The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Use of cartographic images by expert and novice field geologists in planning fieldwork routes

Topographic maps and aerial photographs are particularly useful when geoscientists are faced with fieldwork tasks such as selecting paths for observation, establishing sampling schemes, or defining field regions. These types of images are crucial in bedrock geologic mapping, a cognitively complex field-based problem-solving task. Geologic mapping requires the geologist to correctly identify rock types and three-dimensional bedrock structures from often partial or poor-quality outcrop data while navigating through unfamiliar terrain. This paper compares the walked routes of novice to expert geologists working in the field (n = 66) with the results of a route planning and navigation survey of a similar population of geologists (n = 77). Results show clearly that those geologists with previous mapping experience make quick and decisive determinations about field areas from available imagery and maps, regardless of whether they are or not physically present in the field area. Recognition of geologic features enabled experts to form and verbalize a specific plan for travel through a landscape based on those features. Novices were less likely to develop specific travel route plans and were less likely to identify critical landscape cues from aerial photographs.     

强跟踪抗差自适应滤波算法及其在无人机导航定位中的应用

针对Sage-Husa自适应滤波算法在无人机导航定位应用中存在滤波发散和定位精度低的问题,本文提出一种强跟踪抗差自适应滤波算法.该算法在Sage-Husa自适应滤波算法基础上,引入强跟踪技术,通过自适应渐消因子降低历史数据对当前滤波的影响,从而抑制滤波发散,增强算法的稳健性;结合量测噪声和系统噪声进行实时估计,并且在估...     

大尺度平原城市精细汇水区分级划分研究

在城市内涝模拟研究中,汇水区划分是十分重要的环节,同时对大尺度平原城市进行汇水区精细划分也是研究者共同研究的技术难点.针对现有基于DEM流向分析的划分方法存在的无法正确反映实际城市复杂地形和流向问题,本文提出了一种顾及地类和流向,适用于大尺度平原城市的精细汇水区分级划分方法.首先,从城市自然地形和主干河流出发,进行一级...     

无人机三维倾斜摄影测量助力自然资源调查——以不动产调查为例

随着第一次地理国情普查与监测、第三次全国土地调查等重大自然资源调查类项目的实施,以无人机作为搭载平台的遥感影像获取手段在不断发展并深入应用,特别是无人机航测技术和地理信息技术的结合应用越来越紧密.本文以不动产调查为例,重点论述结合无人机三维倾斜摄影测量技术在内的测绘地理信息新手段的作业原理及应用情况,并对其优缺点进行总...     

重要性权值的驾车导航POI表达规则

在导航电子地图中,针对驾车任务的POI自适应表达是提高导航电子地图信息传输效率的关键。针对这一问题,提出了基于重要性权值的驾车导航POI表达规则。首先,分析了POI重要性权值的影响因素;其次,从影响因素出发,研究了基于权值的POI规则表达的各规则项;最后,设计了POI自适应表达规则库,并在嵌入了数据处理规则库的绘图软件上进行了技术实现。试验结果表明,利用基于权值的POI表达规则进行地图表达,可以提高地图内容的层次性与清晰性,优化驾车导航服务。     

混合核特征加权SVM遥感湿地空间分类

针对湿地空间信息的复杂性和SVM的分类性能,设计一种基于混合核函数的特征加权SVM分类模型,综合利用多种特征信息,避免被弱相关特征所支配,从而提供更佳的映射性能和泛化能力。实验结果表明,该分类模型兼具良好的外推和内推能力,能够有效地融合不同信息源特征,得到更完整和准确的分类结果,在总体精度、Kappa系数等多项指标上都表现出更高的水平。     

影像与激光雷达数据的城市地物信息提取

针对城市地物信息提取中地物边界难以确定、分类精度不高的问题,该文提出一套综合利用影像及激光雷达点云高程信息的面向对象分类方法。在分割中,各类地物的最佳分割尺度由监督法分割精度评价确定,最终分割结果利用粒度理论下的分割尺度综合方法进行合成,能兼顾不同地物最优分割尺度,获得准确地物边界;在分类中,采用ReliefF特征选择算法度量从影像及点云数据提取的对象特征重要度,选择最佳特征组合,并采用多分类器组合方法进行分类,以消除Hughes现象,提高分类精度。选择德国斯图加特市两块实验区进行分类实验,结果表明:该方法有利于提高大范围城市地物精细信息提取的精度和效率,具有较高的应用价值。     

利用3S技术及三维激光扫描仪进行耕地损毁勘测

利用RS技术发现耕地损毁,网络RTK测量耕地损毁范围及面积,GIS提取耕地被损毁前的分类面积和等别,以及利用网络RTK配合Maptek I-Site8810三维激光扫描仪重构损毁耕地三维模型。