III-CXT: Collaborative Research: Advanced learning and integrative knowledge transfer approaches to remote sensing and forecast modeling for understanding land use change

智力优势。确定大面积地理区域土地覆盖和使用的特点以及对土地使用变化的近期/长期预测是地理信息学的一个关键问题，对于生态变化或城市化迅速的区域尤为重要。目前，详细和准确的定性所需的数据和知识分散在传统的（地理信息系统）空间数据源和遥感数据及其相关模型中，没有一个相互作用良好。这项研究将制定一个全面的框架，以便对大区域的土地覆盖和使用变化进行有效和准确的绘图、监测和建模。这一奋进涉及三项相辅相成的活动：㈠利用先进学习方法，包括迁移学习、主动学习和基于流形的数据描述符，对遥感图像进行大规模分类; ㈡利用集合预测土地变化的下一代空间建模;以及（iii）通过分布式、隐私意识学习，整合从不同数据源获得的分类和门户建设。提出了与各利益相关者互动的计划，以确保结果有意义且可操作。这一项目将大大促进对广大区域遥感数据的分析，并大大减少长期变化预测的不确定性。同时，所选择的应用领域还将提供一个具体的环境，激发几个新的数据挖掘问题，导致新的算法公式和解决方案，有利于更广泛的数据挖掘社区。更广泛的影响。这个项目旨在产生许多不同的更广泛的影响。首先是遥感和建模社区的应用科学家的参与，他们将受益于机器学习的先进方法。研究成果将通过新的研究生课程带入课堂。还计划为初中和高中开设科普讲座，因为可以以视觉方式向这些听众传达有意义的主题和结果，强调更广泛关注的问题，如生态变化和城市扩张的影响。建议举办两个项目范围的讲习班，也将有利益攸关方（例如，规划人员），他们将直接从结果中受益，并提供有价值的反馈。将在第三年创建一个门户网站，以提供对项目产生的数据、代码和工具包的访问。将通过学术出版物传播项目所代表的三个主要学科的成果。最后，将开发各种工具，以便最终将其纳入现有的商业软件，如地理信息系统和遥感软件。

Intellectual Merits. 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 across remotely sensed data, and their associated models, none of which inter-operate well. This research will 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 the 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. Broader Impacts. This project is designed to have many, diverse broader impacts. First is the involvement of application scientists in the remote sensing and modeling communities who will benefit from advanced methods in machine learning. The research results will be brought into the classroom through 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 the 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. Results will be disseminated in each of the three main disciplines represented within the project through scholarly publications. Finally, tools will be developed so that they may eventually be incorporated into Commercial Off The Shelf software, such as GIS and remote sensing software.