MSF-Net: A multiscale supervised fusion network for building change detection in high-resolution remote sensing images
J Chen, J Fan, M Zhang, Y Zhou, C Shen - IEEE Access, 2022 - ieeexplore.ieee.org
Building change detection is a primary task in the application of remote sensing images,
especially in city land resource management and urbanization process assesment. Due to …
especially in city land resource management and urbanization process assesment. Due to …
Machine learning meets big spatial data
The proliferation in amounts of generated data has propelled the rise of scalable machine
learning solutions to efficiently analyze and extract useful insights from such data …
learning solutions to efficiently analyze and extract useful insights from such data …
Multi-modality cascaded fusion technology for autonomous driving
H Kuang, X Liu, J Zhang, Z Fang - 2020 4th International …, 2020 - ieeexplore.ieee.org
A highly reliable sensor is crucial for autonomous driving, which draws more attention on
multi-modality fusion. This paper proposes a general multi-modality cascaded fusion …
multi-modality fusion. This paper proposes a general multi-modality cascaded fusion …
CAFE: A cross-attention based adaptive weighting fusion network for MODIS and Landsat spatiotemporal fusion
Dense medium-resolution images play an important role in time-series geoscience
applications. However, due to technical limitations, remote sensing imaging systems …
applications. However, due to technical limitations, remote sensing imaging systems …
Empowering federated learning for massive models with nvidia flare
In the ever-evolving landscape of artificial intelligence (AI) and large language models
(LLMs), handling and leveraging data effectively has become a critical challenge. Most state …
(LLMs), handling and leveraging data effectively has become a critical challenge. Most state …
Enhancing Uni-Modal Features Matters: A Multi-Modal Framework for Building Extraction
X Shi, J Gao, Y Yuan - IEEE Transactions on Geoscience and …, 2024 - ieeexplore.ieee.org
In recent years, deep learning and multimodal data have substantially propelled the
development of building extraction models. However, prevailing multimodal methods are …
development of building extraction models. However, prevailing multimodal methods are …
Deep learning-based spatiotemporal data fusion using a patch-to-pixel mapping strategy and model comparisons
Tradeoffs among the spatial, spectral, and temporal resolutions of satellite sensors make it
difficult to acquire remote sensing images at both high spatial and high temporal resolutions …
difficult to acquire remote sensing images at both high spatial and high temporal resolutions …
Nebula-I: A general framework for collaboratively training deep learning models on low-bandwidth cloud clusters
The ever-growing model size and scale of compute have attracted increasing interests in
training deep learning models over multiple nodes. However, when it comes to training on …
training deep learning models over multiple nodes. However, when it comes to training on …
Image fusion meets deep learning: A survey and perspective
Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …
from different source images, aims to generate a single image that is more informative and …
Multi-modal fusion transformer for end-to-end autonomous driving
How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …