Project Joins Startup Impact Observatory and Microsoft to Deliver Map to Worldwide UsersEsri, the global leader in location intelligence, today announced it is releasing for the first time ever a new high-resolution, 2020 global land cover map as part of the company’s Living Atlas.

The map was built using European Space Agency (ESA) Sentinel-2 satellite imagery, and developed using a new machine learning workflow teaming with new Esri Silver Partner Impact Observatory, as well as long-time partner Microsoft.The new map will be updated annually supporting change detection and highlighting planetary land changes, especially related to the effects of human activity.

A consistent map of land cover for the entire world based on the most current satellite information, the 2020 Global Land Cover Map can be combined with other data layers for green infrastructure, sustainability projects, and other conservation efforts that require a holistic picture of both the human and natural footprint on the planet.

Later this year, Esri and Impact Observatory will make this new land cover model available to support on-demand land cover classification, allowing the GIS community to create maps for project areas as often as every week.“This is a critical year for climate action,” said Jack Dangermond, Esri founder and president.

“With the UN Climate Change Conference of the Parties (COP26) bringing international parties together to address a set of common goals, we are happy to do our part in making this map available to users that are working towards the health of our planet.”Users will also be able to manipulate the map in association with other GIS layers such as terrain, hydrology, and more, all available in ArcGIS Living Atlas of the World, the foremost collection of geographic information from around the globe, including maps, apps, and data layers.

Through the visualizations being released, planners worldwide will better understand the geography around them to make more informed decisions—enabling them to gain insight into locations with distinctive land cover, as well as human activity affecting them.High-resolution, open, accurate, comparable, and timely land cover maps are critical for decision-makers in many industry sectors and developing nations.

At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.There are also intelligent algorithms that can use a lot of data to make accurate predictive behavior of people and clients.

However, although these concepts are all linked, they are not the same thing.As intelligence experts explain, different parts of AI are positioned as Russian nesting dolls.

Artificial intelligence is “smart” because it can follow very complex instructions without responding to a single or basic trigger.In recent years, AI has gained in popularity, thanks to the increase in available GPUs that make parallel processing easier, cheaper, and more accessible.

With machine learning tools, it is possible to establish computer algorithms that are searchable by data and apply heaps of knowledge and training to a specific task.For example, machine learning service can use millions of face images to identify specific people or certain features on the face.

The artificial intelligence we have today falls into the categories of narrow AI and artificial general intelligence.Narrow AI is a “weak” AI that works in a limited context.

So, how does machine learning work?Machine learning uses two basic methods to deliver results.

Artificial Intelligence (AI) is the tech buzz phrase of 2020.

Amazon Echo ShowThe current range-topping device with Alexa allows you to do everything from checking relatives in the next room or on the other side of the world to buying pants from Amazon to stay away from your daily schedule.The 7-inch screen allows you to make video calls to other show users, which is great, but can also get a brief peak, for example, the appearance of an elderly relative (with their permission, of course) should disturb them.

Great for peace of mind.Not only does Amazon’s Alexa AI provide you with music based on advanced voice search terms — “play sad pop songs from the 1980s,” for example — it also learns to understand you better, the more you use it.2.Kuri Home RobotAlthough Kick starters and Prototypes have come and gone, we haven’t seen many Home Assistant robots come into the mainstream.

With the advancement of AI technology and reduced hardware costs, it may be time for robots to truly be useful around the home, and Kuri is leading the charge.Music can be played by these robots, take photos and videos on the fly, answer your questions, and engage in some conversation, thanks to the AI smarts hidden behind its mechanical eyes.

The gadget also includes mapping sensors, which allow the Kuri to move around the dining room tables and chairs in the living room.Two other areas where the face and voice recognition robot’s artificial intelligence features take effect, so Kuri knows who is talking and can change its responses.To read about: The Revolution Of Artificial Intelligence TechnologyYou can configure Kuri via the affiliate app (to wake you up in the morning), as well as review photos and videos that Kuri has captured while traveling around your home.

By learning when and how to use your heating, the Nest Thermostat can help you use your heating more economically and efficiently.Allow it to use your phone’s location, and the nest automatically turns off heating when other members of your home are gone.

Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers.

This has led to a profusion of initiatives to use such trained models in the domain of Health and Safety (HSE) at workplace.

The output of the model can be used to send notification or drive other processes.The following are some of the interesting initiatives that we are working on training computer vision models to improve health and safety in the workplace.Our Initiatives:PPE (Personal Protective Equipment) Compliance                        Improper adherence to Personal Protective Equipment in industries is a big problem.

Employees don’t wear PPE either due to lack of knowledge on safety or due to the perceived inconvenience in wearing PPE.

We have assembled a detailed data set of workers wearing different PPE and have trained models that can accurately detect if the required PPE is worn by an employer or not.

The environment is very harsh and corrosive, and the truck drivers need to be very careful and diligent in following the sequence of procedures to unload the cargo.

It is important as it helps to identify existing or/and potential hazards.

Many of these lines are inaccessible due to a lack of transportation and geographical locations.

The undetected faulty gas lines and power lines had led to a forest fire.

They also face certain limitations such as inaccessible terrain, contact with high-voltage power lines, hazardous chemicals emission, and more.Using Drones for Utility InspectionsDue to the above, many companies have started to use drones as the inspectors can survey the structures or lines from a safe distance and can cover difficult terrains & conditions with ease.

