In 2023, the natural disaster type with the highest number of people affected was floods. Approximately 32 million people alone were affected by floods worldwide that year. droughts was the natural disaster causing the second highest number of victims, affecting 22 million people.

Disaster is a dataset that contains images collected from various sources for three different disasters: fire, water and land. Besides this, it also contains images for various damaged infrastructure due to natural or man made calamities and damaged human due to war or accidents.

There are 13,720 manually annotated images in this dataset, each image is annotated by three individuals. The authors are also providing discriminating image class information annotated manually with bounding box for a set of 200 test images. Images are collected from different news portals, social media, and standard datasets made available by other researchers.

The Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) aims to provide the Pacific Island Countries (PICs) with disaster risk modeling and assessment tools. It also aims to engage in a dialogue with the PICs on integrated financial solutions for the reduction of their financial vulnerability to natural disasters and to climate change. The initiative is part of the broader agenda on disaster risk management and climate change adaptation in the Pacific region. Additionally, the Pacific Disaster Risk Assessment Project provides 15 countries with disaster risk assessment tools to help them better understand, model, and assess their exposure to natural disasters.

This entry does not contain data itself, it is for the website, the NASA Disasters Mapping Portal: https://maps.disasters.nasa.gov The Disasters Mapping Portal contains numerous datasets that can be streamed from the Portal into GIS software. The Disasters Applications area promotes the use of Earth observations to improve prediction of, preparation for, response to, and recovery from natural and technological disasters. Disaster applications and applied research on natural hazards support emergency mitigation approaches, such as early warning systems, and providing information and maps to disaster response and recovery teams. NOTE: Removed "2017 - Present" from "Temporal Applicability" since it's not valid NOTE: Removed "Event-Specific and Near-Real Time Products" from "Update Frequency" since it's not valid

Introduction

Natural Disaster Statistics: Over the last two decades, there has been a tremendous increase in natural disasters all over the world. In addition to these, some man-made viruses are shaking the world upside down. In recent, India witnessed a high level of rainfall in Kerala state causing thousands of people to relocate and millions of costs to the economy. Not only this, but this is a season of snow storms which are extremely common in many parts of the world that lead to billions of dollars in losses to the economy. However, recent developments in technology have brought some amount of safety to the world which can predict such events. Let’s understand these Natural Disaster Statistics considering the older times as well.

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• Between 2012 to 2022, tropical cyclones were the most expensive natural disasters costing $744.3 billion of damage, along with storms ($218 billion) and droughts ($112.9 billion).
• According to the Natural Disaster Statistics, 2022 was the 8th consecutive year of expensive natural disasters, impacted by more than 10 events in the United States of America.
• The United States of America experienced $18 billion in natural disasters every year over the last 5 years.
• Since 1980, the USA announced that it has faced 348 natural disasters including extreme weather conditions, that crossed $1 billion of economic damage.
• As of 2023, Natural Disaster Statistics showed the states in the USA that are most vulnerable to such disasters as follows: Los Angeles County, California, East Baton Rouge Parish, Louisiana, Orleans Parish, Louisiana, Riverside Country, and California.
• As of 2022, the United States of America had the most natural disasters resulting in 22, followed by Indonesia (20) and Colombia (14).
• In 2005, the USA was hit by Hurricane Katrina which caused $193.8 billion of cost to the economy.
• The 10-year analysis showed that from 2013 to 2023, 88.5% of states in the USA announced being affected by natural disasters.
• As of 2023, 40% of homeowners around the world are worried about extreme weather conditions, such as rain, wind, hail, ice, and flood damaging their homes.
• In 2022, severe storms, and tropical cyclones were the most frequent natural disasters in the USA resulting in 11, and 3 events respectively.

As of 2022, Hurricane Katrina - which struck the United States in August 2005 - remained the most expensive insured loss event since 1900, as it incurred insured losses amounting to approximately 90 billion U.S. dollars.

Insuring against natural disasters

Insuring is the practice of transferring risk from one entity to another in exchange for payment. It is important, especially if one lives, owns property or a business in an area prone to natural disasters, to take out coverage for a range of storms, catastrophic events and natural disasters that could cause damage to real estate.

