Incremental dual-memory LSTM in land cover prediction

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

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

27 Scopus citations

Abstract

Land cover prediction is essential for monitoring global environmental change. Unfortunately, traditional classification models are plagued by temporal variation and emergence of novel/unseen land cover classes in the prediction process. In this paper, we propose an LSTM-based spatiotemporal learning framework with a dual-memory structure. The dual-memory structure captures both long-term and short-term temporal variation patterns, and is updated incrementally to adapt the model to the ever-changing environment. Moreover, we integrate zero-shot learning to identify unseen classes even without labelled samples. Experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework over multiple baselines in land cover prediction.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages867-876
Number of pages10
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Other

Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Country/TerritoryCanada
CityHalifax
Period8/13/178/17/17

Bibliographical note

Funding Information:
This work was funded by the NSF Awards 1029711, and Gordon and Betty Moore Foundation and the Belmont Forum/FACCE-JPI funded DEVIL project (NE/M021327/1). Access to [13] computing facilities was provided by NASA Earth Exchange and Minnesota Supercomputing Institute.

Keywords

  • LSTM
  • Land cover
  • Zero-short learning

Fingerprint

Dive into the research topics of 'Incremental dual-memory LSTM in land cover prediction'. Together they form a unique fingerprint.

Cite this