CAREER: Mapping Anthropocene Geomorphology with Deep Learning, Big Data Spatial Analytics, and LiDAR

职业：利用深度学习、大数据空间分析和激光雷达绘制人类世地貌图

基本信息

批准号：
2046059
负责人：
Aaron Maxwell
金额：
$ 63.68万
依托单位：
West Virginia University Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046059&HistoricalAwards=false
关键词：
CAREER Mapping Anthropocene Geomorphology Deep

项目摘要

Geospatial data (e.g., aerial and satellite imagery, digital elevation data, and weather observations) are being generated at an astounding rate. One example, the US Geological Survey’s Landsat Earth observation program, collects about a terabyte of data daily, and their 3D Elevation Program (3DEP) is working toward generating the first-ever high-detail elevation dataset for the entire country, which is scheduled to be completed by 2023. While these “big data” projects present many opportunities, it is currently very difficult to extract actionable information from them in an efficient manner that supports scientific research and informed decision making. While recent advances in artificial intelligence and machine learning show great promise in analyzing such data, there is a need to further research and develop these techniques for application to digital mapping tasks, such as detecting landslides, monitoring resource extraction, and documenting landscape change. This research will develop state-of-the-art “deep learning”-based techniques to derive valuable information on human modifications to the landscape using geospatial data, including elevation models and historic maps, to fundamentally advance geomorphic mapping science. In addition to supporting and training graduate students at West Virginia University, the work will engage future high school STEM teachers, in-service teachers, and high school students by developing training and instructional materials that will help enable the next generation of data scientists, geospatial professionals, and coders.This project will advance the application of geospatial data analytics and advanced computational methods to extract high spatial resolution information from geospatial data over wide regions to further understanding of natural landscapes and anthropogenic landscape change. It specifically explores semantic and instance segmentation deep learning methods based on convolutional neural networks (CNNs), which are able to model spatial context information, for extracting geomorphic features and historic mining from geospatial data, including historic topographic maps, light-detection and ranging (LiDAR) point clouds, and additional terrain representations (i.e., hillshades and other topographic derivatives). Ultimately, this project will contribute to operationalizing deep learning for geomorphic mapping using the increasing abundance of quality digital terrain data with the eventual goal of generating accurate datasets at regional to global extents that will allow for documentation, quantification, and modeling of geomorphic hazards and natural and anthropogenic landscape change. This project is jointly funded by Geomorphology and Land-use Dynamics 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.

地理空间数据（例如，空中和卫星图像，数字高程数据和天气观察）正在以惊人的速度生成。一个例子是，美国地质调查局的Landsat地球观察计划，收集了有关数据的Terabyte及其3D高程计划（3DEP）的努力，正在努力为整个国家生成有史以来的第一个高点升级数据集，该数据集计划在2023年完成，而这些措施的措施是有效的。制作。尽管人工智能和机器学习的最新进展在分析此类数据方面表现出了巨大的希望，但有必要进一步研究并开发这些技术以应用于数字地图任务，例如检测滑坡，监视资源提取和文档景观变化。这项研究将开发最新的“深度学习”技术，以使用地理空间数据（包括高程模型和历史图）从根本上推进地貌映射科学，从而获取有关人类修饰的有价值的人类修饰的信息。除了在西弗吉尼亚大学的支持和培训研究生外，这项工作还将通过开发培训和教学材料来吸引未来的高中STEM老师，服务教师和高中生，这将有助于使下一代数据科学家，地理空间专业人员和编码人员和编码人员和编码人员一起。该项目将促进高级数据分析和高级计算方法的应用，从景观和人为景观的变化。 It specifically explores semantic and instance segmentation deep learning methods based on convolutional neural networks (CNNs), which Ultimately, this project will contribute to operating deep learning for geomorphic mapping using the increasing abundance of quality digital terrain data with the event goal of generating accurate datasets at regional to global extents that will allow for documentation, quantification, and modeling of geomorphic hazards and natural and anthropogenic landscape 改变。该项目由地貌学和土地利用动态以及刺激竞争研究的既定计划（EPSCOR）共同资助。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估的评估。