When considering this type of insurance it is important to ask a lot of the important questions up front. How long will it take for a claim to be settled for example, not all insurers settle claims with the same speed. Many also provide specific exclusions, be they for floods, earthquakes or other types of natural events. A detailed inspection of exclusions in a policy is important in order to find out which coverage is still needed. Obviously, the extent of coverage that one should take out is wholly dependent on the area in which one lives, in the United States, as well as in the rest of the world, there are low risk areas and there are high risk areas.

Despite this, no one can be sure where a natural disaster will occur and the severity of the destruction it could bring with it when it does, no one can stop natural disasters or the economic impact that they have but insurance helps to mitigate the loss caused by them.

This hosted feature layer is editable and allows the end user to add a natural disaster to a web map or application. After adding the event to a map presentation, the end user can also provide key attribute information about the incident.Types of Natural Disasters Represented in the Hosted Feature layerThe types of natural disasters represented within this hosted feature layer are earthquakes, tornadoes, hurricanes, landslides, snow events, or floods.Attribute InformationThe end user can add the attribute information of natural disaster type, report date, description, location description, and damage severity (High, Medium, Low, No Damage) through typing text into the appropriate field or selecting an option from a pull down menu.ArcGIS Online ProductsThis hosted feature layer has been incorporated into two web applications published on ArcGIS Online. They are titled "GIS Data and Tools for Today's Emergency Managers" and "CAPSTONE Situational Awareness Viewer".

Insured property losses caused by natural disasters in the United States fluctuated over the past 15 years. In 2022, the insured property losses reached almost 100 billion U.S. dollars, up from a mere 25.5 billion U.S. dollars three years earlier.

Natural Disaster Detection IoT Market size was valued at USD 1.2 Billion in 2023 and is projected to reach USD 4.06 Billion by 2030, growing at a CAGR of 37.2 % during the forecast period 2024-2030.

Global Natural Disaster Detection IoT Market Drivers

Improved Early Warning Systems: The Internet of Things (IoT) makes it possible to implement sophisticated early warning systems for natural disasters such hurricanes, floods, tsunamis, earthquakes, and wildfires. Sensors placed in disaster-prone locations are able to identify environmental anomalies and precursor signals, sending real-time data to central monitoring systems. This makes it easier to notify authorities and locals in a timely manner, lessening the effects of calamities and maybe saving lives.

Enhanced Surveillance and Forecasting: Internet of Things-capable sensors and surveillance apparatuses furnish constant data gathering and examination capacities, imparting discernment into environmental factors like temperature, humidity, pressure, seismic activity, and meteorological trends. This data is processed using sophisticated analytics and machine learning algorithms to find patterns, trends, and early warning signs of impending disasters. This allows for more accurate forecasting and preparedness planning.

Remote sensing and surveillance of disaster-prone locations are made possible by Internet of Things (IoT) devices outfitted with cameras, drones, and satellite imaging technology. Emergency responders and decision-makers can benefit greatly from the situational awareness that these sensors can provide by monitoring changes in the topography, vegetation, water levels, and integrity of infrastructure. Efforts to assess damage, prepare for emergencies, and conduct catastrophe assessments are improved by real-time imagery and video feeds.

Integration with Geographic Information Systems (GIS): Spatial analysis, mapping, and visualization of disaster-related data are made easier by the integration of IoT data with GIS platforms. Decision-making processes are improved by geographic data overlays, risk maps, and geospatial modeling tools, which help authorities identify high-risk areas, allocate resources wisely, and schedule evacuation routes and shelter places.

Developments in Sensor Technology: The spread of IoT devices for natural disaster detection is driven by ongoing developments in sensor technology, such as downsizing, enhanced sensitivity, and low power consumption. Highly weatherproof and resilient sensors can survive extreme weather conditions, which makes them appropriate for use in dangerous and remote areas that are vulnerable to natural disasters.

Government Initiatives and Regulations: Across the globe, governments and regulatory agencies are investing more money and requiring the use of Internet of Things (IoT)-based technologies for resilience and disaster management. Adoption of IoT technologies to improve catastrophe warning, response, and recovery capacities is encouraged by national disaster preparedness programs, financing initiatives, and regulatory frameworks.

