Incremental dual-memory LSTM in land cover prediction

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

doi:10.1145/3097983.3098112

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 proceeding › Conference contribution

53 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 language	English (US)
Title of host publication	KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	867-876
Number of pages	10
ISBN (Electronic)	9781450348874
DOIs	https://doi.org/10.1145/3097983.3098112
State	Published - Aug 13 2017
Event	23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada Duration: Aug 13 2017 → Aug 17 2017

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	Part F129685

Other

Other	23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Country/Territory	Canada
City	Halifax
Period	8/13/17 → 8/17/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

Keywords

LSTM
Land cover
Zero-short learning

Publisher link

10.1145/3097983.3098112

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Jia, X., Khandelwal, A., Nayak, G., Gerber, J. S., Carlson, K. M., West, P., & Kumar, V. (2017). Incremental dual-memory LSTM in land cover prediction. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 867-876). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685). Association for Computing Machinery. https://doi.org/10.1145/3097983.3098112

Incremental dual-memory LSTM in land cover prediction. / Jia, Xiaowei; Khandelwal, Ankush; Nayak, Guruprasad et al.
KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2017. p. 867-876 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jia, X, Khandelwal, A, Nayak, G, Gerber, JS, Carlson, KM, West, P & Kumar, V 2017, Incremental dual-memory LSTM in land cover prediction. in KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685, Association for Computing Machinery, pp. 867-876, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, Halifax, Canada, 8/13/17. https://doi.org/10.1145/3097983.3098112

Jia X, Khandelwal A, Nayak G, Gerber JS, Carlson KM, West P et al. Incremental dual-memory LSTM in land cover prediction. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2017. p. 867-876. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3097983.3098112

Jia, Xiaowei ; Khandelwal, Ankush ; Nayak, Guruprasad et al. / Incremental dual-memory LSTM in land cover prediction. KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2017. pp. 867-876 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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AB - 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.

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Incremental dual-memory LSTM in land cover prediction

Abstract

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