One-day Online Workshop on Recent Algorithms for Remote Sensing Applications in Agriculture (RARSAA-2023) in March 2023
We are glad to inform you that the IEEE GRSS Bangalore Section, NITK IEEE GRSS Student Branch Chapter, and Dept of Electronics and Communications Engineering, NITK Surathkal are jointly organizing a one-day online workshop on “Recent Algorithms for Remote Sensing Applications in Agriculture” (RARSAA-2023) on March 31, 2023.
Important Information:
- Registration Link: https://forms.gle/FD7kta5Ykf3jixoc8
- Last Date for Registration: March 29, 2023
- Registration Fees: NIL
- Flyer (pdf)
- Eligibility Criteria: (a) Students at all levels (Ph.D./M.Tech./M.Sc./B.Tech.- 3rd Year); (b) Faculty members from academia; (c) Engineers and researchers from industry organizations including R&D laboratories.
- Preference will be given to IEEE GRSS and IEEE members.
Objectives of the workshop:
- Exposing participants to the fundamentals of remote sensing image processing and analysis.
- Building in confidence and capability amongst the participants in the application of remote sensing image processing and analysis using machine and deep learning.
- Providing exposure to practical problems and their solutions, through case studies in the remote sensing applications.
- Enhancing the capability of the participants to identify new applications of remote sensing
- Image processing and analysis using machine and deep learning.
Overview of the workshop:
Remote sensing can be defined as the collection of data about an object from a distance. Humans and many other types of animals accomplish this task with aid of eyes or by the sense of smell or hearing. Geographers use the technique of remote sensing to monitor or measure phenomena found in the Earth’s lithosphere, biosphere, hydrosphere, and atmosphere. Remote sensing of the environment by geographers is usually done with the help of mechanical devices known as remote sensors. These gadgets have a greatly improved ability to receive and record information about 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 an object’s transmission of electromagnetic energy from 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 Earth’s surface.
Machine and deep learning offer the many potential applications in the field of remotes sensing data processing and analysis. One such potential for effective and efficient classification of remotely sensed imagery. The strengths of machine and deep learning 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 are not straightforward, and the literature provides conflicting advice regarding many key issues.
The proposed summer school presents machine and deep learning for remote sensing applications. It also highlights many societal applications of remote sensing and contains sessions for the participants who may not have a strong background in the field. The purpose of the 5 days’ 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 various practical problems in remote sensing domain.
Schedule:
| # | Date | Time | Lecture |
| 01 | March 31, 2023 | 09:00 – 10:30 | Deep Learning Algorithms for Agriculture Remote Sensing Applications – by Dr. Arun P. V., IIIT Sri City Chittoor |
| 02 | March 31, 2023 | 10:45 – 12:15 | Remote Sensing Applications in Agriculture – by Mr. Mohammed Ahamed J, NRSC (ISRO) Bangalore |
| 03 | March 31, 2023 | 14:00 – 15:30 | Applications of Remote Sensing in Agricultural and Water Resource Management – by Dr. Shwetha H. R., NITK Surathkal |
Organization:
| Workshop Coordinators | Dr. Shyam Lal, Vice-chair, IEEE GRSS Bangalore Chapter and Founding Faculty Advisor of NITK IEEE GRSS Student Branch Chapter, Assistant Professor, Department of Electronics and Communication Engineering. National Institute of Technology Karnataka, Surathkal, Mangaluru-575025 (Karnataka), India Tel: +91-824-2473522; Mob.: +91-9741072082 (WhatsApp) Email(s): shyamfec@nitk.edu.in (For any inquiry: contact workshop coordinator) Dr. Shwetha H. R. IEEE GRSS Member Assistant Professor, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025 (Karnataka), India Email: hrshwetha@nitk.edu.in |
| Student Volunteers | Mr. Basavaraju K.S., Chair, NITK IEEE GRSS SBC Ms. Vibha K, Member of NITK IEEE GRSS SBC |