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NIT Surathkal : Online Summer School on Machine & Deep Learning for Remote Sensing Applications

NIT Surathkal : Online Summer School on Machine & Deep Learning for Remote Sensing Applications

About NIT Surathkal : Online Summer School on Machine & Deep Learning for Remote Sensing Applications

 

Remote sensing is the collection of data about an object from a distance. Unlike direct sensing which is
humanly possible, the geographers use the technique of remote sensing to monitor or measure phenomena
found in the lithosphere, biosphere, hydrosphere, and atmosphere of the planet Earth. Remote sensing of the
environment by geographers is usually done with the help of mechanical devices known as remote sensors.
These gadgets use advanced technology to receive and record information, accurately and efficiently, of an
object without any physical contact. Often, these sensors are positioned away from the object of interest by
using helicopters, planes, and satellites.

 

Most sensing devices record information about an object by measuring its ability to reflect and transmit electromagnetic energy from its reflecting and radiating surfaces. Remote sensing imagery has many applications in mapping land-use and cover, agriculture, soils mapping,forestry, city planning, archaeological investigations, military observation, and geomorphological surveying,among other uses. For example, foresters use aerial photographs for preparing forest cover maps, locating possible access roads, and measuring quantities of trees harvested. Specialized photography using color infrared film has also been used to detect disease and insect damage in forest trees. The simplest form of remote sensing uses photographic cameras to record information from visible or near infrared wavelengths.

 

In the late 1800s, cameras were positioned above the Earth’s surface in balloons or kites to take oblique aerial photographs of the landscape. During World War I, aerial photography played an important role in gathering information about the position and movements of enemy troops. These photographs were often taken from airplanes. After the war, civilian use of aerial photography from airplanes began with the systematic vertical¬†imaging of large areas of Canada, the United States, and Europe. Many of these images were used to
construct topographic and other types of reference maps of the natural and human-made features found on the surface of the Earth.

Over time, the information gathered by these sensors has become big data.

Machine and deep learning tackle the challenges in handling big data. These tools offer many potential applications in the field of remote sensing data processing and analysis. One such potential task is in effective and efficient classification of remotely sensed imagery. The strengths of machine and deep learning methods include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine and deep learning classification method is not straightforward, and the literature provides conflicting advice regarding many key issues. The proposed summer school presents tutorials for application of machine and deep learning for remote sensing data. It also highlights many real applications of remote sensing and contains sessions for the participants who may not have a strong background in the field. The purpose of the two weeks online summer school is to provide an intensive understanding of how to use the machine and deep learning algorithms and to equip the participants with software tools for solving the practical problems in the remote sensing domain.

 

 

REGISTRATION LINK : https://forms.gle/ttNTqpgZUUEYCSNa6

 

Duration: July 05-16, 2021 (10 days/ 2 weeks)

 

Last date for registration: June 28, 2021

 

For more details refer the Official Website

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