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Found 13 papers, 6 papers with code
Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community
Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems.
MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations
This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
AI-GOMS: Large AI-Driven Global Ocean Modeling System
Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction.
Intelligent model for offshore China sea fog forecasting
Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities.
Koopman neural operator as a mesh-free solver of non-linear partial differential equations
By approximating the Koopman operator, an infinite-dimensional operator governing all possible observations of the dynamic system, to act on the flow mapping of the dynamic system, we can equivalently learn the solution of a non-linear PDE family by solving simple linear prediction problems.
KoopmanLab: machine learning for solving complex physics equations
To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator family, for learning PDEs without analytic solutions or closed forms.
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs.
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources
Accordingly, a full-coverage high-resolution air pollutant dataset with timely updates and historical long-term records is essential to support both research and environmental management.
Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches
We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources
Conditional probability distributions were computed based on data quality and reliability by using information selectively.
