Data publishing platform Silk has recently published a map of countries that outlaws consensual, same-sex relationships of lesbian, gay, bisexual, and transgender (LGBT) people.
The map highlights about 76 countries who criminalize consensual, same-sex relations with provisions of punishments that include prison sentences, flogging, and even death penalties. The map filters countries on basis of the type of law they have on criminalisation of same-sex relations and the type of sentences for the type of “crime.”
The map identifies seven countries, where persons in same-sex relations are sentenced with death penalties. Countries like Iran, Qatar, Saudi Arabia, and Yemen, persons in same-sex relations are punishable by death, with additional sentences like 100 lashes, 1-3 years in prison, or stoning by death.
According to the map, Nigeria falls into a “mixed sentences” category because it has various types of laws, under both criminal and Sharia law, which criminalize consensual same-sex conduct. It also criminalizes discussion of LGBT rights.
Many of these laws have been introduced recently in Russia, Nigeria, and Gambia, where they have introduced laws that restrict people’s ability to discuss LGBT rights or to organize a gathering of LGBT people, denying them basic freedom of expression and association and stripping them of their capacity to advocate for change.
Such laws are used to legitimize violence and discrimination against LGBT peoples. They are a threat not only to LGBT people, but to fundamental human rights to which all people are entitled.
This map only includes information regarding criminal laws that regulate consensual sexual relations between adults of the same sex. Here is an interactive map of countries where same-sex relations are an outlaw.
Singapore-based Otsaw Digital has developed world’s first ground-aerial outdoor security robot system. Named as O-R3, the system combines an autonomous roving ground vehicle and a surveillance drone. The O-R3 can be launched to track any intruder’s location. Powered by machine learning algorithms, the vehicle avoids obstacles and identifies unattended objects.
In an exclusive interview with Geospatial World, Rohan Verma, Executive Director and CTO, MapmyIndia says that eLoc is like Aadhaar of addresses. Verma also discusses how the company is monetizing eLoc and its connection with ISRO.
Indian government’s ‘Toilet Locator’ initiative across 85 cities to be in action by October 2, 2017. The app by the Ministry of Urban Development will use Google Maps to track nearby toilets. Agra, Ajmer, Ahmedabad, Bhubaneswar, Guwahati will have this feature. Quality Council of India will map public toilets under control by the local municipal body. You can search toilet anywhere with options like a toilet for ‘Female’ or ‘Male. Toilets can be ranked based on parameters like hygiene, infrastructure, safety, seat type, and if the toilet is paid or free. It will also help to find out if the toilet is ‘disabled friendly’ and details including its operational timings. The app has ‘Submit Toilet’ option too where anyone can submit a new entry. Toilet Locator’ is already in action in National Capital Region. Till date, total of 5,162 toilets have been mapped across the region.
To reach the masses and educate them on national disaster management, ISRO used the social media platform to announce the launch of its updated National Database for Emergency Management (NDEM). An initiative taken by the Government of India to build a safer and disaster resilient India, the latest version of NDEM includes features that give greater impetus to the purpose.
Though in existence since 2013, several updates have been implemented on the platform to make it more user-friendly. NDEM’s updated version has been designed to boost the portal’s ability to manage emergency situations promptly and more efficiently — almost in real time. The portal is browser independent and compatible with all computer devices and mobile phones with vector rendering services.
The main purpose of NDEM is to address disasters such as flood, cyclone, drought, forest fire, landslide, and earthquake. Earth observation satellites together with meteorological and communication satellites and aerial survey system form the base for providing timely support and services for disaster management.
The latest version was released by Minister of Home Affairs Rajnath Singh during the inaugural ceremony of the second meeting of National Platform on Disaster Risk Reduction (NPDRR) at Vigyan Bhavan, New Delhi on May 15.
The first version, i.e. NDEM Portal Version 1.0, was launched and made operational in June 2013. This version helped various Indian state governments to manage disaster events efficient during 2013-2014.
Then in May 2015, NDEM’s version 2.0 was launched. The more advanced version had features like disaster dashboard, comprehensive multi-scale geospatial database, historical disaster database, customized Decision Support System (DSS) tools, incident reporting, interaction tools, mobile applications and Indian Disaster Resource Network (IDRN)/ health database.
Now with the launch of version 3.0, ISRO wants to further address the crucial issue of disaster management. Some of the salient features of the NDEM Version 3.0 are:
A dashboard for visualization of disaster alerts, warnings issued by nodal departments, current disaster news and authorized social media content
Incident reporting through Web and mobile apps
Integrated visualization of multi-scale data services
Customised decision support tools such as proximity and optimal path analysis, report generation, etc., for relief management
Interactive tools for communication among State Government Departments and MHA through portal
Live audio/video module for visualization of on-site response operations
Resource management module for allocation and monitoring of relief resources
The main objective of NDEM is:
Development of Decision Support System (DSS) tools for addressing disaster/emergency management.
