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

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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…

3xPapers accepted at ICONIP 2019

3xPapers accepted at ICONIP 2019

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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…

PhD Proposal Defense: Usman Nazir

PhD Proposal Defense: Usman Nazir

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Learning Socio-economic Indicators from Remote Sensing Data Thursday 12 Sep, 2019 at 03:30 am in CS Smart Room 9-105 SBASSE. Abstract Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty and slavery,…

CVPRW2019 – Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
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CVPRW2019 – Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia

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Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, Murtaza Taj International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16-21, 2019 Abstract This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within “Brick-Kiln-Belt” of South Asia. The framework is developed…