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Natural Disaster Video Clips Free Download

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Engineers are people that solve problems with the help of math and science. For natural disasters, they design and construct buildings meant to protect people during a disaster. For example, they design buildings that can withstand an earthquake.



natural disaster video clips free download

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These natural disasters begin far off in the ocean. They are big waves that occur when there is an abrupt movement on the ocean floor, usually caused by an underwater earthquake. This means tsunamis originate near plate boundaries.


This presidential declaration allows the USDA Farm Service Agency (FSA) to extend much-needed emergency credit to producers recovering from natural disasters through emergency loans. Emergency loans can be used to meet various recovery needs including the replacement of essential items such as equipment or livestock, reorganization of a farming operation, or to refinance certain debts. FSA will review the loans based on the extent of losses, security available, and repayment ability.


The USDA recently announced that it will provide more than $3 billion to commodity and specialty crop producers impacted by natural disaster events in 2022. Eligible impacted producers can apply for financial assistance through the Emergency Relief Program (ERP) 2022. The program will help offset the financial impacts of crop yield and value losses from qualifying disasters occurring in 2022.


According to a Pew Research search of videos connected to the disaster, in the 24 hours immediately after the tornado struck, the 10 most viewed YouTube videos of the event were seen a combined 1.8 million times. Seven of those 10 videos, ranging in length from 28 to 194 seconds, were shot by eyewitnesses unaffiliated with a news organization. video shot by a group of storm chasers.






In an earlier Pew Research report that monitored YouTube news videos from January 2011 through March 2012, another horrific natural disaster generated the most interest in that period. It was the earthquake and tsunami that struck the Northeastern Coast of Japan on March 11, 2011 and killed more than 18,000 people. In the seven days following that tragedy, the 20 most viewed news-related YouTube videos were all devoted to that subject. And combined, they were viewed more than 96 million times.


In 2022, the U.S. experienced 18 separate weather and climate disasters that cost $1 billion. That number puts 2022 in a three-way tie with 2017 and 2011 for the third-highest number of billion-dollar disasters in a calendar year, behind the 22 events in 2020 and 20 events in 2021. It was another year with a high diversity of destructive disasters including winter storm, wildfire, drought, flooding, tornado, tropical cyclone and severe weather/hail events.


Families who have recently experienced a disaster are encouraged to use this toolkit to move toward financial recovery. The toolkit is available in three versions: Minnesota, North Dakota and non-state specific.


Abstract:Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively.Keywords: change detection; natural disasters; deep learning; threshold selection; optical flow estimation


From the Zika outbreak to natural disasters to the recent wildfires, the US has faced a series of complex public health emergencies. Each of these public health crises revealed a need for public health and health care systems at the national, state, and local levels to prepare for and rapidly respond to the unique needs of families. Watch video.


This video follows U.S. Virgin Islands Department of Health and CDC staff as they conduct a CASPER (community health survey) five months after hurricanes Irma and Maria, explaining how CASPER works and highlighting how the USVI Department of Health has been able to use CASPER to guide its disaster recovery work.

Watch video.


Cascadia 9.0 was developed as part of an ongoing research project to determine what motivates young adults to prepare for earthquakes and other natural disasters. Using video games as research and outreach tools, L&C researchers take an interdisciplinary approach to disaster preparedness.


With the continuing growth of earth observation techniques and computer technology, massive amounts of remote sensing data for natural disaster with different spectral-spatial-temporal resolution are available for surveying and assessing changes in natural disaster, which greatly promotes the development of change detection methodologies. Many change detection approaches for natural disaster scenes have been proposed and they can be broadly divided into traditional and deep learning (DL)-based [15]. For traditional CD methods, the simplest approaches are algebra-based methods. Hall and Hay [16] firstly segmented two panchromatic SPOT data observed at different times and then detected changes through an image differencing method. Matsuoka et al. [17], on the basis of the difference between the backscattering coefficient and correlation coefficient achieved in an earthquake, applied supervised classification of the pre- and post-event optical images to present the distribution of damaged areas in Bam. These directly algebraic operations were easy to be implemented but always generated noisy outputs, such as isolated pixels or holes in the changed objects; thus, some transformation and models were used in CD researches. Sharma et al. [18] finished a damage assessment of landslides in a minimum time by pseudo color transformation and extracting the landslide affected area based on the pre- and post-earthquake Landsat-8 images. Lee et al. [19] first proposed an optimization algorithm based on Stepwise Weight Assessment Ratio Analysis (SWARA) model and geographic information system to assess seismic vulnerability. In order to overcome the limitation of the sole band and improve the identification of change detection, fusing datasets acquired from various remote sensors and geographical data are paramount to monitoring the environmental impacts of natural disasters. ElGharbawi et al. [20] estimated the crustal deformation affected by the 2011 Tohoku earthquake combined with two deformation patterns using Synthetic Aperture Radar (SAR) and GPS data. With the purpose of determining the changed buildings in forested areas, Du et al. [21] adopted the graph cuts method taking account of spatial relationships and took grey-scale similarity from old aerial images and height difference based on Digital Surface Model (DSM) generated from LiDAR data as two change detection indexes to optimize building detection.


Due to the rapid development of computer technology, the research of traditional change detection approaches has tuned into integrating deep learning techniques in recent years. Deep learning-based methods have presented promising potentials based on the extraction of high-level features. Saha et al. [22] detected collapsed buildings from SAR images and Ji et al. [23] further put them into a random forest classifier to detect post-seismic destroyed buildings using pre- and post-disaster remote sensing images. In order to achieve higher accuracy, some new neural networks have been introduced into disaster monitoring researches. Ci et al. [24] proposed a novel Convolutional Neural Network (CNN) model in combination with a CNN feature extractor, a new loss function and an ordinal regression classifier to evaluate the degree of building damage caused by earthquakes using aerial imagery. Peng et al. [25] utilized an end-to-end CD method named UNet++ to fuse multiple feature maps from different semantic segmentation levels to generate a final change map with high accuracy. Yavariabdi et al. [26] proposed a new change detection method based on multiobjective evolutionary algorithm (MOEA), which is robust to multispectral Landsat images with atmospheric changes. In this method, the similarity index measure (SSIM) is used to generate the difference image. After that, MOEA is applied to obtain a set of multiple binary change masks by iteratively minimizing two objective functions for changed and unchanged regions and the final binary mask is optimally fused by MRF. With the purpose of improving efficiency, in Ghaffarian et al. [27], extended U-net based on deep residual (ResUnet) followed a Conditional Random Field (CRF) implementation was proposed to update the post-disaster buildings from very high resolution imagery. Alizadeh et al. [12] established a new hybrid framework of Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for earthquake vulnerability assessment. To avoid labeling a massive number of data for the training network, transfer learning has received increased attention. Pi et al. [28] employed transfer learning to train eight CNN models based on You-Only-Look-Once (YOLO), so as to recognize undamaged building roofs in disaster-affected areas. Transferring learning was used by Kung et al. [29] to manage disaster by combination of data augmentation, reference model and augmented model. Li et al. [30] proposed SDPCANet by combining PCANet and saliency detection to make change detection based on SAR images, which effectively reduced the number of training samples but kept higher change detection performance.

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Natural Disaster Video Clips Free Download Velva Naderman <velvanaderman@gmail.com> - 2023-12-30 14:19 -0800

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