Incremental Dual-memory LSTM in Land Cover Prediction

Citation data:

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17, Vol: Part F129685, Page: 867-876

Publication Year:
2017
Captures 27
Readers 27
Citations 2
Citation Indexes 2
DOI:
10.1145/3097983.3098112
Author(s):
Jia, Xiaowei; Khandelwal, Ankush; Nayak, Guruprasad; Gerber, James; Carlson, Kimberly; West, Paul; Kumar, Vipin
Publisher(s):
Association for Computing Machinery (ACM)
Tags:
Computer Science
conference paper description
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.