In the past 50 years, the frequency of natural disasters has increased by 500%. From 2005 to 2014, more than 700,000 people died in natural disasters around the world. Besides their death, the damage inflicted from these events amounted to $1.4 trillion USD.
Among the types of natural disasters recorded were wildfires like the ones that have created a new season called “fire seasons” in countries like Portugal, Spain, Australia, Brazil, and provinces and states such as British Columbia, and California. One might question calling wildfires natural disasters since human behaviour, not just droughts is a strong contributing factor. Nonetheless, the need to develop prevention and rapid response to these events has never been more needed particularly since human population growth has increasingly encroached on the natural world.
I have taken the liberty to edit the raw material Peter has sent but have stayed true to his intent as he describes the development of technologies such as artificial intelligence (AI), robotics, drones, sensors, and smart networks and their application to preventing, predicting, and responding in the first hours to help victims of these events. In this posting he looks at three technology groups specifically. They include:
When it comes to immediate and high-precision emergency response data is gold. We are witnessing the deployment of global space-based telecommunications networks and the rollout of 5G with the potential to connect every individual on this planet. Aside from democratizing the world’s information the upsurge in connectivity will soon grant anyone the ability to broadcast geotagged data, particularly useful in the face of a natural disaster. This is crowdsourcing that can play a vital role in emergency response, turning historically one-way blind rescue operations into a two-way dialogue between connected crowds and smart response systems.
With this skyrocketing abundance of data comes a new paradigm, one in which we no longer face a paucity of answers. Instead, it will be the quality of our questions that matter. And this is where AI comes in. What if we could map all incoming data points, or predict the dynamics of a flood, identify a tsunami’s most vulnerable targets, or plot the path of a storm? Through the power of volunteer crowdsourcing we can create relatime information updates to help optimize crisis response.
Born out of Stanford University under the mentorship of leading AI expert Andrew Ng, One Concern is a using AI machine learning to predict and quantify the impact of natural disasters, and to build more resilient businesses, infrastructure, and global communities. Partnering with the City of Los Angeles, San Francisco, and numerous places in San Mateo County, One Concern assigns verified, unique digital fingerprints to every location. It builds robust models of each system and then monitors site-specific impacts from climate change, seismic movements, and other natural disasters. It combines this data with legacy-created data about city infrastructure and former disasters, and then uses all of it to predict future damage under a range of disaster scenarios along with advice on prevention and mitigation. In its four years of existence, One Concern can now use its AI to make precise predictions with 85% accuracy in less than 15 minutes.
Adam Kochanski and a team of researchers at Utah University are developing an AI forest fire mapping algorithm built using historic and new data to predict how fires spread and what weather events follow in their wake. The team has used predictive forest fires (set deliberately for the purpose of spurring forest regrowth) accompanied by infrared-camera-fitted drones, laser scanners, and sensors to collect data to build a predictive model. The experiment is contributing to “coupled fire-atmosphere models,” which leverage collected data to predict how wildfires influence local weather conditions and the interaction that occurs between the fires and spawned weather. Kochanski’s model is proving to be remarkably accurate in predicting wildfire behavior. It is hoped that the tool will help to stem early fires before they gain momentum, saving forests, lives, property, and entire ecosystems.
The spatial web isn’t limited to a set of wired access points and wireless hotspots. The spatial web acts as a mesh network connecting entire cities using hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically. This puts the power of networking and crowdsourcing intelligence into the hands of every smartphone user.
How can this be used?
Imagine a scenario in which armed attacks break out suddenly across different parts of a city. Clusters of eyewitnesses and at-risk civilians can broadcast an aggregate of 360-degree videos, all fed to a photogrammetry AI that builds a live hologram of the ongoing events to give first responders and those potentially affected a complete visual map.
A coastal community in the throes of torrential rainfall leading to flooding and failed infrastructure can have eyewitnesses collecting live feeds that can be combined with existing maps and data to provide first responders with accurate reporting to specify where relief is needed first. Drones tied into the crowdsourced intelligence could be directed to provide live video feeds, while swarms of drones and robots could gather emergency supplies, and deliver them to where they are needed. This scenario leads us to our next subject.
