Automated UAV and satellite image analysis for wildlife monitoring

Project Rationale
There is an increasing interest in application of UAVS (Unmanned Aerial Vehicles) and satellite acquired imagery for monitoring wildlife for ecology/conservation purposes including in particular inaccessible areas of the globe such as Antarctic.
With regard to the last location, image data are regularly collected by the British
Antarctic Survey (BAS). The manual analysis of this imagery by humans is a tedious and expensive task which strongly motivates the development of an automated image processing solutions. This said, to our knowledge the existing algorithms do not provide the required performance/robustness. This project will aim to develop automated computer vision algorithms for detection and counting of wildlife. Initially, we will focus on the seal and penguin imagery, but the aim is to develop methods generic enough that could suit monitoring other wildlife with a possibility of using this technology for other applications beyond ecology/conservation.
Methodology
Recently, a family of computer vision algorithms known as 'Deep Learning' has been reported to provide a step-change in performance in many image processing/computer vision tasks. In computer vision Deep Learning usually utilizes a deep convolutional neural network (CNN). The key feature of DL and CNN based algorithms is that they replace the step of designing handcrafted features in the prior art algorithms with the automated hierarchical feature learning. As part of their PhD, a successful candidate will investigate development and application of Deep Learning algorithms for the relevant field i.e. counting wildlife in images. The student will make use of data captured using imagery collected from satellites, manned aircraft and UAVs. A key aspect of the project will be to provide the recommendations on the requirements of the imagery allowing for ensuring the required level of algorithm robustness. The new developed algorithms will be compared to the prior-art. The envisaged system will require a large dataset of annotated imagery for training and this will require some expert knowledge on the image appearance of the relevant objects. The student will use the existing databases when available, but will also need to closely liaise with the relevant experts in the BAS for extending those datasets if necessary.
Training
The NEXUSS CDT provides state-of-the-art, highly experiential training in the
application and development of cutting-edge Smart and Autonomous Observing
Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of East Anglia, hosted at School of Computing Sciences in the Graphics, Vision and Speech laboratory. The student will receive training in all areas relevant to the project including computer vision, machine learning as well as Matlab and Python programming. The student will spend periods of time at British Antarctic Survey in order to familiarize with the images and the ecological aspects of the project.

References
L. F. Gonzalez, G. A. Montes, E. Puig, S. Johnson, K. Mengersen and K. J.
Gaston, Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence
Revolutionizing Wildlife Monitoring and Conservation, Sensors 2016, 16, 97;
doi:10.3390/s16010097

V. Lempitsky and A. Zisserman. "Learning to count objects in images." Advances
in Neural Information Processing Systems. 2010.

G. French, M. H. Fisher, M. Mackiewicz and C.L. Needle, Convolutional Neural
Networks for Counting Fish in Fisheries Surveillance Video, 2015, Machine Vision
of Animals and their Behaviour Workshop at the 26th British Machine Vision
Conference