Learning large-scale plantation mapping from imperfect annotators

Xiaowei Jia, Ankush Khandelwal, James S Gerber, Kimberly Carlson, Paul West, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Plantation mapping is important for understanding deforestation and climate change. Most existing plantation products rely heavily on visual interpretation of satellite imagery, which results in both false positives and false negatives. In this paper we aim to design an automatic framework that map plantations in large regions. Conventional classification methods cannot be directly applied due to the lack of ground-truth data. To this end, we propose a novel method that learns from multiple imperfect annotators. Since each annotator's labeling accuracy varies across different land covers due to his expertise and reference imagery, we model the annotator's reliability level to be associated with different types of locations. On the other hand, the temporal variation of land covers also greatly impacts the performance of conventional learning model. Therefore we utilize the remote sensing data which are available at multiple periods of a year and extend our proposed method by incorporating multi-instance learning. Finally, we show the superiority of the proposed method over multiple baselines in both synthetic dataset and real-world dataset. In addition, through several case studies we demonstrate that our method can achieve a better balance of precision and recall than the existing plantation products.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1192-1201
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • Multi-instance Learning
  • Multiple Annotators
  • Remote Sensing

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