{"type":"ScholarlyArticle","title":"Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments","authors":[{"name":"Nemanja Djuric","slug":"nemanja-djuric","given":"Nemanja","family":"Djuric","affiliation":""},{"name":"Lakesh Kansakar","slug":"lakesh-kansakar","given":"Lakesh","family":"Kansakar","affiliation":""},{"name":"Slobodan Vucetic","slug":"slobodan-vucetic","given":"Slobodan","family":"Vucetic","affiliation":""}],"abstract":"Aerosol Optical Depth (AOD), recognized as one of the most important quantities in understanding and predicting the Earth's climate, is estimated daily on a global scale by several Earth-observing satellite instruments. Each instrument has different coverage and sensitivity to atmospheric and surface conditions, and, as a result, the quality of AOD estimated by different instruments varies across the globe. We present a method for learning how to aggregate AOD estimations from multiple satellite instruments into a more accurate estimation. The proposed method is semi-supervised, as it is able to learn from a small number of labeled data, where labels come from a few accurate and expensive ground-based instruments, and a large number of unlabeled data. The method uses a latent variable to partition the data, so that in each partition the expert AOD estimations are aggregated in a different, optimal way. We applied the method to combine AOD estimations from 5 instruments aboard 4 satellites, and the results indicate that it can successfully exploit labeled and unlabeled data to produce accurate aggregated AOD estimations.","doi":null,"url":"https://ijcai.org/proceedings/2013/412","pdf":"https://ijcai.org/proceedings/2013/0412.pdf","markdown":"https://ijcai.org/proceedings/2013/412.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/412","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":412,"pages":{"first":2797,"last":2802},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}