As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace. Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that could form a first line of defense in everything from earthquake search-and-rescue missions to high-risk operations in radiation zones. The Cheetah III can run at a speed of 6.4 meters per second
Now capable of running at 6.4 meters (21 feet) per second, and leap heights of 60 centimeters (approximately 2 feet), doing obstacle avoidance autonomously.
Initially designed to perform spectral inspection tasks in hazardous settings such as nuclear powerplants or chemical factories, the Cheetah III’s various iterations focus on increasing payload capacity, extending range of motion, and even adding an arm for dexterity. As explained by Associate Professor Sangbae Kim, of MIT, the Cheetah III, and future versions is and will be aimed at saving lives in almost any environment. Kim says, “Let’s say there’s a fire or high radiation, [whereby] nobody can even get in. [It’s in these circumstances that] we’re going to send a robot [to] check if people are inside. [And even] before doing all that, the short-term goal will be sending [the] robot where we don’t want to send humans at all, […] for example, toxic areas or [those with] mild radiation.”
Cheetah III is not alone. In February this year, Tokyo’s Electric Power Company (TEPCO) put a robot to the test in the Fukushima nuclear power plant that was heavily damaged in the 2011 tsunami. The robot was able to enter the reactor to retrieve chunks of radioactive fuel at five of six test sites, offering promise for long-term planning to clean up the still-deadly interior.
And Japan’s Mitsubishi Heavy Industries (MHi) is using robots to fight fires autonomously. The MHi Water Cannon Bot can put out blazes in difficult-to-access or highly dangerous fire sites delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure. The Cannon Bot and its accompanying Hose Extension Bot form part of a greater AI-geared system. As wildfires grow more untamable, using bots like these could prove a lifesaver. Paired with the predictive AI forest fire mapping described earlier, and delivered to fire sites by autonomous vehicles, solutions like the Cannon Bot means pre-emptive measures will limit fire outbreaks and fewer human firefighters can focus on search and resuce while robots like the Cannon Bot can handle the flames.
And then there is the robot that can build emergency shelters as needed during times of natural disasters. The Hadrian X, from Fastbrick Robotics, can build a fully livable, 180-square meter home in less than three days all on its own using 3D modeling, a robot arm, and a supply of bricks.
Imagine deploying the Hadrian X and similar autonomous solutions in post-disaster and conflict sites to help with recovery.
But in the absence of bricks a new 3D printing robot called Pylos, developed at the Institute of Advanced Architecture of Catalonia (IAAC), converts almost any soil into building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low greenhouse gas emissions (GHGs), fire protection, air circulation, and thermal mediation, Pylos can build a house on-site for material costs of as little as $1,000 USD.
Next-Generation Relief and Supply Drones (UAVs)
Autonomous drone technology is fundamentally revolutionizing the way we find survivors in need and automate relief supplies. Using high-resolution imagery for real-time mapping and damage assessment, UAVs can far outpace any ground-based rescue team in locating isolated survivors. Drones in China are even being used to build a temporary mobile wireless broadband network in the event of a disaster with the drones acting as cell towers.
Drones have helped assess flood damage in the aftermath of Hurricane Harvey. Texas A&M during Harvey flew 119 drone missions over the City of Houston using them to monitor levee infrastructure and to identify those in need of rescue.
But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe. Take for example Zipline which has been used in places like Costa Rica, Rwanda, and Tanzania to deliver medical supplies from vaccines to HIV medications, to blood and intravenous tubes. Zipline has proven it can outpace ground-based supply transport to provide critical supplies in less than an hour.
There are even drones capable of delivering much larger payloads. Under contract with DARPA and the U.S. Marine Corps, Logistic Gliders, is building autonomous drones capable of carrying payloads of up to 816 kilograms (1,800 pounds) over long distances. The gliders are built out of plywood and cost a few hundred dollars to produce making them perfect candidates for high-volume, remote aid delivery. They can be navigated by remote control using a human pilot, or fly on their own based on disaster zone mapping data.
One of the most consequential and impactful applications of the converging technologies described above is their ability to transform disaster relief methods. AI-driven intelligence platforms, crowdsourced firsthand experiential data, mobile connectivity, and drone-supplied networks are granting newfound narrative power to those most in need. And as new hardware advancements give rise to robotic responders, swarm technology and aerial drones, we are fast approaching an age of instantaneous and efficiently distributed responses in conflict zones and in the aftermath of natural catastrophes.
