Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Multiple Time Series for Anomaly Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Active learning with irrelevant examples
ECML'06 Proceedings of the 17th European conference on Machine Learning
Tree detection from aerial imagery
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Monitoring global forest cover using data mining
ACM Transactions on Intelligent Systems and Technology (TIST)
Introduction to data mining for sustainability
Data Mining and Knowledge Discovery
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery
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The study of land cover change is an important problem in the Earth Science domain because of its impacts on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Most well-known change detection techniques from statistics, signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data. In our work, we seek to address these challenges with new change detection techniques that are based on data mining approaches. Specifically, in this paper we have performed a case study for a new change detection technique for the land cover change detection problem. We study land cover change in the state of California, focusing on the San Francisco Bay Area and perform an extended study on the entire state. We also perform a comparative evaluation on forests in the entire state. These results demonstrate the utility of data mining techniques for the land cover change detection problem.