Fusion of heterogeneous geosensing observations for enhanced site characterization

融合异质地理传感观测以增强场地特征

• 批准号: RGPIN-2020-03995
• 负责人: Fotopoulos, Georgia
• 金额: $ 2.26万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754987
• 关键词: Fusion; heterogeneous; geosensing; observations; enhanced

Site investigation is an essential element to numerous disciplines including surveying, geological, civil, environmental, and mining engineering, as well as archaeology and geophysics. Investment in a rigorous phased site investigation program has been demonstrated to significantly reduce engineering project cost over-runs and future spending during implementation and monitoring phases. A key element in the first phase is site characterization, which involves accurate topographic/morphological information (digital elevation models) and subsurface characterization (lithological/hydrological). These are critical parameters for estimating the type, location, and quantity of intrusive in situ data collection campaigns. The overall objective of this research program is to transform site characterization capabilities for surface and subsurface models at various spatial (macro, meso, micro) and temporal scales (episodic to several annum) via the fusion of state-of-the-art geosensing innovations. Geosensing refers to a technology-driven approach for surveying and mapping from satellite, airborne and terrestrial platforms and includes, but is not limited to, light detection and ranging (LiDAR), Global Navigation Satellite Systems, synthetic aperture radar, optical and hyperspectral imaging and space geodesy. Geodesy is often referred to as the invisible infrastructure because its fundamental principles are at work underlying a variety of observational networks providing a consistent framework for data integration over time and space for diverse disciplines. This research program seeks to challenge the status-quo by discovering opportunities for the innovative fusion of multi-platform, multi-scale observations using geodetic estimation theory and stochastic principles; examples include, (i) augmenting comprehension of geological relationships by integrating 3D laser scanned hand samples within a geospatial information system environment, (ii) mapping surface water via the fusion of synthetic aperture radar, optical imagery and airborne LiDAR, (iii) assessing uncertainty models for satellite gravity observations in a modernized height system and (iv) integrating unmanned vehicle payload solutions into site characterization. HQP will gain advanced skills to excel in a world that demands a high-level of geospatial intelligence for applications in infrastructure, resource and mineral exploration, hazard assessment and environmental monitoring, to name a few. This research program will ensure that Canada remains at the forefront of geomatics engineering by providing innovative, technically sound and high-level uncertainty information for fused solutions leading to rigorous site characterizations, which will improve our policies as we move towards sustainable developments and transition from the information age to the knowledge age in the future.

现场调查是许多学科的基本要素，包括测量，地质，土木，环境和采矿工程，以及考古学和地球物理学。在严格的分阶段现场调查计划中进行投资已被证明可以显着减少工程项目成本超支以及在实施和监测阶段的未来支出。第一阶段的一个关键要素是场地特征，包括精确的地形/形态信息（数字高程模型）和地下特征（岩性/水文）。这些是估算侵入式原位数据收集活动的类型、位置和数量的关键参数。该研究项目的总体目标是通过融合最先进的地理传感创新，在不同的空间（宏观、中观、微观）和时间尺度（从一段到几年）上，转变地表和地下模型的站点表征能力。地球遥感是指从卫星、机载和地面平台进行测绘的技术驱动方法，包括但不限于光探测和测距（激光雷达）、全球导航卫星系统、合成孔径雷达、光学和高光谱成像以及空间大地测量学。大地测量学通常被称为无形的基础设施，因为它的基本原理是各种观测网络的基础，为不同学科的数据集成提供了一个一致的框架。该研究项目旨在通过发现利用大地测量估计理论和随机原理进行多平台、多尺度观测的创新融合的机会来挑战现状；例子包括：(i)通过在地理空间信息系统环境中集成3D激光扫描的手部样本来增强对地质关系的理解；（ii）通过合成孔径雷达、光学图像和机载激光雷达的融合来绘制地表水图；（iii）评估现代化高度系统中卫星重力观测的不确定性模型；（iv）将无人驾驶车辆有效载荷解决方案集成到现场表征中。HQP将获得先进的技能，以在基础设施、资源和矿产勘探、危害评估和环境监测等应用中需要高水平的地理空间情报的世界中脱颖而出。该研究项目将确保加拿大保持在地理信息工程的前沿，为融合解决方案提供创新的、技术可靠的和高水平的不确定性信息，从而导致严格的场地特征，这将改善我们的政策，因为我们将朝着可持续发展和从信息时代过渡到知识时代。