MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer

1 code implementation • ICCV 2023 • Fudong Lin, Summer Crawford, Kaleb Guillot, Yihe Zhang, Yan Chen, Xu Yuan, Li Chen, Shelby Williams, Robert Minvielle, Xiangming Xiao, Drew Gholson, Nicolas Ashwell, Tri Setiyono, Brenda Tubana, Lu Peng, Magdy Bayoumi, Nian-Feng Tzeng

In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.

Contrastive Learning Crop Yield Prediction +1

Mega-cities dominate China's urban greening

no code implementations • 3 Jul 2023 • Xiaoxin Zhang, Martin Brandt, Xiaoye Tong, Xiaowei Tong, Wenmin Zhang, Florian Reiner, Sizhuo Li, Feng Tian, Yuemin Yue, Weiqi Zhou, Bin Chen, Xiangming Xiao, Rasmus Fensholt

Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being.