Establishing computer infrastructure to facilitate network connectivity, data ingest, validation, GIS databases organization, data dissemination and services hosting
Organization of multi-scale geospatial database for entire country at 1:50,000 scale; for 350 Multi-hazard prone districts at 1:10,000 scale; for 5 Mega-cities
What is NDEM and how does it work?
NDEM portal acts as a geospatial national repository of data and was developed to manage emergency situations in the country. It has a set of decision support tools that assist disaster managers to gather timely information and take swift decisions during any emergency situation.
The National Remote Sensing Centre (NRSC) of ISRO had implemented NDEM for the Government of India’s Ministry of Home Affairs and remains responsible for operationalizing the NDEM projects.
So far about 788 value added disaster specific products covering 15 Indian states were served through ISRO-DMS VPN secured network. In addition to these disaster specific products, multi-scale geospatial data services were provided for 36 states/Union territories on 1:50,000 scale, 210 Multi-Hazard prone districts on 1:10,000 scale (out of 350 districts), High-Resolution Satellite imageries for 210 towns. Mobile applications were developed for relief management and made available to all State and Central departments. Seven regional training programs were organized across the country (Guwahati, Dehradun, Kolkata, Gandhinagar, Thiruvananthapuram, Bhopal, and Delhi) during June – August 2015 for the familiarization of NDEM private and public portals for enabling the better utilization of NDEM products and services by state departments.
In the first part of this blog series, we gave you simple and elaborative definitions of what is artificial intelligence (AI), machine learning and deep learning. This is the second part of the series; here we are elucidating our readers with – What is the difference between AI, machine learning, and deep learning.
You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI.
Artificial intelligence is any computer program that does something smart. It can be a stack of a complex statistical model or if-then statements. AI can refer to anything from a computer program playing chess, to a voice-recognition system like Alexa. However, the technology can be broadly categorized into three groups — Narrow AI, artificial general intelligence (AGI), and superintelligent AI.
IBM’s Deep Blue, which beat chess grandmaster Garry Kasparov at the game in 1996, or Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI — AI that is skilled at one specific task. This is different from artificial general intelligence (AGI), which is AI that is considered human-level and can perform a range of tasks. Superintelligent AI takes things a step further. As Nick Bostrom describes it, this is “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” In other words, it is when the machines have outfoxed us.
Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves. It is currently the most promising tool in the AI pool for businesses. Machine learning systems can quickly apply knowledge and training from large datasets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Machine learning allows a system to learn to recognize patterns on its own and make predictions, contrary to hand-coding a software program with specific instructions to complete a task.
While Deep Blue and DeepMind are both types of AI, Deep Blue was rule-based, dependent on programming — so it was not a form of machine learning. DeepMind, on the other hand — beat the world champion in Go by training itself on a large data set of expert moves.
That is, all machine learning counts as AI, but not all AI counts as machine learning.
Deep learning is a subset of machine learning. Deep artificial neural networks are a set of algorithms reaching new levels of accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.
It uses some machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be costly and requires huge datasets to train itself. This is because there are a huge number of parameters that need to be understood by a learning algorithm, which can primarily yield a lot of false-positives. For example, a deep learning algorithm could be trained to ‘learn’ how a dog looks like. It would take an enormous dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox.
Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol in 4 out of 5 games of Go using deep learning in early 2016. Google said, “the way the deep learning system worked was by combining Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”
As there is a lot of talk going around India’s smart cities, it was only natural that IoT or Internet of Things, which is considered to be an important component for smart cities across the world, to be integrated in the government’s flagship smart cities mission project, to make them more intelligent and self-reliant. And today, we are seeing that IoT has become an integral part of smart cities, so much so that for each one of our needs, the developers want to offer an IoT solution.
Role of geospatial technologies in IoT
Now drawing the connection between geospatial technologies and IoT, this study explains that in a network of billions of connected devices, to identify and operate one device remotely would be very challenging unless the device knows its geographical location on Earth. Every element that will be connected in this network must identify their unique identification, location and functionalities in order to function properly.
The concept of IoT is being developed to make the internet even more immersive and pervasive. So that a wide variety of devices such as, home appliances, surveillance cameras, monitoring sensors, actuators, displays, vehicles, and so on, can be fostered under one network of connected devices.
Employing IoT will enable the development of a number of applications that make use of potentially enormous amount and variety of data generated by such objects to provide new services to citizens, companies, and public administrations. For instance, when we talk about smart cities, we don’t think of cities that have the word – smart, only in their name, instead, we think of cities that work from to their own intelligence.
