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: 2008973
• 负责人: Zhe Jiang
• 金额: $ 50万
• 依托单位: University of Alabama Tuscaloosa
• 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: 2020-10-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008973&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自愿地理信息的新型空间机器学习算法，作为机器学习和地理信息科学交叉应用的基础事实。从太空、机载和地面平台收集的地理空间和时空数据的快速增长为科学家、农民和急救人员提供了他们所需的有关地球表面的关键信息。这个将机器学习（尤其是深度学习）与地理信息科学相交叉的新兴领域被称为 GeoAI。 GeoAI 可以通过解决快速灾难响应、水资源管理和交通等重大挑战来改变社会。然而，一个主要障碍是深度学习严重依赖大量的训练标签，由于实地调查缓慢且昂贵，这些标签通常不容易用于地理应用。由于地理数据的复杂性（例如空间异质性），现有的半监督学习研究无法完全解决这些问题。该项目将通过利用大规模、低成本和近实时的自愿地理信息来填补这一空白。该项目将为美国21世纪的下一代水资源管理做出贡献。这项研究不仅可以提高灾害应对机构的态势感知能力，还可以增强国家水模型的洪水预报能力。计划的算法将被实施到开源工具中，这将增强灾害管理和水文学界的研究基础设施。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。首席研究员过去曾指导过一所历史悠久的黑人大学的本科生，并将在阿拉巴马大学继续努力，该大学以培养非裔美国研究人员而闻名。计划中的框架将带来多项创新，以解决数据质量问题带来的重大技术挑战。首先，为了解决嘈杂、有偏见和不完整的标签语义，该项目将开发基于物理感知空间结构约束的新型标签增强和丰富算法，通过基于结构依赖性联合增强标签来推进现有方法（通常假设独立标签）。其次，该项目将探索一种新的位置误差模型，该模型比现有的基于方形补丁的模型更好地捕获几何形状，并设计高效的联合学习算法来更新深度模型参数，同时推断真实的形状位置。最后，为了解决位置模糊性，该项目将探索位置模糊性模型，并通过利用输入图像特征的地理背景以及空间层次约束来减轻位置模糊性。这些想法推进了现有的仅基于自然语言处理中的文本语义上下文的位置消歧方法。该项目可以通过利用大规模、低成本和近实时自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，从而潜在地改变地理空间数据科学领域。该项目可能会对跨学科的 GeoAI 应用产生变革性影响，例如快速灾害响应和国家水资源预测。该项目由 III 和刺激竞争性研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

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

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

A Hidden Markov Tree Model for Flood Extent Mapping in Heavily Vegetated Areas based on High Resolution Aerial Imagery and DEM: A Case Study on Hurricane Matthew Floods

基于高分辨率航空图像和 DEM 的植被茂密地区洪水范围测绘的隐马尔可夫树模型：飓风马修洪水案例研究

• DOI: 10.1080/01431161.2020.1823514
• 发表时间: 2021
• 期刊: International Journal of Remote Sensing
• 影响因子: 3.4
• 作者: Jiang, Zhe;Sainju, Arpan Man
• 通讯作者: Sainju, Arpan Man

Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

• DOI: 10.3390/rs13122368
• 发表时间: 2021-06
• 期刊: Remote. Sens.
• 作者: L. Stanislawski;E. Shavers;Shaowen Wang;Zhe Jiang;E. L. Usery;E. Moak;Alexander Duffy;Joel Schott-Joel-S

An attention U-Net model for detection of fine-scale hydrologic streamlines

• DOI: 10.1016/j.envsoft.2021.104992
• 发表时间: 2021-02
• 期刊: Environ. Model. Softw.
• 作者: Zewei Xu;Shaowen Wang;L. Stanislawski;Zhe Jiang;N. Jaroenchai;Arpan Man Sainju;E. Shavers;E. L. Usery;Li Chen;Zhiyu Li;Bin Su