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.     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.     

构建面向服务的汛情信息共享平台

将面向服务架构的设计原则应用到汛情信息共享平台建设中,以数字驻马店地理空间框架提供的地理信息服务数据为基础,以水利汛情信息为管理对象,借助Flex技术并结合ArcGIS Server平台,实现了汛情信息的共享与集成展示、降雨形势分析等功能,为水利部门提供了一个良好的管理规划和决策支持的Web共享平台。     

一种建筑信息模型与三维数字城市集成方法

BIM的发展为数字城市三维模型提供了更加精确的数据来源,现有研究主要关注BIM空间实体模型向三维表面模型转换,缺少建筑语义信息映射及整体集成方案研究。本文以构成建筑骨架的主要空间构件为研究对象,在参考IFC标准基础上,结合数字城市应用特点设计了具有空间语义一致性的建筑构件信息模型BCM,在此基础上提出建筑信息模型和三维数字城市集成方案。首先,基于CAD图纸开展BIM模型交互式构建,然后,通过空间和语义信息映射将其转换为BCM,并基于ArcGIS Geodatabase建立建筑构件信息库,在三维数字城市场景开展建筑构件信息的集成应用。该方案可实现建筑构件信息与三维数字城市的大规模集成,对推动数字城市向智慧城市发展,进一步开展建筑内外模型集成展示、建筑节能分析、内部设施管理等具有重要价值。     

一级检查在航测外业调绘中的应用与探讨

结合工作实践,主要探讨了一级检查在1：5000及1：10000航测外业调绘工作中的应用,并对如何提高产品质量做一定的思考和研究。     

基于GIS的进口食品安全追溯系统设计与实现

基于现有的福建省地理信息公共服务平台结合WebGIS相关技术,设计实现了福建省检验检疫进口食品安全追溯系统。实现了食品安全信息与GIS信息的快速关联,使得进口食品监管更加直观,为科学决策提供依据。     

地理国情（市情）普查外业信息采集

地理国情监测不仅扩大了测绘行业的内涵,更重要的是,对我国总体布局和规划提供了可靠的依据。目前,在全国范围内逐步开展地理国情普查工作,我分院按照安排,以厦门市为试点,对其进行了外业的调查与核查工作。结合实地作业情况来看,可以在地理国情普查内容与指标基本不变的前提下,依据地域的不同增加分类或修改部分类别的内涵。为今后全面普查工作的顺利开展,对现有的作业方式和作业内容都应有一定的规范。     

新疆地震应急基础数据的处理和建库

介绍了新疆地震应急基础数据库的构成、数据处理和建库及数据库在地震应急指挥技术系统中发挥的作用等。解决了多源数据的处理和整合、多种数据库之间的协同和关联、海量数据的集成等问题。     

基于LBS的移动地理信息服务平台架构技术研究

基于位置的服务(LBS)需求日益增长,在GPS定位技术、通信技术以及电子计算机整体性能不断提高的前提下,LBS与移动GIS结合成为可能,并在专业领域和应用领域发挥重要作用。本文以基于LBS的移动地理信息服务平台架构技术为研究对象,首先分析了SOA架构概述和特性分析,探讨了基于SOA的LBS移动地理信息服务平台架构的设计思路,提出了实现策略,相信对从事相关工作的同行能有所裨益。     

“测绘专业英语”课程教学改革的探索与实践

测绘专业英语既是一门语言课,同时又具有明显的专业课特征,这决定课程在测绘人才培养中具有承前启后的重要作用。测绘专业英语的教学质量,直接影响到大学生毕业后能否较好地利用英语开展测绘领域的相关工作。为此,结合测绘工程本科专业的特点及学生实际,在教学中针对教学目的、教学内容和教学方法做了一些有益的探索与实践。