Publications

M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
M4Fog dataset Overall We have collected nearly a decade’s worth of multi-modal data related to continuous marine fog stages from four series of geostationary meteorological satellites, along with meteorological observations and numerical analysis, covering 15 marine regions globally where maritime fog frequently occurs.
PDF Cite Code Track-A H8/9 Track-A FY4A Track-A GOES Track-B H8/9 Track-C H8/9 Area1 Track-C H8/9 Multi-areas Track-C FY4A Track-C Meteo-multiareas Track-C Meteo-single-area ICOADS
M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
MoANet: A Motion Attention Network for Sea Fog Detection in Time Series Meteorological Satellite Imagery
Sea fog detection is a significant and challenging issue in meteorological satellite imagery. Distinguishing between sea fog and low …
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MoANet: A Motion Attention Network for Sea Fog Detection in Time Series Meteorological Satellite Imagery
Weakly Supervised Sea Fog Detection in Remote Sensing Images via Prototype Learning
Sea fog detection is a challenging and significant task in the field of remote sensing. Deep learning-based methods have shown …
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Weakly Supervised Sea Fog Detection in Remote Sensing Images via Prototype Learning
SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection
Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing …
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SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Weather forecasting is one of the cornerstones of meteorological work. In this paper, we present a new benchmark dataset named …
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Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Attention based Long Short-Term Memory Network for Coastal Visibility Forecast
Visibility prediction in coastal areas has always been an important issue affecting the safety of residents and the efficiency of urban …
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Identify, Guess and Reconstruct- Three Principles for Cloud Removal Task
Remote sensing images serve a significant role in earth observation to tackle climate change and post-disaster reconstruction concerns. …
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Identify, Guess and Reconstruct- Three Principles for Cloud Removal Task
Annotating Only at Definite Pixels- A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition
Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning …
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Annotating Only at Definite Pixels- A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data
With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great …
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Domain Adaptation on Multiple Cloud Recognition From Different Types of Meteorological Satellite
Meteorological satellites have become an indispensable meteorological tool for earth observation, as aiding in areas such as cloud …
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Domain Adaptation on Multiple Cloud Recognition From Different Types of Meteorological Satellite
Sea fog detection based on unsupervised domain adaptation
Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other …
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Sea fog detection based on unsupervised domain adaptation
Sea Fog Monitoring Method Based on Deep Learning Satellite Multi-channel Image Fusion
Sea fog, whether on the sea or the coast, has adverse effects on transportation, marine fishing, marine development projects, and …
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Sea Fog Monitoring Method Based on Deep Learning Satellite Multi-channel Image Fusion
A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery
Sea fog detection is a challenging and essential issue in satellite remote sensing. Although conventional threshold methods and deep …
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A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery