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Improving field boundary delineation in ResUNets via adversarial deep learning.

, , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2022)

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The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA., , , , , , and . Remote. Sens., 12 (7): 1111 (2020)Evaluation of Four New Land Surface Temperature (LST) Products in the U.S. Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3., , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2021)Detecting In-Season Crop Nitrogen Stress of Corn for Field Trials Using UAV- and CubeSat-Based Multispectral Sensing., , , , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 12 (12): 5153-5166 (2019)Retrieval Based Time Series Forecasting., , , , , , and . CoRR, (2022)Deriving Vegetation Phenological Time and Trajectory Information Over Africa Using SEVIRI Daily LAI., , , , , and . IEEE Trans. Geosci. Remote. Sens., 52 (2): 1113-1130 (2014)Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments?, , , , , , , , , and 5 other author(s). IGARSS, page 3931-3934. IEEE, (2018)Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco., , , , , , and . Remote. Sens., 13 (9): 1740 (2021)Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction., , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2020)Improving field boundary delineation in ResUNets via adversarial deep learning., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2022)Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders., , , , , , and . KDD, page 2256-2268. ACM, (2023)