For a better understanding of how it works, let’s say that you live in a smart city and you are planning to go out for dinner. But first, you want to buy that dress you were checking out last week at the mall near you. So you pick up your phone and you log on to that fashion store’s website and you choose the dress, select the size, pay for the bill, and order the dress to be delivered to your home. The website’s server immediately takes a note of your request and sends an alert to the store-staff about the order. The staff immediately processes the order and sends out a drone to deliver your package. Then the UAV flying across the city, lands in your apartment’s balcony and drops the package and returns to its owner. Immediately, you get a notification on your phone saying that the package has been delivered. And you go check in balcony where it’s waiting for you. Similarly, there can be numerous example where the devices are connected to each other, working in synergy to facilitate your needs.
The US city, Denver is a better example of this. Located south of Denver International Airport, the futuristic neighborhood of Pena Station Next began getting smart LED street lights last month. The technological implementation of IoT in Denver is being planned by Panasonic, where the company has covered the parking with solar panels, plus a storage microgrid is almost done. The area is being developed for the future’s autonomous shuttles to transport residents to the nearby RTD rail stop, shops and restaurants.
Getting developed by Panasonic, the city – Denver was chosen to create a smart city lab and test different technologies. Click here to see some of the future technologies that may show up in Pena Station Next.
According to a report, many industry experts and excited consumers have pegged the IoT as the next industrial revolution. Whereas, according to some, the power of IoT is applied to more technology use cases – keep it grounded and real. Now it’s a data first world long-term as we collect the data and determine what it can tell us about interoperability, cross correlation benefits. The web of devices is correlating – triangulating disparate data to obtain unforeseen insights.
Based on an analysis done by management consulting firm, McKinsey, the total market size of IoT that was up to $900M in 2015, is expected to rise by $3.7B in 2020 attaining a 32.6% CAGR.
Last year during 10th SPIE Asia-Pacific Remote Sensing Symposium being organized in New Delhi the then-NASA chief Charles F Bolden and ISRO chief A S Kiran Kumar Rao unveils about NISAR. A joint venture of NASA and ISRO, NISAR is scheduled to be launched by 2021. Other than earthquakes, the satellite is designed to study and measure some of the most complex natural processes of the Earth, like ecosystem disturbances, ice-sheet collapse, biomass estimation etc. It will also give inputs on agricultural production activities. The announcement was made at the 10th SPIE Asia-Pacific Remote Sensing Symposium being organized in New Delhi last year.
Lets admit it! Most of us have seen and read that fake news, believed it and even shared it without verifying the facts.
As reported in a section of media that Ebola was wreaking havoc in Texas. It was spreading like wildfire on the social landscape and these were messages from sources that sounded like authentic. This happened in 2014 and what followed were series of fake news sending shock waves across the media.
As per The New York Times, just before the presidential elections in the US, fake news and memes became the tools for perpetrators to influence the outcome of elections.
Any news becomes a fake news if the information being given is incorrect or information doesn’t represent facts that it is expected to carry.
It is important to identify if the news we are reading or watching is fake or true. And it is equally important that we dig into the fake news, identify it and prevent people from believing distorted facts.
For journalists, running after news, chasing deadline, cut throat competition, makes it very difficult to authenticate the source of the content at times. The sheer pressure of breaking the news as fast as you can leaves them with hardly anytime to verify the facts. We are flooded with so much of information that it seems impossible to identify if the content we are watching or reading is based on facts or just fake. So what’s the way out? How do we filter fake from facts?
Artificial Intelligence can rescue us as it provides certain elements, which can help us, rate the news for authenticity. Artificial intelligence is being used to find words or even patterns of words that can throw light on fake news stories.
Automated tools powered by Artificial Intelligence and Machine Learning algorithms that can combat fake news.
Once Big Data came into the picture, Artificial Intelligence and Machine Learning tools became more sophisticated and reliable.
So, how will Artificial Intelligence help us scan each and every news source?
Ways to identify fake news
An NLP engine can go through the subject of a story along with the headline, main body text, and the geo-location. Further, Artificial Intelligence will find out if other sites are reporting the same facts. In this way, facts are weighed against reputed media sources using Artificial Intelligence.
Score Web Pages
Scoring web pages is a method pioneered by the tech giant Google. Google takes the accuracy of facts presented to score web pages. The technology has grown in significance as it makes an attempt to understand pages’ context without relying on third-party signals.
With a Machine Learning model, a website’s reputation can be predicted through considering features like domain name and Alexa web rank.
Discover Sensational Words
When it comes to news items, the headline is the key to capturing the attention of the audience. Artificial Intelligence has been instrumental in discovering and flagging fake news headlines by using keyword analytics.
Are There Tools to Combat Fake News?
Forbes.com had given a summary of some tools used to fight fake news.
