scholarly journals Convolutional Neural Network based Hurricane Damage Detection using Satellite Images

2021 ◽  
Author(s):  
Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Deepika Koundal ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
North Atlantic ◽  
Satellite Images ◽  
North Atlantic Ocean ◽  
Human Life ◽  
Rapid Assessment ◽  
Hurricane Damage ◽  
The North ◽  
Proposed Model

Abstract Huge swirling storms known as hurricanes are tropical storms appearing in the North Atlantic Ocean and Northeast Pacific that result in winds of 120 km/hour and higher. The winds occurring during hurricanes are catastrophic resulting in immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the first responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a Convolutional Neural Network model has been designed that assesses the damage caused to buildings of post hurricane satellite images. The images have been classified as Damaged and Undamaged. The model is composed of five convolutional layers, five pooling layers, one flattening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23000 images of size 128 X 128 pixels has been used in this paper. The proposed model performed best at learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95, precision of 0.97, recall of 0.96 and F1-score of 0.96. It also achieved the best accuracy and minimum loss.

scholarly journals Estimating mixed layer nitrate in the North Atlantic Ocean

2010 ◽  
Vol 7 (3) ◽  
pp. 795-807 ◽  
Author(s):  
T. Steinhoff ◽  
T. Friedrich ◽  
S. E. Hartman ◽  
A. Oschlies ◽  
D. W. R. Wallace ◽  
...  
Keyword(s):  
Neural Network ◽  
Atlantic Ocean ◽  
Observational Data ◽  
Mixed Layer ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Mixed Layer Depth ◽  
Model Data ◽  
The North ◽  
The North Atlantic

Abstract. Here we present an equation for the estimation of nitrate in surface waters of the North Atlantic Ocean (40° N to 52° N, 10° W to 60° W). The equation was derived by multiple linear regression (MLR) from nitrate, sea surface temperature (SST) observational data and model mixed layer depth (MLD) data. The observational data were taken from merchant vessels that have crossed the North Atlantic on a regular basis in 2002/2003 and from 2005 to the present. It is important to find a robust and realistic estimate of MLD because the deepening of the mixed layer is crucial for nitrate supply to the surface. We compared model data from two models (FOAM and Mercator) with MLD derived from float data (using various criteria). The Mercator model gives a MLD estimate that is close to the MLD derived from floats. MLR was established using SST, MLD from Mercator, time and latitude as predictors. Additionally a neural network was trained with the same dataset and the results were validated against both model data as a "ground truth" and an independent observational dataset. This validation produced RMS errors of the same order for MLR and the neural network approach. We conclude that it is possible to estimate nitrate concentrations with an uncertainty of ±1.4 μmol L−1 in the North Atlantic.

scholarly journals Estimating mixed layer nitrate in the North Atlantic Ocean

2009 ◽  
Vol 6 (5) ◽  
pp. 8851-8881
Author(s):  
T. Steinhoff ◽  
T. Friedrich ◽  
S. E. Hartman ◽  
A. Oschlies ◽  
D. W. R. Wallace ◽  
...  
Keyword(s):  
Neural Network ◽  
Atlantic Ocean ◽  
Observational Data ◽  
Mixed Layer ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Mixed Layer Depth ◽  
Model Data ◽  
The North ◽  
The North Atlantic

Abstract. Here we present an equation for the estimation of nitrate in surface waters of the North Atlantic Ocean (40° N to 52° N, 10° W to 60° W). The equation was derived by multiple linear regression (MLR) from nitrate, sea surface temperature (SST) observational data and model mixed layer depth (MLD) data. The observational data were taken from merchant vessels that have crossed the North Atlantic on a regular basis in 2002/2003 and from 2005 to present. It is important to find a robust and realistic esitmate of MLD because the deepening of the mixed layer is crucial for nitrate supply to the surface. We compared model data from two models (FOAM and Mercator) with MLD derived from float data (using various criteria). The Mercator model gives a MLD estimate that is close to the MLD derived from floats. MLR was established using SST, MLD from Mercator, time and latitude as predictors. Additionally a neural network was trained with the same dataset and the results were validated against both model data as a "ground truth" and an independent observational dataset. This validation produced RMS errors of the same order for MLR and the neural network approach. We conclude that it is possible to estimate nitrate concentrations with an uncertainty of ±1.5 μmol L−1 in the North Atlantic.

Contrail frequency over Europe from NOAA-satellite images

1994 ◽  
Vol 12 (10/11) ◽  
pp. 962-968 ◽  
Author(s):  
S. Bakan ◽  
M. Betancor ◽  
V. Gayler ◽  
H. Graßl
Keyword(s):  
North Atlantic ◽  
Cloud Cover ◽  
Western Europe ◽  
Visual Inspection ◽  
Satellite Images ◽  
North Atlantic Ocean ◽  
Infrared Images ◽  
The North ◽  
Noaa Avhrr ◽  
The North Atlantic

Abstract. Contrail cloudiness over Europe and the eastern part of the North Atlantic Ocean was analyzed for the two periods September 1979 - December 1981 and September 1989 - August 1992 by visual inspection of quicklook photographic prints of NOAA/AVHRR infrared images. The averaged contrail cover exhibits maximum values along the transatlantic flight corridor around 50 °N (of almost 2%) and over western Europe resulting in 0.5% contrail cloudiness on average. A strong yearly cycle appears with a maximum (<2%) in spring and summer over the Atlantic and a smaller maximum (<1%) in winter over southwestern Europe. Comparing the two time periods, which are separated by one decade, shows there is a significant decrease in contrail cloudiness over western Europe and a significant increase over the North Atlantic between March and July. Contrail cloud cover during daytime is about twice as high as during nighttime. Contrails are found preferentially in larger fields of 1000 km diameter which usually last for more than a day. Causes, possible errors and consequences are discussed.

Zooplankton Biogeochemical Cycles

2017 ◽  
Author(s):  
Deborah Steinberg
Keyword(s):  
North Atlantic ◽  
North Atlantic Ocean ◽  
Significant Proportion ◽  
Biogeochemical Cycles ◽  
Biological Pump ◽  
The North ◽  
Particle Export ◽  
Chemical Cycling ◽  
Cycling Of Nutrients ◽  
Planktonic Communities

The structure of planktonic communities profoundly affects particle export and sequestration of organic material (the biological pump) and the chemical cycling of nutrients. This chapter describes the integral and multifaceted role zooplankton (both protozoan and metazoan) play in the export and cycling of elements in the ocean, with an emphasis on the North Atlantic Ocean and adjacent seas. Zooplankton consume a significant proportion of primary production across the world's oceans, and their metabolism plays a key role in recycling carbon, nitrogen, and other elements. The chapter also addresses how human or climate-influenced changes in North Atlantic zooplankton populations may in turn drive changes in zooplankton-mediated biogeochemical cycling.

Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean

2018 ◽  
Vol 612 ◽  
pp. 1141-1148 ◽  
Author(s):  
Min Zhang ◽  
Yuanling Zhang ◽  
Qi Shu ◽  
Chang Zhao ◽  
Gang Wang ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Chlorophyll A ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Spatiotemporal Evolution ◽  
The North ◽  
The North Atlantic

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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