{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:53:53Z","timestamp":1771037633298,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/703","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"5066-5072","source":"Crossref","is-referenced-by-count":2,"title":["Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net"],"prefix":"10.24963","author":[{"given":"Joshua","family":"Fan","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Chen","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaming","family":"Wen","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Sun","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Gomes","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:11:10Z","timestamp":1658128270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/703"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/703","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}