How Developers are using Machine learning in mapping schools
In 2019 ITU and UNICEF introduced an initiative in the name of Project Connect to link every school infrastructure to the internet till the year 2030. Basically, this project aims to provide students with boundless information and opportunities. Here, machine learning technology is playing a huge role in making this project possible. In fact, as per the machine learning expert, AI and ML are the critical drivers of live school mapping projects.
So, let us learn more on the matter:
How is machine learning helping in live school mapping?
The basic concept for this initiative is to make use of applicable ML algorithms and apply satellite imagery tools to find out potential school infrastructures on the basis of some critical arrangements. For example, building structures, playgrounds and rooftops.
However, one of the crucial and initial steps to make it happen is extensively training the ML algorithm. In fact, algorithms are the benchmark that brings successful results.
For training ML algorithms, developers take the help of crowdsourcing games that are available for anyone to play using the internet. Further in this game, the instructions teach users to find and identify a school over satellite imagery. After this, it asks players to use their reasoning and judgment to state if a particular location is a school or not.
Due to this practice, a number of players assist in teaching the ML algorithm what a probable school looks like, which will help it to improve its accuracy with time.
However, we should note that not every school location is easy to recognize using satellite imagery. Also, most school education does not look precisely the same and different countries and regions.
Thus, developers need a wide range of machine learning algorithms that can represent a variety of landscapes within various regions and countries, including rural, urban, and Jungle settings.
Furthermore, project developers aim to continue building applications and tools for government initiatives regarding live school mappings. Further, it will allow headmasters principles and students to map school locations by themselves which will improve their data and information systems.
The project aims to use the strategy where a broad range of approaches by different individuals helps in mapping every school across the world, and no location remains behind. However, individuals who want to contribute to the project in some way or the other should apply for machine learning certification through a reputed course provider.
Finding out coverage in live school mapping
Once the project maps a school location, it validates a crucial follow-on action, where it provides proactive support to obtain and examine data to evaluate internet coverage at all the school locations.
By analyzing data on internet coverage initiatives can detect critical challenges on the success of project connect. And one of the main challenges that it faces is whether a school location has internet coverage.
Further, keep in mind that coverage is not similar to connectivity. For instance, a school location might have 4G internet coverage but have no access to an active mobile broadband connection. Hence, even after a school has internet coverage, it does not connect to the internet.
If we consider this context, coverage represents accessibility to broadband services at the location on the basis of involvement of excess technology. For example, mobile access technology like for internet connection for fixed access technology like fiber. Further, the project can use this data to estimate if coverage exists at a school location or not. Additionally, projects can also analyze the quality of service using the same data.
Finding out connectivity in live school mapping
On the other hand, if you look into connectivity which basically represents an active subscription by a school location regardless of which technology it is using.
However, to prove that a school location has the connectivity, it must show the evidence for its active subscription.
By analyzing connectivity data, developers can solve another challenge that the project connect faces: capacity and quality of broadband service that school locations receive on a regular basis.
By performing live monitoring, developers can generate vast amounts of information. However, to thoroughly understand and utilize data to its maximum potential, support from different experts, including AI and ML teams, is a necessity. The test is beneficial for accountability, and so you are sure that connections are getting proper maintenance. Further, it will ensure that governments and school locations are receiving standard levels of services matching their investments. All over, it will help in unlocking the potential for in-depth pattern identification and analysis through machine learning algorithms.
Conclusion
Up till now, we have seen excellent progression in live school mapping processes across numerous countries. Also, due to such exciting and valuable projects, we see a growth in the introduction of the machine learning course and machine learning training programs on a global spectrum. Check out the GLOBAL TECH COUNCIL to gain insights into tech-related news.
