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III: Small: Geospatial Data Representation and Analysis through the Stellar Decomposition

III：小：通过恒星分解进行地理空间数据表示和分析

基本信息

批准号：
1910766
负责人：
Leila DeFloriani
金额：
$ 49.96万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910766&HistoricalAwards=false
关键词：
III Small Geospatial Data Representation

项目摘要

Thanks to recent developments in remote sensing technologies, the amount of spatial and spatio-temporal data currently available has been dramatically increasing. Specifically, LiDAR (Light Detection and Ranging) technologies generate precise three-dimensional information about the shape of the Earth, and its characteristics, in the form of massive point clouds. LiDAR data are used in a variety of different fields, such as urban modeling, climate study, earthquake analysis, disaster management, flood risk mapping, forestry analysis. Capitalizing on the opportunities presented by such massive, high-resolution, data and representing, analyzing and transforming them into useful information poses several challenges. The project focuses on developing scalable data representations, and algorithms for processing and analyzing scattered big geospatial data. The emphasis is on dealing with point clouds of very large size, arising from LiDAR acquisitions, and on applications to terrain modeling and to tree reconstruction from forestry data, with benefits to the research in environmental and Earth science. Being the approach entirely data agnostic, the project has a potential impact on a broader range of applications, including neuroscience, social science, and virtual reality. Software tools for modeling and analysis of very large terrains, and for forestry segmentation will be developed and distributed in the public domain.Currently, raw LiDAR point clouds are processed by first converting them into raster models, with high computational costs, potential loss of information and creation of artifacts due to missing data and to the presence of noise. The innovative aspect of the project is in performing data analysis directly on the LiDAR point clouds. This requires encoding the neighboring relations among the points as a simplicial mesh so as to provide an approximation of the underlying "shape" of the point cloud. In order to be able to deal with massive data sets, the research will develop efficient and effective mesh data structures, based on a new data clustered spatio-topological model, the Stellar decomposition, supporting scalability, and efficient processing of fundamental spatial and connectivity queries. New algorithms, rooted in computational topology, will be developed for terrain simplification and analysis, and for tree reconstruction from forestry data. The new scalable framework will make the analysis of terrain and forest data possible on commodity hardware even for datasets composed of billions of points. Moreover, thanks to the use of the Stellar decomposition, it will be well suited for implementations in a distributed environment for the analysis of such data at global Earth scale. The project website will include, besides resulting publications, the public-domain software tools developed, and benchmark and real datasets used, for demonstration purposes and to ensure reproducibility.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.

由于遥感技术的最新发展，目前可获得的空间和时空数据量急剧增加。具体来说，LiDAR（光探测和测距）技术以大量点云的形式生成有关地球形状及其特征的精确三维信息。LiDAR数据被用于各种不同的领域，如城市建模，气候研究，地震分析，灾害管理，洪水风险测绘，林业分析。利用如此大规模的高分辨率数据所带来的机遇，并将其表示、分析和转换为有用的信息，这带来了一些挑战。该项目的重点是开发可扩展的数据表示，以及用于处理和分析分散的大地理空间数据的算法。重点是处理非常大尺寸的点云，从激光雷达采集，并应用到地形建模和树木重建从林业数据，有利于环境和地球科学的研究。作为完全数据不可知的方法，该项目对更广泛的应用具有潜在影响，包括神经科学，社会科学和虚拟现实。目前，激光雷达原始点云的处理方法是首先将其转换为光栅模型，计算成本很高，可能会丢失信息，而且由于数据丢失和噪声的存在，会产生伪像。该项目的创新之处在于直接在LiDAR点云上进行数据分析。这需要将点之间的相邻关系编码为单纯网格，以便提供点云的基本“形状”的近似。为了能够处理海量数据集，研究将开发高效和有效的网格数据结构，基于一种新的数据聚类空间拓扑模型，恒星分解，支持可扩展性，并有效地处理基本的空间和连接查询。新的算法，植根于计算拓扑学，将开发地形简化和分析，并从林业数据的树木重建。新的可扩展框架将使地形和森林数据的分析成为可能，即使是由数十亿个点组成的数据集。此外，由于使用恒星分解，它将非常适合在分布式环境中实现，以分析全球地球尺度的此类数据。该项目的网站将包括，除了最终的出版物，开发的公共领域的软件工具，基准和真实的数据集使用，用于演示目的，并确保reproducibility.This奖项反映了NSF的法定使命，并已被认为是值得的支持，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。