Few examples of utility inspections carried out using drones are:Power transmission line– to identify foliage encroachment, sagged wires, fuels buildup that leads to forest firesVertical constructions– to check signs of irregularities and damageBridges & overpass– to check signs of damage or cracksWater systems– to identify leakages, management of vegetationDams– to check structural defects and identify the repairs neededDrones improve the quality of such inspection as it allows the inspectors to conduct frequent inspections and to collect more data.

Thermal imagery, hyperspectral, color, light detection & ranging are the sensors used.

That may seem like a cliché, or hype, or buzz, but it is true.The tech is fundamentally changing the way packages move around the world, from predictive analytics to autonomous vehicles and robotics.

Here are the top five ways in whichArtificial Intelligence is transforming the logistics industry as we know it: Predictive Capabilities Skyrocket When AI in Logistics is ImplementedThe capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning.

Having a tool for accurate demand forecasting and capacity planning allows companies to be more proactive.By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.

The tech is using data to its full potential to better anticipate events, avoid risks, and create solutions.This allows organizations to then modify how resources are used for maximum benefit — and Artificial Intelligence can do these equations much faster and more accurately than ever before.In general, predictive analytics solutions in logistics and supply chains are on the rise.

The most well-known examples are Transmetrics and ClearMetal, which were both mentioned in the latest DHL’s Logistics Trend Radar.AI analysis can also be used to safeguard against risk.

Another good example from DHL is its platform which monitors more than 8 million online and social media posts to identify potential supply chain problems.

Water is the vital natural resource for human survival and development, as well as an important restriction factor of the Eco-environment.

It is not only critical to the ecosystems as a key component of the hydrologic cycle but also touches every aspect of our lives, such as drinking water, agriculture, electricity production, transportation, and industrial purposes.Surface water bodies are dynamic as they shrink, expand, or change their appearance or course of flow with time, owing to different natural and human-induced factors.

Change in surface water volume usually causes serious consequences.

The spatial and temporal change pattern of the surface water has important practical significance and scientific value for water resources management, biodiversity, emergency response, and global climate change.

However, small water bodies such as small ponds and narrow rivers cannot be extracted due to the limited spatial resolution of these remote-sensing images.

Most high-resolution remote-sensing images only have four bands (blue, green, red, and near-infrared), lacking the short-wave infrared (SWIR) data necessary to compute the modified normalized difference water index (MNDWI) and the automated water extraction index (AWEI) indices.A high-resolution spatial multi-spectral image has more detailed spatial features information, which can greatly improve the accuracy of urban water body extraction.

With such an extensive amount of buzz occurring since the most recent couple of days, it is inescapable to dodge, what machines procuring mean, and even what all could MACHINE LEARNING help us with!With Machine Learning or the errand of causing machines to realize what all could be the conditions or the occasions wherein, a machine could work things out, according to the circumstances, and the typical way any human would carry on, as any procedure of Machine Learning instructional exercise, would be working out!Deep learning is a piece of a more extensive group of Machine Learning strategies dependent on counterfeit neural systems.

The procedure can some of the time require area information about a given issue.To all the more likely comprehend include designing, think about the accompanying model.In the land business, the area of a house significantly affects the selling cost.

A deep learning calculation will filter the information to look for highlights that correspond and consolidate them to empower quicker learning without being unequivocally advised to do as such.This capacity implies that information researchers can at times spare a very long time of work.

On different occasions, information naming may require the decisions of deeply gifted industry specialists, and that is the reason, for certain enterprises, getting excellent preparing information can be over the top expensive.To make right, self-sufficient choices, the calculation requires a huge number of well-clarified pictures where diverse physical oddities of the human body are obviously marked.

Given that around 4-5 pictures can be dissected every hour, legitimate naming of all pictures will be costly.With deep learning, the requirement for well-named information is caused outdated as deep learning calculations to exceed expectations at learning without rules.

In the model over, a deep learning calculation would have the option to identify physical irregularities of the human body, even at prior stages than human specialists.Proficient at Delivering High-quality Results People need rest and fuel.

Aventior has signed a long-term partnership with ESRI to become an ESRI marketplace provider for AI and Deep Learning Solutions.Aventior is excited to announce the launch of its Vehicle Detection Deep Learning Algorithm on the ESRI marketplace.

Detecting vehicles and determining their count from satellite imagery is a complex and time-consuming approach.

For handling such intricate tasks, deep learning models offer the superior capability to learn such complex workflow semantics and produce exemplary results.

This Deep Learning based process block is capable of detecting and counting various vehicles such as Cars, Vans, Pickup Trucks and Buses, in satellite images in an automated fashion, thus reducing effort and time for analysis.

Aventior’s Vehicle Detection Deep Learning model delivers exemplary performance on 30 cm resolution satellite  imagery.Aventior’s Vehicle Detection Model results can be used for a variety of purposes, including base map preparation, humanitarian aid, disaster management, and transportation planning.

Applications range from taxation, urban planning, public works, visualization, simulation to a wide range of suitability analysis applications.For more information on our service offerings please contact us at [email protected] or visit our webpage  https://aventior.com/vehicle-detection-esri-release/