NSF III-CXT:Advanced learning and integrative knowledge transfer approaches to remote sensing and forecast modeling for understanding land use change (2007-2011) (Award 0705836)
Joydeep Ghosh, University of Texas, Austin
Melba Crawford, Purdue University
Bryan C. Pijanowski, Purdue University
The characterization of land cover and usage over large geographical regions, as well as the near/long-term forecasting of changes in land use, is a key problem in geo-informatics that is particularly important for regions that are subject to rapid ecological changes or urbanization. At present, the data and knowledge required for detailed and accurate characterization is scattered across both traditional (GIS) spatial data sources and remotely sensed data, and their associated models. We propose to integrate information from both GIS and remote sensing at multiple modeling and methodological stages in order to develop a comprehensive framework for efficient and accurate mapping, monitoring and modeling of land cover and changes in usage over large regions. This endeavor involves three complementary activities: (i) large scale classification of remote sensing imagery using advanced learning methods, including transfer learning, active learning and manifold based data descriptors; (ii) next-generation spatial modeling using ensembles for forecasting land transformations; and (iii) integration of GIS and remote sensing data by distributed, privacy aware learning, integrating taxonomies obtained from different data sources and portal building. A plan of interaction with various stakeholders is proposed to ensure that our results are meaningful and actionable. This project will result in substantial advances in analysis of remotely sensed data over extended regions and lead to a substantial reduction in the uncertainty of long-term forecasts of change. Concurrently, the chosen application domain will also provide a concrete setting that motivates several new data mining problems, leading to new algorithmic formulations and solutions that benefit the broader data mining community.
Our project is designed to have many, diverse broader impacts. First, the project involves application scientists in the remote sensing and modeling communities who will benefit from advanced methods in machine learning. A plan is presented to take research results into the classroom through specific new graduate courses. Popular science lectures for middle and high school are also planned since the subject matter and results can be conveyed meaningfully to this audience in a visual way that emphasizes issues of broader concern, such as the impact of ecological changes and urban sprawl. Two project-wide workshops are proposed that will also involve stakeholders (e.g., planners) who would directly benefit from our results and provide valuable feedback. A portal will be created in year 3 to provide access to data, code and toolkits produced by the project. Modelers and stakeholders will be important users of the project web portal. Results will be disseminated in each of the three main disciplines represented within the project through scholarly publications. Finally, we shall develop our tools so that they could eventually be incorporated into COTS software, such as GIS and remote sensing software. Keywords: data mining; transfer learning; remote sensing; land use forecasting
Last updated March 6, 2011