]]>Diamandis selects three technologies that he believes will have the greatest impact on dealing with the aftermath of or the prevention of disasters. These include:
When it comes to immediate and high-precision emergency response, data is gold. The meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is connecting every last individual on our planet. Aside from democratizing the world’s information, this upsurge in connectivity is granting all of us the ability to broadcast detailed geo-tagged data, which is particularly valuable in the face of natural disasters. Armed with the power of data broadcasting and crowdsourcing, disaster victims can now play an important role in emergency response, turning one-way blind rescue operations into a two-way dialogue, connecting the crowd to smart response systems. With the skyrocketing abundance of collected data comes a new paradigm, one in which we no longer have few answers, but rather now can ask the questions needed to ensure we can address the disaster in near real-time.
In the case of emergency response, we can strategically map an almost endless amount of incoming data points. We can begin to predict the dynamics of a flood and identify a tsunami’s most vulnerable targets even before it strikes. And we can trigger automatic aid using surveillance drones which can immediately alert volunteers to take action. Already, a number of organizations are leveraging AI, crowdsourced intelligence, and cutting edge visualization to improve crisis response and speed up relief efforts.
One of these is One Concern. Conceived by victim survivors of past catastrophes and geologists studying volcanoes, this organization leverages AI through analytical disaster assessment to calculate damage estimates. So far One Concern is partnering with a number of California cities including Los Angeles and San Francisco to create unique ‘digital fingerprints’ for every element in an urban area. Their AI platform creates robust models which then help to monitor site-specific impacts from climate change, earthquakes, and other natural disaster events. The collected data when combined with information from these cities including their histories of previous disasters, is then used to predict future damage providing recommendations on structural improvements for prevention and mitigation. Since its inception four years ago One Concern has grown in its ability to make predictions with 85% accuracy in under 15 minutes.
Besides handling natural disasters, there are other uses for crowdsourced intelligence for mapping potential crises. The story of Ushahidi is a case in point.
Ushahidi is the Swahili word for “testimony.” The technology came from a local blogger’s idea to collect reports of violence in Kenya in the 2008 post-election period and map their locations. He sent out a request to techies to create a mashup of where violent and destructive episodes were being reported. Within days, four techies built a platform to crowdsource first-hand reports through short message services (SMS), collecting over 40,000 verified reports and sending back alerts to locals on the ground and viewers around the world.
Ushahidi today is being used in more than 150 countries, with over 100,000 deployments impacting 20 million people. Its open-source crisis-mapping software is accessible to anyone, mining millions of tweets, hundreds of thousands of news articles, and geo-tagged, time-stamped data from countless sources all posted to the Cloud.
Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in crowdsourcing witness reports, providing real-time geographic visualizations of all verified data, and enabling civilians to report missing people, civilian casualties, disease outbreaks, and relief supply needs all while avoiding the government-controlled cellular network.
As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale. Within the next 10 years, spatial web technology may even let us tap into mesh networks. While traditional networks rely on wired access points and wireless hotspots, the spatial web connects entire cities using hundreds of dispersed nodes that communicate with each other and share network connections non-hierarchically, which means that individual mobile users can establish a local mesh network together using nothing but the computing power of their handheld devices.
If taken further, a local population of strangers can collectively broadcast countless 360-degree feeds across a mesh network. So imagine a scenario in which armed attacks break out across disjointed urban districts. Eye-witnesses and at-risk civilians will be able to broadcast an aggregate of 360-degree videos, all fed through photogrammetric AIs that will build a live hologram in real time to create complete information for first responders. The technology could help a community facing failing infrastructure from a hurricane’s landfall providing a collective live feed of verifiable data in real-time to inform first responders about specific relief needs.
With all the right technology pieces in place, first responders will be able to use drones for automated deliveries. Imagine crowdsourced intelligence, cross-referenced with sensor data and verified algorithmically, along with AI to determine the specific needs and degree of urgency with precision coordinates. Within minutes swarm robots can rush to collect requisite supplies, equip size-appropriate drones and provide rapid-fire delivery.
But wait, there are other breakthroughs on the horizon with AI-geared robots paving the way in emergency response for some of today’s most dangerous environments. These include autonomous robots and swarm solutions.
