial resolution, using linear unmixing for mixed pixels of MODIS data. The high-frequency MODIS data document the seasonal cycle, with an abrupt rise early in wet season and a progressive decrease in the dry season. They also provide a base to study the inter-annual variability of the flood regime, with sharp contrasts between dry years such as 2004 (low and early maximal area) and wetter years such as 2001 and 2002 (respectively high and late maximal area). The highest water level reached annually greatly depends on the volume, intensity and timing of rain events. However, the overall reduction by 20% of annual rains of the current period, compared to the 50' and 60', is concomitant with an apparently paradoxical large increase in the area of surface water, starting from the late 1980's. Spectacular for the two study cases of Agoufou and Ebang Mallam, for which time series covering the 1954-present period exist, this increase also reaches 98% between 1975 and 2002 for 92 ponds identified in central Gourma. Ponds with turbid waters and no aquatic vegetation are responsible for this increase, more pronounced to the north of the study zone. Possible causes of this change in surface water volume and regime are discussed based on differential changes in ponds dynamics related to the specifics in topography, soil texture and vegetation cover over the watershed. Changes in rain pattern and in ponds sedimentation are ruled out, and the impact of changes in land use, limited in the area, is found secondary, as opposed to what has often been advocated for in cultivated Sahel. Instead, major responsibility is attributed to increased runoff triggered by the lasting impact of the 1970–1980's droughts on the vegetation and on the hydric system over shallow soils.

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shihcheng Guo ◽  
yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. The artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. As one of AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) have been developed. However, the GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that the CGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), KNN, random forest classification (RF (C)), random forest regression (RF (R)), logistic regression (LogR) and support vector machine (SVM). To illustrate their applications, the proposed CGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that the MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

scholarly journals Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2220
Author(s):  
Luis Gonzalez-Abril ◽  
Cecilio Angulo ◽  
Juan-Antonio Ortega ◽  
José-Luis Lopez-Guerra
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Computational Models ◽  
Synthetic Data ◽  
Real Data ◽  
Learning Technologies ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Lung Cancer Patients ◽  
Adversarial Networks

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

scholarly journals Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms

2020 ◽  
Vol 10 (24) ◽  
pp. 9133
Author(s):  
Lloyd A. Courtenay ◽  
Diego González-Aguilera
Keyword(s):  
Sample Size ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Model Performance ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Geometric Morphometric ◽  
Adversarial Networks ◽  
The Impact

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

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