AI and IoT – Facilitating better decisions for more productive outcomes Posted on : Jun 21 - 2018

Artificial intelligence plays a crucial role to enable the ability of machines to behaves like humans. The main advantages of using AI are learning (supervised or unsupervised), taking self-decisions based on the processing of complex data (organised or unorganised).

Some domains where applications of IoT and AI are very essential to improve the decision making are as follows:

• Environmental Sensing
• Manufacturing automation
• Security, facial recognition
• Agriculture
• Natural resources monitoring such as water resources, forest, cryospheric research and many more
• Education
• Healthcare
• Climate Change

Driverless cars, automated fuelling station

The Fourth industrial revolution (4IR) has brought in an interesting confluence of the artificial intelligence and the Internet of Things (IoT).  The science of where i.e. location also has a significant role in these revolutionary technologies. All devices and sensors must be having some location (static and or dynamic) and this ‘where’ part is highly important to understand, analyse and getting maximum information from the huge data generated by sensors.

The geospatial technologies are scanning the earth, (other planets also like mars, Jupiter and many more) with connected satellites through communication, sharing the abundance of data. This data can be accessed and analysed anywhere with the power of the cloud.

The autonomous vehicles equipped with different sensors fully utilizes the geospatial information to know about transport and facilities. Fuelling the gas through robots having IoT devices in the driverless cars can be seen everywhere very soon. IoT, Artificial Intelligence and Cloud technologies drive the geospatial industry, they also create a lot of opportunities bringing about a multitude of disruption in the processes as well.

What AI can do for us

The benefits of using AI in applications are many. Let us understand how AI helps by way of the following examples:

AI Modelling of Snow Wetness and Density using Hyperspectral Dataset

Snow properties such as snow wetness and density are highly important for avalanche and hydrological applications. These properties are mainly investigated in field-based surveys. The issues in a field-based survey are rugged terrain, difficult weather conditions and logistics issues. This problem can be solved using AI based on the airborne hyperspectral dataset. View More