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A short circuit in the wire led to a fire at Notre Dame de Paris, and 3D digital technology may help rebuild it.

a serious fire broke out at Notre Dame de Paris, France, on Monday. A sudden fire burned this 850 year old world treasure, which is distressing. However, the media said that there was hope for a perfect reconstruction of it - a detailed 3D map of Notre Dame de Paris had been produced a few years ago

a serious fire broke out in the Notre Dame Cathedral in Paris, France, on Monday. A sudden fire burned this 850 year old world treasure, which is heartbreaking

the cause of the fire may be a short circuit in the wires on the top floor

according to French media reports, the fire started on the top of the building, spread rapidly, and flames shot out from the two bell towers of the church. The fire destroyed most of the roofs and devoured the spire of this landmark building

France mobilized 400 firefighters to fight the fire. After five hours of fighting, the fire was gradually brought under control

it is reported that the judicial department has launched an investigation into the cause of the fire, and foreign media reported that the cause of the fire at Notre Dame de Paris may be a short circuit in the wires on the top floor

French media said that the fire may have started from the scaffolding on the roof, and the repair work here had been going on for several months at the time of the incident. It is worth mentioning that the latest news shows that the altar and cross of Notre Dame in Paris survived the fire, and some precious cultural relics, including the crown of thorns, were saved

3d map or help the reconstruction of Notre Dame in Paris

President macron promised to rebuild this complex Gothic cathedral, but the reconstruction work will not be easy and may take years or even decades

however, the media said that the hope of perfectly reconstructing it was the existence of the detailed 3D map of Notre Dame de Paris, which had been produced a few years ago

Andrew Tallon, an associate professor at the school of art of Vassar College, previously used laser scanning DWC impact experiments. The low-temperature tank is a special auxiliary refrigeration equipment for low-temperature impact experiments, creating a perfect and accurate model of Notre Dame de Paris

andrew Tallon on-the-spot investigation

Talon died last November, but he told the media before his death: install the laser beam on the tripod, scan around the Cathedral Choir, and measure the distance between the scanner and each point it scanned. Each measurement is represented by a color dot, which cumulatively forms a three-dimensional image of the Cathedral. In this way, the accuracy of scanning is within 5 mm

the technology used for 3D archiving is called 3D laser scanning/lidar, which can accurately (mm accuracy) and quickly (measure hundreds of thousands of points per second) obtain 3D geometric information of buildings. The data obtained is called laser scanning data or point cloud data

in his work from 2014 to 2015, he found that the gallery of kings has almost moved a foot from the plumb bob, and this area of the cathedral may remain unchanged for ten years before the work began, allowing time for the soil to settle. His work also shows that the internal columns of Notre Dame de Paris are not perfectly aligned

although Andrew Tallon has died, I hope his work during his lifetime can play a role in the reconstruction of Notre Dame in Paris

what can AI do in the face of fire

as early as 2016, researchers at NASA's Jet Propulsion Laboratory in California were using AI to help firefighters. AI can collect relevant data of temperature, gas and other danger signals, so that firefighters can make a more accurate comprehensive assessment of the fire situation, and guide firefighters to pass through the fire safely when performing tasks

the new AI system jointly developed by NASA Jet Propulsion Laboratory and the U.S. Department of homeland security can guide fire-fighting activities, and is expected to improve fire-fighting efficiency and reduce casualties.

the system is named Audrey, which realizes the purpose of assisting fire fighting through the reasoning, extraction and interpretation of data related to the fire environment. Audrey uses IOT technology to realize wireless interaction between many fire-fighting equipment and sensor information. Wearable sensors in firefighters' clothes can obtain GPS location information, heat information of other rooms, hazardous chemicals and gas composition information, as well as satellite images

nasa creates a cloud guardian angel for firefighters

as a cloud based software, Audrey can not only send data to field personnel, but also learn and predict subsequent resource needs. Audrey is the cloud guardian angel of firefighters, said Edward Chow, project manager of Audrey. Because the sensor can detect all the data, it can prevent firefighters from entering the room that will collapse immediately

chow indicates that the effect of AI fire fighting is related to the amount of data analyzed and learned. The more data, the more likely AI is to make useful suggestions. We use complex reasoning to simulate human thinking. This enables us to provide firefighters with more useful information than traditional AI systems

on September 19, 2018, Audrey project researchers and local fire department personnel conducted a simulated fire field test on the system. The building simulating the fire is a special cabin composed of modern combustibles, equipped with complex thermocouple sensors, advanced thermal imaging and visual imaging equipment, and even a heat-resistant 360 camera

With the help of AI, satellite positioning forest fires only take a few minutes

in November 2018, the camp fire forest fire in California, USA, caused a total of 85 deaths, 249 missing, heavy casualties, and more than 18000 buildings were destroyed. For a long time, the successful prediction, early detection and early suppression of forest fires have been the goals of people's efforts

as we all know, it is very difficult to predict forest fires. At present, most fires are reported through 911, commercial flights or fire lookout stations. This inefficient reporting method makes some forest fires undetectable for hours or even days after the fire

the star image of camp fire forest fire satellite taken by NASA during the process of dynamic loading experiment. At present, two NASA satellites orbiting the earth scan almost the whole earth every day and can find the thermal characteristics of the fire. This process takes at least three hours. During this period, the satellite needs to cross the space flight center outside Washington, D.C., transmit data downward, and run images through a supercomputer

however, James MacKinnon, an engineer at NASA, said that AI neural networks can shorten the process to a few minutes. He used satellite images from all over the world with a time span of up to one year to train the system, and the accuracy of the system for fire identification is as high as 98%

post disaster information summary: use social media to find out the situation in 30 minutes

social media also plays an irreplaceable role in the information exchange of post disaster recovery. Disaster response AI (aidr) is a social open platform for marking and discussing post disaster emergencies and information. Aidr uses machine learning to classify millions of tweets and Facebook posts about disasters

emergency disaster relief personnel can input the list of keywords to be found into the system to train the system, such as campfire, or parade fire, or improve the competitive advantage of relevant industries from it to extract the geographical areas in social media information. The system can understand the situation of the disaster area in only 30 minutes

to this end, the Facebook research team created an index called disaster impact index (DII), which can measure the damage caused by natural disasters in a certain area, and this index can be used to evaluate the loss caused by fires

this study was jointly completed by saikat Basu, Guan Pang, researchers from Facebook AI research department, and Jigar Doshi, head of machine learning at crowd AI company. At present, the accuracy of this evaluation tool based on convolution neural network has reached more than 80%

(comprehensively collated from Tencent technology, globegroup, and Xinzhiyuan)

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