A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

Han, Wang; Xuan, Yunqing

doi:10.3390/rs14153823

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Since the high-resolution remote-sensing images contain numerous geographical details of the monitoring objects, which are usually spatiotemporal, nonstationary, largesized, intricate with high dimensions [43], it greatly challenges pattern recognition by using conventional CNNs. Not only the local features of images but also their global interaction in the context is essential for improving the performance of pattern recognition and change detection.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the conclusion is given in Section 5. follow the multi-head attention with a feed forward network (FFN) where the global contextual information can be obtained by the multi-head attention and interact channel-wise. Since the high-resolution remote-sensing images contain numerous geographical details of the monitoring objects, which are usually spatiotemporal, nonstationary, largesized, intricate with high dimensions [43], it greatly challenges pattern recognition by using conventional CNNs. Not only the local features of images but also their global interaction in the context is essential for improving the performance of pattern recognition and change detection.…”

Section: Introductionmentioning

confidence: 99%

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A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images

Yao,

Wang,

et al. 2024

Remote Sensing

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Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth’s surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images

Yao,

Wang,

et al. 2024

Remote Sensing

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“…The rising of Artificial Intelligence (AI) has been instrumental for pattern recognition 1 , reasoning under uncertainty 2 , control methods 3 , analyzing and classifying big data. 4,5 Nevertheless, there is a need for scalable and energy-efficient hardware constructed following the same scheme: further progress of AI algorithms depends on the efficiency of its hardware. 6 In this scenario, Neuromorphic Computing promises higher efficiency since it manipulates information with hardware processes that directly mimic the nature of neurons instead of emulating this via software.…”

Section: Introductionmentioning

confidence: 99%

Lanthanide molecular nanomagnets as probabilistic bits

M.¹,

Giménez‐Santamarina²,

Hu³

et al. 2023

Preprint

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Over the decades, the spin dynamics of a large set of lanthanide complexes have been explored. Lanthanide-based molecular nanomagnets are bistable spin systems, generally conceptualized as classical bits, but many lanthanide complexes have also been presented as candidate quantum bits (qubits). Here we offer a third alternative and model them as probabilistic bits (p-bits), where their stochastic behavior constitutes a computational resource instead of a limitation. We present a modelling tool for molecular spin p-bits, we demonstrate its capability to simulate bulk magnetic relaxation data and ac experiments and to simulate a minimal p-bit network under realistic conditions. Finally, we go back to a recent systematic data gathering and screen the best lanthanide complexes for p-bit behavior, lay out the performance of the different lanthanide ions and chemical families and offer some chemical design considerations.

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Lanthanide molecular nanomagnets as probabilistic bits

Gutiérrez-Finol,

Giménez-Santamarina,

et al. 2023

npj Comput Mater

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Over the decades, the spin dynamics of a large set of lanthanide complexes have been explored. Lanthanide-based molecular nanomagnets are bistable spin systems, generally conceptualised as classical bits, but many lanthanide complexes have also been presented as candidate quantum bits (qubits). Here, we offer a third alternative and model them as probabilistic bits (p-bits), where their stochastic behaviour constitutes a computational resource instead of a limitation. Employing an ad-hoc modelling tool for molecular spin p-bits and molecular nanomagnets, we simulate a minimal p-bit network under realistic conditions. Finally, we go back to a recently published dataset and screen the best lanthanide complexes for p-bit behaviour, lay out the performance of the different lanthanide ions and chemical families and offer some chemical design considerations.

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A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

Cited by 3 publications

References 39 publications

A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images

A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images

Lanthanide molecular nanomagnets as probabilistic bits

Lanthanide molecular nanomagnets as probabilistic bits

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