Beyond a shadow of a doubt: land surveying in the real world

Scientific Background
In our increasingly digitized world, computer vision technology plays a crucial role in a multitude of sectors, from agriculture to coastal monitoring. However, these systems often struggle in environments with variable lighting, particularly when dealing with shadows. This presents a significant challenge in real-world applications, leading to inaccuracies in environment classification and other essential tasks. Our project, in collaboration with the Centre for Environment, Fisheries and Aquaculture Science (CEFAS), seeks to tackle this problem head-on, aiming to develop advanced shadow-invariant processing algorithms that will greatly enhance computer vision capabilities. This timely research aims to bridge the gap between human visual competences and computer vision systems, a pioneering step towards increased accuracy and efficiency in sectors reliant on land and coastal surveying.

Research Methodology
The project will follow a comprehensive, hands-on research approach. The student involved will undergo training in measurement and calibration techniques, which will be applied to enhance vision systems used for remotely piloted aircraft. They will work closely with CEFAS to produce an annotated image set, identifying the same material both in and out of shadow regions. The primary focus will be to extend and adapt shadow invariant measurement techniques for the unique challenges posed by farm surveying and coastal monitoring. By incorporating Near Infrared (NIR) technology, the project will explore innovative ways to improve visual perception in shadowy environments. The research will take place both at the lab and in the field, offering a diverse and enriching experience.

Training
In addition to gaining experience in cutting-edge computer vision research, the student will develop a robust set of skills in several key areas. They will receive training in the fundamentals of measurement and sensor calibration and get hands-on experience with field deployment of RPA (remotely piloted aircraft) systems. Through the development and implementation of algorithms, they will hone their programming and data analysis skills. They will also be exposed to interdisciplinary collaboration, working closely with professionals from agritech, geophysics, ecology, and computational science sectors. Their scientific research skills will be enhanced through the production of academic papers and contribution to public domain source code. This project offers a unique springboard for a future inter-disciplinary career in the AgriFoRwArdS area, providing a rich blend of theoretical knowledge and practical, industry-relevant experience.

Join us as we redefine the frontiers of computer vision technology, paving the way for more accurate, efficient, and reliable environmental surveying solutions. Your participation could help shape the future of agricultural and coastal monitoring systems, making a lasting impact on these crucial sectors.