[HTML][HTML] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Generative adversarial networks: a survey on applications and challenges
MR Pavan Kumar, P Jayagopal - International Journal of Multimedia …, 2021 - Springer
Deep neural networks have attained great success in handling high dimensional data,
especially images. However, generating naturalistic images containing ginormous subjects …
especially images. However, generating naturalistic images containing ginormous subjects …
[HTML][HTML] Building segmentation through a gated graph convolutional neural network with deep structured feature embedding
Automatic building extraction from optical imagery remains a challenge due to, for example,
the complexity of building shapes. Semantic segmentation is an efficient approach for this …
the complexity of building shapes. Semantic segmentation is an efficient approach for this …
Hybrid quantum-classical convolutional neural network model for image classification
Image classification plays an important role in remote sensing. Earth observation (EO) has
inevitably arrived in the big data era, but the high requirement on computation power has …
inevitably arrived in the big data era, but the high requirement on computation power has …
Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF)
Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D
building modeling, urban planning, and disaster management. Due to the complexity of …
building modeling, urban planning, and disaster management. Due to the complexity of …
Dilated-ResUnet: A novel deep learning architecture for building extraction from medium resolution multi-spectral satellite imagery
In today's world, satellite images are being utilized for the identification of built-up area,
urban planning, disaster management, insurance & tax assessment in an area, and many …
urban planning, disaster management, insurance & tax assessment in an area, and many …
Techniques and tools for integrating building material stock analysis and life cycle assessment at the urban scale: A systematic literature review
The urban building stock has a high demand for materials and energy, exerting tremendous
pressure on natural resources. A current research trend is to integrate Building Material …
pressure on natural resources. A current research trend is to integrate Building Material …
Adversarial shape learning for building extraction in VHR remote sensing images
Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary
ambiguity problems. Although conventional convolutional neural networks (CNNs) based …
ambiguity problems. Although conventional convolutional neural networks (CNNs) based …
A deep learning-based framework for automated extraction of building footprint polygons from very high-resolution aerial imagery
Accurate building footprint polygons provide essential data for a wide range of urban
applications. While deep learning models have been proposed to extract pixel-based …
applications. While deep learning models have been proposed to extract pixel-based …
MHA-Net: Multipath Hybrid Attention Network for building footprint extraction from high-resolution remote sensing imagery
J Cai, Y Chen - IEEE Journal of Selected Topics in Applied …, 2021 - ieeexplore.ieee.org
Deep learning approaches have been widely applied to building footprint extraction using
high-resolution imagery. However, the traditional fully convolution network still has problems …
high-resolution imagery. However, the traditional fully convolution network still has problems …