As hardware advancements converge with AI we will see the emergence of fully autonomous disaster relief robotic responders. A prototype is the Cheetah III, a product of MIT’s Biomimetic Robotics Lab. Cheetah III is capable of running at a 6.4 meters (21 feet) per second pace, can leap heights of 60 centimeters (almost 2 feet), and has sophisticated collision and obstacle avoidance capability. Robots like this may form the first line of defense in disaster responses to earthquakes for search-and-rescue missions, or for surveillance in high-risk radiation zones. Future plans for the robot include payload carrying capability, enhanced range of motion, and increased dexterity. Stated Biomimetic Robotics Lab Director, Associate Professor Sangbae Kim, “the short-term goal will be sending the robot where we don’t want to send humans at all.”
Joining the Cheetah III are a number of other robots designed for working in hazardous zones. In February Japan’s Tokyo Electric Power Company inserted a robot into the damaged Fukushima Nuclear Reactor to retrieve a number of samples of radioactive fuel for analysis and cleanup planning on the site. This robot was guided by human operators and was able to operate in an environment where no one could safely go.
Also in Japan, Mitsubishi Heavy Industries (MHi) has built a firefighting robot called the Water Cannon Bot designed to put out fires at sites considered too dangerous for human entry. It can deliver foam or water at a rate of 4,000 liters (over a 1,000 gallons) per minute and at pressures of 1 Megapascal (145 PSI). The robot can be accompanied by a companion Hose Extension Bot extending the firefighting range by 300 meters (almost 1,000 feet). Imagine using this kind of robot to fight wildfires when combined with predictive AI forest fire mapping and autonomous hauling vehicles, and you have a solution for what is becoming a growing problem as climate change dries out many forest areas. Deployment in anticipation of wildfire would mean the avoidance of population displacement, unnecessary deaths, and significant damage to natural environments.
There are RoboBees developed at the Wyss Institute, Harvard University. These autonomous flying micro-robots have the potential to be used for crop pollination, search and rescue missions, surveillance, and high-resolution weather, climate, and environmental monitoring. They feature self-directed flight and swarm behaviour, that is the RoboBees can work together as a single unit for co-ordinated surveillance operations.
There is Snakebot, developed at Carnegie Mellon University, and designed to navigate in environments completely inaccessible to human responders. Driven by AI, a Snakebot can maneuver through densely packed rubble to find survivors and has onboard cameras and microphones to aid in communication.
Advancements in drone technology will revolutionize disaster response whether to natural phenomena or wars. Drones provide high-resolution imagery for real-time mapping and damage assessment and are already outperforming ground-based rescue teams in locating isolated survivors of a catastrophe. In the aftermath of Hurricane Harvey, a team led by Texas A&M Computer Science Robin Murphy flew 119 drone missions over Houston, to monitor levee infrastructure and identify people left behind by rescue teams.
But drones are capable of much more than surveillance. At the University of Science and Technology of China, engineers have built a first responder temporary mobile wireless broadband network facilitated by airborne drones. And drones are being used to supply remote areas of the planet with lifesaving medicines. One of these is Zipline, a drone delivery system operating in Rwanda and Tanzania that is delivering vaccines, HIV medications, blood products, and intravenous supplies to 11 million living in remote locations. Zipline, a California-based company, has far outpaced any ground-based supply transport, in many instances providing lifesaving biomedical products in under an hour.
Larger capacity aerial drones are being developed for the U.S. Military. Logistic Gliders is using plywood-constructed gliders costing only a few hundred dollars to provide the U.S. Marine Corps with aerial resupply capacity at 816 kilograms (1,800 pounds) per flight. Flying at speeds of up to 250 kilometers (135 nautical miles) per hour, and a 140 kilometer (80 miles) range, these drones can be released from an aircraft or helicopter and are disposable. They can operate autonomously or be guided remotely.
Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods. While AI-driven intelligence platforms can crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those placed in need because of natural or human-caused disasters. It is this rise of robotic responders, swarm technology and aerial drones that are giving us unprecedented response capacity to catastrophes. Empowered by these new tools, how much can we improve humanity’s overall plight when everyone has similar access to relief supplies and immediate resources in times of disaster?
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