scholarly journals Detecting Natural Disasters, Damage, and Incidents in the Wild

2020 ◽  
pp. 331-350
Author(s):  
Ethan Weber ◽  
Nuria Marzo ◽  
Dim P. Papadopoulos ◽  
Aritro Biswas ◽  
Agata Lapedriza ◽  
...  
Keyword(s):  
Natural Disasters ◽  
In The Wild

scholarly journals Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

2021 ◽  
Vol 13 (4) ◽  
pp. 796
Author(s):  
Long Zhang ◽  
Xuezhi Yang ◽  
Jing Shen
Keyword(s):  
Natural Disasters ◽  
Current Method ◽  
Peak Frequency ◽  
Significant Information ◽  
Physiological Phenomenon ◽  
In The Wild ◽  
Motion Magnification ◽  
Background Motion ◽  
Detection And Localization ◽  
Frequency Properties

The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.

Freeze-substituted fungal cells of Nectria haematococca: A comparison of the macroconidial walls of the adhesion-competent wild type with an adhesion-reduced mutant

1990 ◽  
Vol 48 (3) ◽  
pp. 688-689
Author(s):  
Thecan Caesar-Ton That ◽  
Lynn Epstein
Keyword(s):  
Freeze Substitution ◽  
Uranyl Acetate ◽  
Wild Type ◽  
Nectria Haematococca ◽  
Fruit Extract ◽  
Dialysis Tubing ◽  
Tube Emergence ◽  
Contrast Microscopy ◽  
In The Wild ◽  
Freeze Damage

Nectria haematococca mating population I (anamorph, Fusarium solani) macroconidia attach to its host (squash) and non-host surfaces prior to germ tube emergence. The macroconidia become adhesive after a brief period of protein synthesis. Recently, Hickman et al. (1989) isolated N. haematococca adhesion-reduced mutants. Using freeze substitution, we compared the development of the macroconidial wall in the wild type in comparison to one of the mutants, LEI.Macroconidia were harvested at 1C, washed by centrifugation, resuspended in a dilute zucchini fruit extract and incubated from 0 - 5 h. During the incubation period, wild type macroconidia attached to uncoated dialysis tubing. Mutant macroconidia did not attach and were collected on poly-L-lysine coated dialysis tubing just prior to freezing. Conidia on the tubing were frozen in liquid propane at 191 - 193C, substituted in acetone with 2% OsO4 and 0.05% uranyl acetate, washed with acetone, and flat-embedded in Epon-Araldite. Using phase contrast microscopy at 1000X, cells without freeze damage were selected, remounted, sectioned and post-stained sequentially with 1% Ba(MnO4)2 2% uranyl acetate and Reynold’s lead citrate. At least 30 cells/treatment were examined.

When Mother Earth Rises Up

2013 ◽  
Vol 44 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Simona Sacchi ◽  
Paolo Riva ◽  
Marco Brambilla
Keyword(s):  
Free Will ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Mental States ◽  
Disaster Victims ◽  
Willingness To Help ◽  
Mother Earth

Anthropomorphization is the tendency to ascribe humanlike features and mental states, such as free will and consciousness, to nonhuman beings or inanimate agents. Two studies investigated the consequences of the anthropomorphization of nature on people’s willingness to help victims of natural disasters. Study 1 (N = 96) showed that the humanization of nature correlated negatively with willingness to help natural disaster victims. Study 2 (N = 52) tested for causality, showing that the anthropomorphization of nature reduced participants’ intentions to help the victims. Overall, our findings suggest that humanizing nature undermines the tendency to support victims of natural disasters.

PENERAPAN ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DAERAH RAWAN BENCANA DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Amril Mutoi Siregar
Keyword(s):  
Natural Disasters ◽  
Accurate Data ◽  
Disaster Victims ◽  
Central Java ◽  
North Sulawesi ◽  
Natural Beauty ◽  
Or Groups ◽  
Disaster Data ◽  
The Government ◽  
Almost All

Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm. The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.

Social innovation and natural disasters: the "Casa Italia" Plan

2017 ◽  
pp. 87-102 ◽  
Author(s):  
Francesco Pagliacci ◽  
Margherita Russo ◽  
Laura Sartori
Keyword(s):  
Natural Disasters ◽  
Social Innovation

Prey tell: what quillback rockfish early life history traits reveal about their survival in encounters with juvenile coho salmon

2020 ◽  
Vol 650 ◽  
pp. 7-18 ◽  
Author(s):  
HW Fennie ◽  
S Sponaugle ◽  
EA Daly ◽  
RD Brodeur
Keyword(s):  
Life History ◽  
Early Life ◽  
Life History Traits ◽  
Direct Evidence ◽  
Coho Salmon ◽  
Larval Fish ◽  
Early Life History ◽  
In The Wild ◽  
Otolith Microstructure Analysis ◽  
Pelagic Juvenile

Predation is a major source of mortality in the early life stages of fishes and a driving force in shaping fish populations. Theoretical, modeling, and laboratory studies have generated hypotheses that larval fish size, age, growth rate, and development rate affect their susceptibility to predation. Empirical data on predator selection in the wild are challenging to obtain, and most selective mortality studies must repeatedly sample populations of survivors to indirectly examine survivorship. While valuable on a population scale, these approaches can obscure selection by particular predators. In May 2018, along the coast of Washington, USA, we simultaneously collected juvenile quillback rockfish Sebastes maliger from both the environment and the stomachs of juvenile coho salmon Oncorhynchus kisutch. We used otolith microstructure analysis to examine whether juvenile coho salmon were age-, size-, and/or growth-selective predators of juvenile quillback rockfish. Our results indicate that juvenile rockfish consumed by salmon were significantly smaller, slower growing at capture, and younger than surviving (unconsumed) juvenile rockfish, providing direct evidence that juvenile coho salmon are selective predators on juvenile quillback rockfish. These differences in early life history traits between consumed and surviving rockfish are related to timing of parturition and the environmental conditions larval rockfish experienced, suggesting that maternal effects may substantially influence survival at this stage. Our results demonstrate that variability in timing of parturition and sea surface temperature leads to tradeoffs in early life history traits between growth in the larval stage and survival when encountering predators in the pelagic juvenile stage.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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