---
title: "Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments"
authors:
  - "Nemanja Djuric"
  - "Lakesh Kansakar"
  - "Slobodan Vucetic"
doi: ""
url: "https://ijcai.org/proceedings/2013/412"
pdf: "https://ijcai.org/proceedings/2013/0412.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2797-2802"
track: ""
keywords:
---

# Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments

Nemanja Djuric, Lakesh Kansakar, Slobodan Vucetic

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2797-2802.

## 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.