Artificial Intelligence: Transforming the Golden era of Global Trade

Shipping is that the world’s major source of transportation. Nearly 90%[1] of the global trade happens through it. There are several Challenges in Shipping trade that create it the foremost volatile.

In Shipping Industry challenges begin right from the climatic conditions to security of the ships. Most organisations align with shipping industry as its the foremost in transportation of bulk goods.

One such challenge that desires immediate attention is unauthorised loading of goods/Passengers in Ships. All the ships prepared for voyage are tagged. The label consists of ship weight, supplier and voyage details etc.

The answer could be ‘cant say’. Because of the high prices associated in freight charges, companies misuse the ships. They fill it with merchandise and passengers without permissions on the voyage. They don’t even concern about the basic buoyancy levels a ship needs to maintain.

Yes, by checking the materials at each port it stops we can stop this illegal activity. Yet is it viable to see the complete inventory at each port or stoppage of a ship? But again, this adds to the price.

The simple solution for this will be a Computer vision model . Developing models to observe the type of goods a ship has. This involves Supervised machine learning that auto generate algorithm to solve the issue.

Every Company label the ship with the kind of goods it has and its weight . Also has an update in their own database.

Have u ever saw the marked lines on the edges of the ship? Have you ever ever wondered what they are?

They are Plimsoll lines also referred to as international load lines. They tell us the legal limit a ship can carry in a particular type of water, weather and temperatures. This helps in maintaining buoyancy.

Use of Plimsoll lines in understanding the load of the ship

The purpose of Plimsoll lines is to ensure ship is not overloaded. It tells safe draft between the water line and keel in varied operational conditions.

Identify the seaports falling within the route of voyage. Data about sea ports will be available in websites like searates.com. additionally Websites like Marine-Traffic, fleetmon, shipfinder, Vesselfinder, Vesseltracker, vtexplorer etc., provide time to time ship positions.

Tacking the info of what kind of trade is going on through each ports tells us the type of goods kept in the ship. There are certain information suppliers within the market like Zauba Technologies, Infodrive, etc.

The next step is to feed the machine model with information about the depth of ocean near the seaports on the route of Ship. This information is in websites like Geoplanner, maps.ngdc.noaa.gov, tcarta.com, bathymetrics.shop etc.

Gathering information associated with climate helps you to know the Plimsoll line. Websites like passageweather.com, oceanweather.com, buoyweather.com etc. use GFS and Wave watch models to forecast weather, wave and currents information.

Computer vision models use Initial weight of the ship .This helps you to know the changes in a ship weight at various ports. Using that information machine learning algorithms predict the load of the ship at every halt.

For example: If the route of a ship is through Vishakhpatnam port, there are chances of loading steel and coal. As they are the goods mostly traded through this port.

How will OCLAVI help in identification of the unauthorised material on the ship

Using cuboidal tool ship is annotated to know its depth in water. And this data is compared with data related to depth of the ocean and weather. This helps in estimating the weight of the ship. On the basis of this data machine models can identify whats on the ship.

You can train machine models by annotating with rectangle tool the plimsoll lines on the ship at various ports that a ship passes by. Identification of illegal goods becomes easy. Difference of plimsoll line at start, Middle and end of the voyage makes it clear whether additional goods are dumped or not.

References:

http://www.ics-shipping.org/shipping-facts/shipping-and-world-trade