Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Mapping collective human activity in an urban environment based on mobile phone data

Identifying and characterizing variations of human activity – specifically changes in intensity and similarity – in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of user-generated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collective human activity. In this article, geocomputational methods and geovisual analytics such as self-organizing maps (SOM) are used to explore the variations of these profiles, and its implications for collective human activity. We evaluate the merits of SOM as a cross-dimensional clustering technique and derived temporal trajectories of variations within the mobile communication profiles. The trajectories’ characteristics such as length are discussed, suggesting spatial variations in intensity and similarity in collective human activity. Trajectories are linked back to the geographic space to map the spatial and temporal variation of trajectory characteristics. Different trajectory lengths suggest that mobile phone activity is correlated with the spatial configuration of the city, and so at different times of the day. Our approach contributes to the understanding of the space-time social dynamics within urban environments.     

资源一号02C卫星多光谱数据真彩色模拟技术研究

资源一号02C卫星多光谱数据没有蓝色波段,给制作真彩色影像带来了一定困难。本文针对02C卫星多光谱波段的特点,分析了常用线性模拟蓝波段影像方法,选取多景不同时相的、覆盖山地或平地的真实数据进行加权法和平均值法的对比试验,定性与定量分析后的结果表明：基于加权法生成蓝波段的真彩色模拟结果,无论是从整体色调方面,还是从典型地类的颜色方面,均优于基于平均值法生成蓝波段的真彩色模拟结果,可进一步深入研究后进行规模化应用。     

区域似大地水准面精化算法及成果三维仿真研究

利用GPS水准代替繁重的几何水准测量工作,必须确定高精度、高分辨率的似大地水准面。本文分别介绍了函数模型算法、随机模型算法和综合模型算法,并基于试验区域的GPS水准数据,通过各种算法的分类计算比较,验证了这几种模型算法的有效性和可靠性,结果证明最小二乘配置算法精度较高,并对实验结果进行三维仿真,可视化的分析似大地水准面的变化。     

利用波场模拟方法检验修正瑞雷波频散曲线

目的 提出通过波场模拟来对比不同的模态解释的正确性,以检验和修正频散曲线的提取。对模拟数据和实测数据的分析表明,该方法进行频散曲线的检验和修正是可靠的,可以有效提高频散数据的质量。     

利用多源领域知识迁移CA的城市建设用地模拟

目的 建立传统元胞自动机(CA)模型时,如果样本数量不足,模拟效果往往非常不理想。提出了多源领域知识迁移CA模型,利用多个已有的旧样本数据集来帮助建立新的CA模型,并选取广东省深圳市作为试验区验证了其有效性。试验结果表明,该模型在新样本数量不足的情况下能够明显改善模拟效果,并且有效减小产生负迁移现象的风险。     

甚长基线干涉测量天线参考点和轴线偏差监测方法探讨

高精度监测VLBI天线参考点和轴线偏差等参数,对提高VLBI台站坐标测定精度及天测、测地VLBI资料解析精度等具有重要意义.常规监测方式占用望远镜工作时间,监测效率低,所得参数精度较差.文章提出通过参数化VLBI天线的旋转运动建立天线固连合作目标在局域网中坐标数学模型的方法,有望实现全天候、全自动监测;考察了代表性数学模型,在观测方程构建、解算参数设置和约束条件选取等方面提出了新的见解.最后通过仿真分析,验证了对方程与参数所做调整的有效性,初步结论可供参考.     

从大模型看测绘时空信息智能处理的机遇和挑战

当前,时空信息、定位导航服务已成为重要的新型基础设施,在通用人工智能的驱动下,大模型引领的智能时代已经到来,越来越强大的大模型将在测绘时空信息智能处理与应用中发挥越来越重要的作用。以大模型为研究范式,总结大模型在测绘时空信息智能处理的现状与进展,分析大模型在测绘时空信息智能处理面临的挑战,阐述多模态融合与理解架构设计、提示工程优化微调以及人在回路引导决策3个核心方面在时空信息测绘大模型中的关键作用。针对行业理解深度、数据安全隐患、内容可信度保障以及训练部署成本优化4个方面,展望时空信息测绘大模型面临的挑战与发展趋势。

     

天文经纬度仿真测量精度分析

介绍了天文测量室内仿真系统原理和天文经纬度测量流程;提出了测量精度的评定方法;分析了仿真系统天文经纬度测量的精度。结果表明:系统天文经纬度测量可以达到二等以上的天文测量要求,可以应用于日常天文测量训练和教学。