Collaborations between the Public and Private Sectors: In the development of Internet of Things (IoT)-based solutions for natural disaster detection, cooperation between public agencies, private businesses, academic institutions, and non-governmental organizations (NGOs) promotes innovation and knowledge exchange. In order to improve community safety and catastrophe resilience, technological development, pilot projects, and field testing are driven by public-private partnerships (PPPs) and collaborative research activities.

Growing Concern and Awareness of Climate Change: The need for Internet of Things (IoT) solutions for disaster detection and mitigation has increased as a result of growing global awareness of climate change and its effects on the frequency and intensity of natural catastrophes. The necessity for preventive actions to mitigate climate-related hazards is acknowledged by stakeholders from all industries, which motivates investments in IoT infrastructure, research, and innovation.

Market Overview:

The Global Catastrophe Insurance Market is expected to grow from USD 5.4 million in 2022 to USD 6.5 million by 2030, at a CAGR of 7%. The growth of the market can be attributed to the increasing awareness about the benefits of catastrophe insurance, the rising frequency and severity of natural disasters, and the growing demand for property-casualty (P&C) insurance. The global catastrophe insurance market is segmented on the basis of type, application, and region. On the basis of type, the market is divided into Flood Insurance, storm insurance for hurricanes and tornadoes, Earthquake Insurance, and volcano insurance. On the basis of application, it is classified into businesses and residences. By region wise, it covers North America, Latin America, Europe, Asia Pacificand Middle East & Africa.

Product Definition:

Catastrophe insurance is insurance that covers a business or individual against catastrophic losses. These losses can be physical damage to property, such as in a fire, or they can be economic losses, such as the loss of income resulting from a natural disaster. Catastrophe insurance is important because it protects businesses and individuals from potentially crippling financial losses.

Flood Insurance:

Flood insurance is a form of disaster insurance that provides coverage against flood waters. It can be defined as an agreement between the insurer and the policyholder wherein the former promises to pay the latter monetary benefits in case of the occurrence of a covered calamity. The main goal behind offering such policies is to avoid losses caused by Catastrophes, which are generally uninsurable.

Storm Insurance for Hurricanes and Tornadoes:

Storm insurance is a type of insurance designed to protect individuals and businesses from the financial losses associated with severe storms, such as hurricanes, tornadoes, and other extreme weather events. The insurance typically covers damage to property, contents, and business interruption caused by the storm. It may also provide coverage for medical expenses, additional living expenses, and other costs related to the storm.

Earthquake Insurance :

Earthquake insurance is a type of Property Insurance that covers damage to a structure caused by an earthquake. It may also cover other related losses, such as fire and smoke damage, which can occur as a result of an earthquake. Earthquake insurance policies typically include deductibles, which can range from a few hundred dollars to a few thousand dollars. Earthquake insurance is generally not included in a standard homeowner's or renter's insurance policy and must be purchased as a separate policy. The cost of earthquake insurance depends on the location, age, and construction of the structure, as well as the amount of coverage desired.

Volcano Insurance :

Volcano Insurance is a specialized form of insurance coverage designed to protect businesses and individuals from the financial losses associated with a volcanic eruption. It covers damage caused by volcanic ash, lava flow, mudflows, and other related hazards. This type of insurance can help cover the costs of property damage, evacuation, relocation, and other expenses associated with a volcano-related disaster.

Application Insights:

The business application segment accounted for the largest market share in 2022 and is expected to continue its dominance over the forecast period. The growing global economy has led to an increase in business activities, which has resulted in a rise in demand for infrastructure such as power generation and communication networks. As a result, companies are focusing on increasing their capital reserves by investing them in riskier projects that offer higher returns such as solar plants and wind farms. This trend is expected to drive growth over the forecast period. Residences are anticipated to be one of the fastest-growing segments due largely to population growth coupled with urbanization trends across various regions including Asia Pacific, North America, Latin America/Caribbean region etcetera.

