IEEETGRS2024 – Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation
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IEEETGRS2024 – Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation

Sana Jabbar and Murtaza Taj Abstract: Accurate estimation of building heights is crucial for effective urban planning and resource management as it provides essential geometric information about the urban landscape. Many end-to-end deep learning-based networks have been proposed for image-to-height mapping using high-resolution nonoptical and optical remote sensing imagery. In this study, we develop a…

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M. Wadood Islam

M. Wadood Islam was an MS-CS student at LUMS and a Graduate Research Assistant at Computer Vision and Graphics Lab (CVGL), LUMS Syed Babar Ali School of Science and Engineering. He earned his undergraduate degree from FAST-NUCES in Computer Science. His research interests include machine learning, deep learning, computer vision, and bioinformatics. He worked on classifying…

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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…

5xMS Thesis Proposal Defence

Tuberculosis Diagnosis in Medical Imagery using Deep Learning by Inam Ullah Taj Estimating labor population in Brick Kilns using Satellite Imagery and Remote Sensing by Muhammad Awais Ather Compressing neural networks via teacher assisted learning by Umair Haider Online structured filter pruning for neural networks by Shaiq Munir Malik Semantic Segmentation of water channel to…

IEEE TGRS – A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching
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IEEE TGRS – A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching

Numan Khurshid, Mohbat Tharani, Murtaza Taj, and Faisal Qureshi Abstract: We propose a new method for remote sensing image matching. The proposed method uses encoder subnetwork of an autoencoder pre-trained on GTCrossView data to construct image features. A discriminator network trained on University of California Merced Land Use/Land Cover dataset (LandUse) and High-resolution Satellite Scene…

MS Thesis Defense: Muhammad Ahmed Bhimra

Spatio-Temporal Analysis of Landuse-Landcover Change Using Satellite Imagery Thursday 28 Feb, 2019 at 10:00 am in Smart Room 9-105 SBASSE. Abstract We propose an approach to recognize large scale, rapid spatio-temporal analysis of satellite remote sensing data. This technique can be used to measure longitudinal changes and yearly changes. Most of the existing methods either…

ICASSP 2019 – Using 3D Residual Network For Spatio-Temporal Analysis Of Remote Sensing Data
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ICASSP 2019 – Using 3D Residual Network For Spatio-Temporal Analysis Of Remote Sensing Data

Muhammad Ahmed Bhimra, Usman Nazir, Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network…