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| Content Provider | ACM Digital Library |
|---|---|
| Author | West, Paul Kumar, Vipin Carlson, Kimberly Nayak, Guruprasad Khandelwal, Ankush Gerber, James Jia, Xiaowei |
| 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 spatio-temporal 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. |
| Starting Page | 867 |
| Ending Page | 876 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450348874 |
| DOI | 10.1145/3097983.3098112 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-08-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Lstm Zero-short learning Land cover |
| Content Type | Text |
| Resource Type | Article |
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