Predict land covers with transition modeling and incremental learning

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

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

17 Scopus citations

Abstract

Successful land cover prediction can provide promising insights in the applications where manual labeling is extremely difficult. However, traditional machine learning models are plagued by temporal variation and noisy features when directly applied to land cover prediction. Moreover, these models cannot take fully advantage of the spatio-temporal relationship involved in land cover transitions. In this paper, we propose a novel spatio-temporal framework to discover the transitions among land covers and at the same time conduct classification at each time step. Based on the proposed model, we incrementally update the model parameters in the prediction process, thus to mitigate the impact of the temporal variation. Our experiments in two challenging land cover applications demonstrate the superiority of the proposed method over multiple baselines. In addition, we show the efficacy of spatio-temporal transition modeling and incremental learning through extensive analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Pages171-179
Number of pages9
ISBN (Electronic)9781611974874
DOIs
StatePublished - Jun 9 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Bibliographical note

Funding Information:
This work was funded by the NSF Awards 1029711 and 0905581, and the NASA Award NNX12AP37G. Access to computing facilities was provided by NASA Earth Exchange and Minnesota Supercomputing Institute.

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