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PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images

Autor: Xu, Liying, Li, Huifang, Shen, Huanfeng, Lei, Mingyang, Jiang, Tao

Data quantity and quality are both critical for information extraction and analyzation in remote sensing. However, the current remote sensing datasets often fail to meet these two requirements, for which cloud is a primary factor degrading the data q

Externí odkaz: http://arxiv.org/abs/2410.16955

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A physics-constrained machine learning method for mapping gapless land surface temperature

Autor: Ma, Jun, Shen, Huanfeng, Jiang, Menghui, Lin, Liupeng, Meng, Chunlei, Zeng, Chao, Li, Huifang, Wu, Penghai

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless

Externí odkaz: http://arxiv.org/abs/2307.04817

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Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Autor: Han, Yushan, Zhang, Hui, Li, Huifang, Jin, Yi, Lang, Congyan, Li, Yidong

Publikováno v: IEEE Intelligent Transportation Systems Magazine

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far,

Externí odkaz: http://arxiv.org/abs/2301.06262

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