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

III-CXT：协作研究：遥感和预测建模的高级学习和综合知识转移方法，以了解土地利用变化

• 批准号: 0705836
• 负责人: Melba Crawford
• 金额: $ 56.05万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705836&HistoricalAwards=false
• 关键词: III; CXT; Collaborative; Research; Advanced

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.

智力优点。大地理区域的土地覆盖和利用特征，以及土地利用变化的近/长期预测，是地理信息学的一个关键问题，对于生态快速变化或城市化的地区尤为重要。目前，详细而准确的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，而这些模型都不能很好地互操作。这项研究将开发一个全面的框架，用于对大区域的土地覆盖和使用变化进行高效、准确的绘图、监测和建模。这项工作涉及三项互补的活动：（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) 使用集成来预测土地转变的下一代空间模型； (iii) 通过分布式、隐私意识学习、集成从不同数据源获得的分类法和门户构建来集成 GIS 和遥感数据。提出与各利益相关者互动的计划，以确保结果有意义且可操作。该项目将在扩展区域的遥感数据分析方面取得重大进展，并导致长期变化预测的不确定性大幅减少。同时，所选的应用领域还将提供一个具体的设置，激发一些新的数据挖掘问题，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。该项目旨在产生多种、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举办科普讲座，因为可以通过视觉方式向观众有意义地传达主题和结果，强调更广泛关注的问题，例如生态变化和城市扩张的影响。建议举办两个项目范围的研讨会，利益相关者（例如规划者）也将参与其中，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，以提供对该项目生成的数据、代码和工具包的访问。结果将通过学术出版物在该项目所代表的三个主要学科中传播。最后，将开发工具，以便最终将其纳入商业现成软件，例如 GIS 和遥感软件。