Deep Learning in Geospatial Uncertainty Modeling

地理空间不确定性建模中的深度学习

• 批准号: 2026331
• 负责人: Guofeng Cao
• 金额: $ 26.51万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026331&HistoricalAwards=false
• 关键词: Deep; Learning; Geospatial; Uncertainty; Modeling

This project will contribute new statistical methods and tools for the analysis of complex and incompatible geospatial data to advance uncertainty-aware, data-driven geographic knowledge discovery. As geospatial data become increasingly available, the complexity of geospatial data marked by complex patterns and sources of uncertainty increases dramatically, and goes well beyond the capabilities of conventional methods. This project will develop an uncertainty framework to support statistical learning of incompatible geospatial data for complex spatiotemporal patterns. This framework will be used to characterize and quantify the geospatial uncertainty in scientific modeling and practical applications. This project will offer novel solutions to fundamental analysis, modeling, and integration problems involving geospatial data, and advance the understanding of the nature of geospatial uncertainty. This project will enhance the proper and cost-effective utilization of geospatial data, and will have broader impacts on disciplines that geospatial data are involved. Furthermore, with a public outreach on uncertainty-aware spatial thinking, this project will advance the public good by increasing the public awareness of the geospatial uncertainty and critical map reading and usage.From a geostatistics perspective, this project will address the long-standing unsolved challenges in spatial analysis dealing with complex spatiotemporal patterns, heterogeneity and uncertainty of geospatial data. First, the developed framework will provide a comprehensive and practical approach to seamlessly integrate heterogeneous sources of geospatial data. Secondly, capitalizing on recent advances in deep learning technologies, the developed framework will characterize and model the complex spatiotemporal patterns implied in heterogeneous data. Thirdly, the developed framework will enable the characterization and modeling of geospatial uncertainty and the impact assessment in domain fields. The performances of the developed methods will be evaluated in two domain applications: spatiotemporal disease mapping in public health and modeling uncertainty of land cover changes and the impact on atmospheric models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将提供新的统计方法和工具，用于分析复杂和不兼容的地理空间数据，以促进具有不确定性的数据驱动地理知识发现。随着地理空间数据越来越容易获得，以复杂模式和不确定性来源为特点的地理空间数据的复杂性急剧增加，远远超出了传统方法的能力。该项目将开发一个不确定性框架，以支持对复杂时空模式的不兼容地理空间数据进行统计学习。该框架将用于描述和量化科学建模和实际应用中的地理空间不确定性。该项目将为涉及地理空间数据的基本分析、建模和集成问题提供新颖的解决方案，并促进对地理空间不确定性本质的理解。该项目将加强对地理空间数据的适当和具有成本效益的利用，并将对地理空间数据所涉及的学科产生更广泛的影响。此外，该项目还将通过对公众宣传认识到不确定性的空间思维，提高公众对地理空间不确定性以及关键的地图阅读和使用的认识，从而促进公共利益，并将从地统计学的角度解决在处理复杂的时空模式、异质性和地理空间数据不确定性的空间分析方面长期存在的未解决的挑战。首先，开发的框架将提供一个全面和实用的方法来无缝集成异构的地理空间数据源。其次，利用深度学习技术的最新进展，开发的框架将表征和建模异构数据中隐含的复杂时空模式。第三，所开发的框架将使表征和建模的地理空间不确定性和领域领域的影响评估。所开发的方法的性能将在两个领域的应用程序进行评估：时空疾病映射在公共卫生和建模的不确定性的土地覆盖变化和对大气models.This奖项的影响反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Towards the intelligent era of spatial analysis and modeling

• DOI: 10.1145/3557918.3565863
• 发表时间: 2022-11
• 期刊: Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
• 作者: Di Zhu;Song Gao;Guofeng Cao

Spatial patterns of soil microbial communities and implications for precision soil management at the field scale

• DOI: 10.1007/s11119-021-09872-1
• 发表时间: 2022-01
• 期刊: Precision Agriculture
• 影响因子: 6.2
• 作者: Jasmine Neupane;Wenxuan Guo;G. Cao;Fangyuan Zhang;Lindsey C. Slaughter;S. Deb