Disaster Management

08 Jun 2021

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Dear Reader,  

We are living in the midst of a revolution. Supervised learning, a branch of Machine learning allows engineers to develop models that can train themselves. In turn, these models are helping solve crisis management problems before disaster strikes. 

Technologists have long modeled data to harness machine learning for disaster relief. After the Chernobyl crisis, scientists analyzed satellite imagery and weather data to track the flow of radiation from the reactor. Today’s algorithms far outpace their predecessors in analytic and predictive powers. Machine learning models are able to deliver more granular predictions. NASA has developed the Landslide Hazard Assessment for Situational Awareness (LHASA) Model. Data from the Global Precipitation Measurement (GPM) is fed into LHASA in three-hour intervals. If a landslide-prone area is experiencing heavy rain, LHASA then issues a warning. Analysts then channel that information to the appropriate agencies, providing near-real-time risk assessments. 

Roofing material is a major risk factor in resilience to natural disasters. So, a model that can predict it is also one that can predict which buildings are most at risk during an emergency. In Guatemala, models are identifying “soft-story” buildings–those most likely to collapse during an earthquake. “Forecast funding” can mitigate damage by providing the most vulnerable with cash assistance to prepare for disaster. Bangladesh and Nepal are nations that are already implementing this strategy. 

Natural disasters, such as earthquakes, hurricanes and floods affect large areas and millions of people, but responding to such disasters is a massive logistical challenge. Crisis responders, including governments, NGOs, and UN organizations, need fast access to comprehensive and accurate assessments in the aftermath of disasters to plan how best to allocate limited resources. To help mitigate the impact of such disasters, Google in partnership with the United Nations World Food Program (WFP) Innovation Accelerator has created "Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks", which details a machine learning (ML) approach to automatically process satellite data to generate building damage assessments. As per Google this work has the potential to drastically reduce the time and effort required for crisis workers to produce damage assessment reports. In turn, this would reduce the turnaround times needed to deliver timely disaster aid to the most severely affected areas, while increasing the overall coverage of such critical services. The World Food Programme was awarded the 2020 Nobel Peace Prize and they thanked Google and its team of engineers in pioneering the development of artificial intelligence to revolutionise humanitarian operations.        

The application of machine learning techniques to satellite imagery is revolutionizing disaster relief. Crisis maps and image comparisons are helping relief organizations to deliver aid with precision. 

Credits : Akhil Handa Prithwijit Ghosh

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Data for all

Dear Reader,
Data is king or so they say. Inclusivity for data platforms can be achieved when data reaches all stakeholders without lag or restrictions. Market information disseminated quickly could make the difference between making and losing money in the financial markets.
Spending on financial market data/analysis and news exceeded the USD 30 billion mark for the first time ever in 2018, according to a new report published by Burton-Taylor International Consulting.
Xignite’s market data cloud is a single platform that unifies financial data consumption. It provides cloud-based financial market data APIs to help emerging companies and established enterprises deliver real-time and reference market data to their digital assets, such as websites and apps. Xignite cloud API’s cover millions of financial instrument across all asset classes.
With all the real time data at hand, investors would like to trade across all asset classes. Robinhood makes it possible to trade in stocks, ETF’s, options and crypto, brokerage free. It also gives access to professional research reports, margin trading and instant deposits. Various other tools and features such as price movement notifications and customized investment news provide additional appeal.
Market Prophit is a financial Big Data analytics company delivering real-time, meaningful intelligence to investors through sophisticated natural language processing, predictive analytics, and powerful visualizations of sentiment and buzz derived from financial related conversations in social media chatter. Market Prophit is the first service ever to provide quantitative rankings of financial bloggers data in social media and generate unique, real-time sentiment signals (bullish/bearish).
Another fintech startup Mindbridge Analytics is using AI and ML to detect anomalous patterns of activities, unintentional errors and intentional financial misstatements in financial datasets. The auditing software will automate ingestion and analysis of data and help accountants identify risk. A risk score is generated for the processed data and will flag transactions that need investigation.
Financial institutions harnessing the power of big data coupled with fintech innovation are leveraging meaningful insights from many disparate data sets. Fintech players have set the ball rolling for financial inclusion. A collaborative approach is the natural next step to accelerate the pace of this process.
Credits : Akhil Handa,Prithwijit Ghosh


Edutainment comes from the words "education" and "entertainment." It refers to any form of entertainment that is educational. Edutainment startup aims to make the learning process smooth by engaging students and young learners mostly aged 15 and below with fun and memorable experiences through smartphones and other internet-connected devices, virtual reality-powered tools and other gamified digital learning content.  
Mumbai-based Ontamo Entertainment has developed Ria Rabbit, an animated cartoon from Pashu Nagari, India, for kids in the age group of 0-6. It is India’s first age-appropriate, culturally relevant home-grown intellectual property (IP) content for children. Their storytelling animated videos, audios and picture books engage the attention of kids while building the sense of Indian values which parents would want to inculcate in them through these characters. 
Another startup, SP RoboticsWorks has developed a platform wherein concepts are taught using animation videos and real-world examples. It has established more than 83 dedicated centers across India called SP Robotics Maker Labs which offers courses in Robotics, Internet of Things (IOT), Image Processing, Virtual Reality (VR) and more, both in the online and offline Smart-Class mode. 
Similarly, Paper Boat Apps Pvt Ltd has launched Kiddopia, a subscription-based pre-school edutainment app, which teaches a variety of skills to kids. It covers everything from Math, Language Skills, GK and Social Skills to Creativity and Self-expression by engaging kids with its visuals as well as fun and exciting gameplay. 
Similarly, Panda & Wolf Holding created a mobile-gaming app Eco-warrior for children between the age of 6 - 11. It uses game-based learning to teach children about waste sorting and recycling. Through an engaging storyline and immersive stages, the app informs young users about issues plaguing the environment like deforestation, waste pollution and overconsumption. 
Mumbai-based startup Shirsa Media Labs offers an app NewsPIK which is a digital newspaper for children. The Shirsa team creates news articles, events, quizzes and other information, so children are aware of the world around them. It stimulates young minds and keeps them informed. 
In this pandemic situation where education has gone online, startups are combining the concepts of education and entertainment to offer children interactive learning experience. 

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