Regional Analysis:

North America accounted for the largest share of over 40% in 2022. The region is expected to continue its dominance over the forecast period. This can be attributed to the growing demand for insurance policies from various end-users, such as residential and non-residential buildings, based on their geographical location (e.g., coastal areas) or risk exposure (elevated structures). Moreover, increasing awareness about building and personal disaster insurance plans among individuals is further

Here are a few use cases for this project:

1. Emergency Response Prioritization: Authorities and emergency responders could utilize the "disaster detection" model to quickly identify and prioritize areas affected by different levels of disaster. By categorizing the severity into level1, level2, and level3, the resources and efforts can be allocated more efficiently to address the most critical situations.

2. Insurance Claim Assessment: Insurance companies could use the "disaster detection" model to assess the damage caused to properties after a disastrous event. By identifying the disaster level and analyzing affected properties, the insurance companies can streamline the claim process and provide a more accurate assessment of the damages.

3. Infrastructure Monitoring and Risk Assessment: Urban planners and government agencies can leverage the "disaster detection" model to monitor critical infrastructure such as bridges, buildings, and power lines. By identifying potential disaster areas, the authorities can take preventive measures, make informed decisions and reduce the risk of collapsing or failing infrastructure.

4. Humanitarian Aid and Relief Efforts: Non-governmental organizations (NGOs) and humanitarian aid providers could use the "disaster detection" model to estimate the impact of disasters on communities and assess the scale of help required. By understanding the severity of the disaster, aid organizations can better mobilize resources, personnel, and supplies to ensure that the right level of assistance reaches the affected areas.

5. Disaster Awareness and Community Education: The "disaster detection" model could be used for creating educational materials and conducting awareness programs to inform communities about the risks associated with various disaster levels. By showcasing the potential implications of different disaster levels, it can support community preparedness and help develop appropriate disaster response plans.

This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models. The input data in this job contains thousands of untranslated disaster-related messages and their English translations.

Liste annuelle des déclarations d'état de catastrophe naturelle à Digne-les-Bains. Le fichier permet de connaître la date de la déclaration, le type de catastrophe et la zone géographique concernée. Le fichier compressé permet d'extraire des données au format XLS, ODS et CSV : DIGNE-CATASTROPHE-NATURELLE-2016 DIGNE-CATASTROPHE-NATURELLE-2017 * DIGNE-CATASTROPHE-NATURELLE-2018

Public Law 114-4 requires that the FEMA Administrator provide a report by the 5th day of each month on the Disaster Relief Fund (DRF), which includes a funding summary, a table delineating the DRF funding activities each month by state and event, a summary of the funding for the catastrophic events, and an estimate of the date on which the funds will be exhausted.rnrnThe Disaster Relief Fund (DRF) is an appropriation against which FEMA can direct, coordinate, manage, and fund eligible response and recovery efforts associated with domestic major disasters and emergencies that overwhelm State resources pursuant to the Robert T. Stafford Disaster Relief and Emergency Assistance ActrnrnThrough the DRF, FEMA can fund authorized federal disaster support activities as well as eligible state, territorial, tribal, and local actions such as providing emergency protection and debris removal. rnrnThe DRF also funds:rnrnThe repair and restoration of qualifying disaster-damaged public infrastructurernHazard mitigation initiativesrnFinancial assistance to eligible disaster survivorsrnFire Management Assistance Grants for qualifying large forest or grassland wildfires

The disaster data set from the early 20th century to 2021 was collected by www.emdat.be – Université Catholique de Louvain – Brussels – Belgium and encrypted. The dataset includes 2738 records and associated attributes. They detailed the damage and location as well as the type of disaster.

This dataset provides the timeline of major natural disasters that have affected islands in the FSM, compiled by Whitney Hoot and Danko Taborosi of Island Research & Education Initiative (iREi), from the year 1775 to 2012.

The Low Altitude Disaster Imagery (LADI) Dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2019. The initial release of LADI focuses on the Atlantic hurricane seasons and coastal states along the Atlantic Ocean and Gulf of Mexico. Annotations are included for major hurricanes of Harvey, Maria, and Florence. Two key distinctions are the low altitude, oblique perspective of the imagery and disaster-related features, which are rarely featured in computer vision benchmarks and datasets.

Explore The cure for catastrophe : how we can stop manufacturing natural disasters through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets

In 2022, the total number of deaths related to natural disaster events was 474, a decrease of 35% or 250 deaths from 2021.