Spike is a tool leveraged to identify and predict breakout stories as well as viral stories. The tool analyzes mountains of data from the world of news and predicts what’s going to drive engagement
Hoaxy is a tool that helps users to identify fake news sites
Snopes is a website that helps spot fake stories
CrowdTangle is a tool that helps discover social content early and monitor content
Check is a tool from Meedan that helps verify news breaking online
Google Trends proves its worth by watching searches
Le Decodex from Le Monde is a database that houses websites that are tagged as ‘fake’, ‘real’ among others
Pheme has made a technology leap to read the veracity of user-generated and online content
Fake news is spreading like a plague in the media. Artificial intelligence and Machine learning is the way to tackle fake news items. We also need to focus on educating people, to be critical thinkers, to question and to not take every story at face value.
The German parliament recently approved a law, which permits autonomous cars to drive on roads. However, the law also states the car to have a driver at all times to take control in case of emergency. The new legislation allows Germany to road-test autonomous cars. The testing also requires a black box for recording the journey underway.
The Ministry of Urban Development, Government of India recently conducted a survey to determine the cleanest city in India. The city of Indore has bagged the top position. Incidentally, Indore is also amongst the Top 20 Smart Cities announced by the Government of India.
Evidently, apart from the dedicated efforts put in by the authorities and the people of Indore, geospatial technology has also been a vital player in ensuring this ranking for the city.
Indore Municipal Commission (IMC) ensured that all of its waste collection vehicles (approximately 400), catering to both households as well as commercial establishments were fitted with GPS receivers. Along with the involvement of a few NGOs of the city (who helped in drafting pre-defined routes for each vehicle), this ensured that drivers knew the exact route to be followed for waste collection day in and day out. In this manner the city made sure that tracking the movement of its vehicles along pre-defined routes will ensure timely and structured collection of waste from each and every household.
Making small beginnings with the technology, the city officials have already envisaged a brighter geospatial future for the city of Indore.
Web GIS Portal
In the First Phase, a Web GIS Portal for Citizens is planned to be made available, which shall allow Citizens to locate administrative units, Office Locations, available facilities (like petrol pumps, hospitals, schools etc.). This portal shall also be helpful for officials for making better planning decisions.
In the second phase, the GIS portal will be enhanced and integrated with future planned systems like; Intelligent Transport Management System; Intelligent Utilities Management System (Water, Gas, Power, Sewage and Road); Intelligent Solid Waste Management; Intelligent Vehicle Tracking System; Intelligent Parking Management System; Smart Pole Data Management System; Building Plan Approval System; Workflow based Municipal NOCs Processing System; Health Information Management System; Enterprise Project Management System; and Enterprise Asset Management System.
The Enterprise GIS level system to be made operational will provide the required framework to integrate not only every stakeholder but also every aspect of smart city processes – starting from conceptualization, planning, and development to maintenance. The system shall play a vital role in many areas of Indore Smart City, some areas where the benefits of unique capabilities of GIS can be quickly leveraged include Asset Management-Manage Asset and resource information; Planning & Analysis-Facilitate better planning and analysis; Field Operations-Get information in and out of the field; Situational Awareness-Provide comprehensive view of operations; Constituent Engagement-Engage stakeholders and citizens.
Simultaneously, the vehicle tracking and monitoring solution, being currently used for waste collection vehicles, is being taken forward to its logical second stage. IMC has already planned to implement rigorous monitoring of vehicles to stop pilferage of fuel and reduce fuel consumption by monitoring fuel efficiency of vehicles (through the use of existing GPS trackers and GIS based analytics).
Monitoring and managing Smart City
Apart from this, geospatial technology is also being planned to be utilized for monitoring and managing components of Smart City which includes (but not limited to) the following:
Intelligent Transport System with smart signaling | Smart Parking | Real time air quality monitoring/ Environmental sensors | Smart Energy Meters with net metering | Smart Distribution grid | SCADA System (for water, wastewater & sanitation) | Smart Water meters | ICT based SWM | Pubic Wi-Fi Hotspots | Area Command and control center | Smart Classrooms with Wi-Fi | Sensor based energy based street lighting | Installation of multi-use CCTV cameras | Smart Hospital Management System (including Hospital Information, Electronic Medical Record and Genomics) | Planning for eco-sensitive areas, preparation of Green and Blue Master Plans, Climate Change/Vulnerability and Hazard Modelling etc. | Monitoring and managing Property tax collection | Monitoring and managing e-services | Building plan approval system | Project management
GIS-based property tax survey is under process which aims to provide 27 GIS layers covering rivers, roads, urban agglomerations, vacant lands, villages, settlement boundaries, waste lands etc. Apart from this, GIS database for water and sewer network has also been prepared for the entire city.
There is no doubt that geospatial technology will be the key tool in establishment and management of smart cities. Indore is showing the way, other cities are also firming up their geospatial plans. One hopes that all the technology stakeholders come together to remove bottlenecks in technology adoption and truly realise the power of geospatial technology in realizing the vision for Smart Cities.