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

知识的优点。大地理区域的土地覆盖和土地利用特征以及土地利用变化的近期/长期预测是地理信息学中的一个关键问题，对遭受快速生态变化或城市化的区域尤其重要。目前，详细和准确表征所需的数据和知识分散在传统（GIS）空间数据源和遥感数据及其相关模型中，这些数据和知识都不能很好地互操作。这项研究将制定一个全面的框架，以便有效和准确地测绘、监测和模拟大区域的土地覆盖和利用变化。这一努力涉及三个互补的活动：(i)使用先进的学习方法对遥感图像进行大规模分类，包括迁移学习、主动学习和基于流形的数据描述符；（ii）利用集合预测土地变化的下一代空间建模；（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以及门户网站建设，实现GIS和遥感数据的集成。提出了与各利益相关者互动的计划，以确保结果是有意义的和可操作的。这个项目将使对广大区域遥感数据的分析取得重大进展，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，从而产生新的算法公式和解决方案，使更广泛的数据挖掘社区受益。更广泛的影响。该项目旨在产生许多不同的更广泛的影响。首先是遥感和建模领域的应用科学家的参与，他们将从机器学习的先进方法中受益。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举办科普讲座，因为主题和结果可以通过视觉方式有意义地传达给观众，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，也将涉及利益相关者（例如规划人员），他们将直接从结果中受益并提供宝贵的反馈。将在第三年创建一个门户网站，以提供对该项目产生的数据、代码和工具包的访问。结果将通过学术出版物在项目所代表的三个主要学科中传播。最后，将开发工具，以便最终将它们纳入商业现成软件，例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.