Early Warning System for Forest Fire Detection

Early Warning System for Forest Fire Detection

The overall goal of this project is to develop Forest Fire Monitoring System which is cost-effective, locally indigenized, scalable and feasible for application in Pakistani context. Based on this premise, instead of the high-end and costly thermal camera, NCRA-Agricultural Robotics Lab (NARL) at LUMS is proposing to use standard RGB cameras with night vision capabilities….


AAAI2023 – Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection

Usman Nazir, Wadood Islam, Sara Khalid, Murtaza Taj Abstract: Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean…


ICONIP2023 – Stereoential Net: Deep Network for Learning Building Height Using Stereo Imagery

Sana Jabbar, Murtaza Taj Abstract: Height estimation plays a crucial role in the planning and assessment of urban development, enabling effective decision-making and evaluation of urban built areas. Accurate estimation of building heights from remote sensing optical imagery poses significant challenges in preserving both the overall structure of complex scenes and the elevation details of…

3xPapers accepted at ICONIP 2019

3xPapers accepted at ICONIP 2019

Three papers have been accepted at “International Conference on Neural Information Processing – 19 (ICONIP-19)”. This conference is ranked A by the CORE rating measure and is going to be held from 12th to 15th December 2019 in Sydney, Australia. “Cross-view Image Retrieval – Ground to Aerial Image Retrieval through Deep Learning” “Patch-based Generative Adversarial Network…


ICIAP 2019 – Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval

Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019 Abstract Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory…

NCA2021 – Statistically Correlated Multi-task Learning for  Autonomous Driving

NCA2021 – Statistically Correlated Multi-task Learning for Autonomous Driving

Waseem Abbas, M. Fakhir Khan, Murtaza Taj, and Arif Mahmood Abstract Autonomous driving research is an emerging domain in computer vision and machine learning areas. Most existing methods perform Single Task Learning (STL) from one or more images while Multi-Task Learning (MTL) is more efficient due to the leverage of shared information between different tasks….

Digital Image Processing

This is a graduate-level introductory course on the fundamentals of digital image processing. The course will emphasize the general principles of image processing. It will extend the signals and systems knowledge of the students to two-dimensional signals. This is a very important course for any student who wants to do a senior project related to…

Computer Graphics

Computer Graphics is one of the most exciting ‘application’ fields of Computer Science. This course is intended to introduce the basics of Computer Graphics, laying the foundation for more advanced graduate classes or industry work. The basic graphics pipeline is covered in this course, along with an introduction to OpenGL. This course will be conducted…

Computer Vision

This course gives a broad overview of the field of computer vision, laying the foundations for advanced graduate level classes and research work in vision. This course will be conducted with an application perspective. Therefore students will be expected to implement several techniques learnt in the lectures. A good calculus, linear algebra and programming background…

Deep Learning

Deep Learning is a hierarchical learning methodology based on artificial neural networks which are algorithms inspired by the structure and function of the brain. It has applications in wide-range of industries these days such as face-recognisers working at massive scales, robotics, speech translation, text analysis, improving customer experience, autonomous vehicles etc. In this course we…