Paper Info
Title
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net.
Abstract
Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods.
Year
DOI
Venue
2022
10.24963/ijcai.2022/703
International Joint Conference on Artificial Intelligence
Keywords
DocType
Citations 
Multidisciplinary Topics and Applications: Computational Sustainability,Machine Learning: Weakly Supervised Learning,Computer Vision: Transfer, low-shot, semi- and un- supervised learning,Machine Learning: Multi-instance,Computer Vision: Applications,Multidisciplinary Topics and Applications: Sustainable Development Goals
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Joshua Fan151.50
Di Chen200.34
Jiaming Wen300.68
Ying Sun400.34
Carla P. Gomes52344179.21