@inproceedings{595a53bcbc474cf0b64a90fa424d8f73,
title = "Incremental dual-memory LSTM in land cover prediction",
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.",
keywords = "LSTM, Land cover, Zero-short learning",
author = "Xiaowei Jia and Ankush Khandelwal and Guruprasad Nayak and Gerber, {James S} and Carlson, {Kimberly M} and Paul West and Vipin Kumar",
year = "2017",
month = aug,
day = "13",
doi = "10.1145/3097983.3098112",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "867--876",
booktitle = "KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
note = "23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 ; Conference date: 13-08-2017 Through 17-08-2017",
}