III: Small: Spatial Deep Learning from Imperfect Volunteered Geographic Information

III：小：从不完美的自愿地理信息中进行空间深度学习

• 批准号: 2207072
• 负责人: Zhe Jiang
• 金额: $ 50万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207072&HistoricalAwards=false
• 关键词: III; Small; Spatial; Deep; Learning

This project aims to investigate novel spatial machine learning algorithms based on imperfect volunteered geographical information as ground truth for applications at the intersection of machine learning and geographic information science. The rapid growth of geospatial and spatiotemporal data being collected from space, airborne, and terrestrial platforms provides scientists, farmers, and first responders critical information they need about the surface of the Earth. This emerging area that intersects machine learning, especially deep learning, with geographic information science is called GeoAI. GeoAI can potentially transform society by addressing grand challenges such as rapid disaster response, water resource management, and transportation. One major obstacle, however, is that deep learning heavily relies on a large number of training labels, which are often not easily available for geographic applications due to slow and expensive field surveys. Existing research on semi-supervised learning could not fully resolve the issues due to the complex nature of geographic data such as spatial heterogeneity. This project will fill the gap by exploiting large scale, low-cost, and near real-time volunteered geographic information. The project will contribute towards the next generation water resource management for the U.S. in the 21st century. This research can not only improve the situational awareness for disaster response agencies but also enhance the flood forecasting capabilities of the National Water Model. Planned algorithms will be implemented into open source tools that will enhance the research infrastructure for disaster management and hydrology communities. Educational activities include curriculum development, K-12 computer science education at Alabama Computer Science Summer Camps. The principal investigator has a past record in mentoring undergraduate students from a historically black university and will continue the efforts at the University of Alabama, which has a reputation for producing African American researchers. The planned framework will bring about several innovations to address significant technical challenges due to data quality issues. First, to address the noisy, biased, and incomplete label semantics, the project will develop novel label enhancement and enrichment algorithms based on physics-aware spatial structural constraints, which advance existing methods (that often assume independent labels) by jointly enhancing labels based on structural dependency. Second, the project will explore a new location error model that better captures geometric shapes than existing square patch-based models, and design efficient joint learning algorithms to update deep model parameters while inferring true shape locations. Finally, to address location ambiguity, the project will explore location ambiguity models and mitigating location ambiguity by leveraging geographical contexts from input imagery features as well as spatial hierarchical constraints. Such ideas advance existing location disambiguation methods merely based on the textual semantic context in natural language processing. The project can potentially transform the field of geospatial data science by addressing a major obstacle of limited training labels by a systematic framework of exploiting large-scale, low-cost, and near real-time volunteered geographic information data. The project can potentially make transformative impacts on interdisciplinary GeoAI applications such as rapid disaster response and national water forecasting. This project is jointly funded by III and the Established Program to Stimulate Competitive Research (EPSCoR).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.

该项目旨在研究基于不完美的志愿地理信息的新型空间机器学习算法，作为机器学习与地理信息科学相交的应用的基础真理。从太空，空降和陆地平台收集的地理空间和时空数据的快速增长为科学家，农民和第一响应者提供了他们所需的有关地球表面的关键信息。与地理信息科学相交的机器学习，尤其是深度学习的新兴领域称为GEOAI。 Geoai可以通过应对诸如快速灾难响应，水资源管理和运输等巨大挑战来改变社会。但是，一个主要的障碍是，深度学习在很大程度上取决于大量的培训标签，由于较慢且昂贵的现场调查，通常不容易用于地理应用。由于地理数据（例如空间异质性）的复杂性质，现有关于半监督学习的研究无法完全解决问题。该项目将通过利用大规模，低成本和接近实时的志愿地理信息来填补空白。该项目将在21世纪为美国的下一代水资源管理做出贡献。这项研究不仅可以提高灾难反应机构的情境意识，而且还可以增强国家水域水模型的洪水预测能力。计划的算法将被实施到开源工具中，以增强灾难管理和水文学社区的研究基础设施。教育活动包括课程开发，阿拉巴马州计算机科学夏令营的K-12计算机科学教育。首席研究员在指导一所历史悠久的黑人大学的本科生方面拥有过去的记录，并将继续在阿拉巴马大学的努力，该大学以生产非裔美国人的研究人员而闻名。计划的框架将带来一些创新，以应对数据质量问题，以应对重大的技术挑战。首先，为了解决嘈杂，有偏见和不完整的标签语义，该项目将基于物理感知的空间结构约束来开发新的标签增强和富集算法，这些结构约束可以通过基于结构依赖性来促进现有方法（通常假设独立的标签）。其次，该项目将探索一个新的位置错误模型，该模型比现有的基于平方补丁的模型更好地捕获几何形状，并设计有效的关节学习算法，以更新深层模型参数，同时推断真实形状的位置。最后，为了解决位置歧义，该项目将通过利用输入图像特征以及空间层次结构的地理环境来探索位置歧义模型并减轻位置歧义。这些想法仅基于自然语言处理中的文本语义上下文，推进了现有的位置歧义方法。该项目可以通过利用大规模，低成本和近实时志愿地理信息数据的系统框架来解决有限培训标签的主要障碍，从而有可能改变地理空间数据科学领域。该项目可能会对跨学科的GEOAI应用产生变革性的影响，例如快速灾难响应和国家水预测。该项目由III共同资助和启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

项目成果

An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)

用于在地球图像上绘制洪水范围的高程引导注释工具（演示论文）

• DOI: 10.1145/3557915.3560962
• 发表时间: 2022
• 期刊: SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
• 作者: Adhikari, Saugat;Yan, Da;Sami, Mirza Tanzim;Khalil, Jalal;Yuan, Lyuheng;Joy, Bhadhan Roy;Jiang, Zhe;Sainju, Arpan Man
• 通讯作者: Sainju, Arpan Man

Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy

• DOI: 10.1137/1.9781611976700.29
• 发表时间: 2020-08
• 期刊: ArXiv
• 作者: Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan

Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors

• DOI: 10.1145/3447548.3467301
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Zhe Jiang;Wenchong He;M. Kirby;S. Asiri;Dan Yan

A Hidden Markov Forest Model for Terrain-Aware Flood Inundation Mapping from Earth Imagery

用于根据地球图像进行地形感知洪水淹没绘图的隐马尔可夫森林模型

• 发表时间: 2023
• 期刊: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM
• 作者: Jiang, Zhe;Zhang, Yupu;Adhikari, Saugat;Yan, Da;Sainju, Arpan Man;Jia, Xiaowei;Xie, Yiqun
• 通讯作者: Xie, Yiqun

Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction

EM 算法的半监督学习：非结构化和结构化预测的比较研究

• DOI: 10.1109/tkde.2020.3019038
• 发表时间: 2020
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: He, Wenchong;Jiang, Zhe
• 通讯作者: Jiang, Zhe