1 code implementation • NeurIPS 2023 • Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle Maddix, Yi Zhu, Mu Li, Yuyang Wang
We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
2 code implementations • 28 Mar 2023 • Vitus Benson, Claire Robin, Christian Requena-Mesa, Lazaro Alonso, Nuno Carvalhais, José Cortés, Zhihan Gao, Nora Linscheid, Mélanie Weynants, Markus Reichstein
Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe.
1 code implementation • 12 Jul 2022 • Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-yan Yeung
With the explosive growth of the spatiotemporal Earth observation data in the past decade, data-driven models that apply Deep Learning (DL) are demonstrating impressive potential for various Earth system forecasting tasks.
Ranked #1 on Earth Surface Forecasting on EarthNet2021 OOD Track
no code implementations • 3 Apr 2019 • Ting Sun, Lei Tai, Zhihan Gao, Ming Liu, Dit-yan Yeung
This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only.
4 code implementations • NeurIPS 2017 • Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
Ranked #1 on Video Prediction on KTH (Cond metric)