Automatic change detection in lane-level road networks using GPS trajectories

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%.     

Landscape adaptation to climate change: Local networks,social learning and co-creation processes for adaptive planning

The development of legitimate, operative, and feasible landscape adaptation planning for climate change is dependent on the specific characteristics of the landscape and its inhabitants. Spatial patterns, culture, governance systems, socio-economic structures, planning methods, history, and collectively envisioned futures need to be accommodated. The literature suggests that landscape is a complex and dynamic socio-ecological system, the management and adaptation of which requires systemic and integrative approaches to respond to a wide variety of drivers of change, challenges, and interests. Based on activities developed in 15 European pilot landscapes, we identify some of the key factors and conditions affecting the generation of representative local networks for landscape adaptation to climate change. We illustrate how social learning and co-creation processes can be implemented in them and how their co-produced outcomes can help local communities overcome barriers and address critical issues in adaptive planning. Our results provide a framework for the creation of similar networks in other landscapes, exploring at the same time the interactions between the composition of networks, social learning, and the quality of the co-produced outputs as a fundamental step for the development of Landscape Adaptation Plans to Climate Change.     

纳入空间自相关的FLUS模型在土地利用变化多情景模拟中的应用

FLUS模型是一种新型的土地利用变化模拟模型,应用前景广阔。本文通过在FLUS模型的人工神经网络(Artificial Neural Network,ANN)训练模块中引入空间自相关因子来改进模型,以珠江三角洲地区为例,基于2009年、2015年土地利用数据和一系列驱动因子对改进的模型进行了验证,并利用该改进的FLUS模型模拟了2035年研究区在3种情景下土地利用变化格局。结果表明:①引入空间自相关因子后各地类发生概率分布的预测精度更高,耕地、林地、建设用地、水体和未利用土地的拟合优度ROC值分别从0.819、0.928、0.885、0.855和0.861提高到0.857、0.934、0.890、0.863和0.978;②改进的FLUS模型的模拟精度有一定的提高,Kappa系数从0.732提高到0.744,FOM系数从0.077升到0.106;③情景模拟表明,3种情景下珠江三角洲建设用地和林地均将增加、而耕地均呈减少趋势。但不同情景下模拟的土地利用格局也存在显著差异:基准情景下,建设用地明显扩张且大幅侵占耕地。耕地保护情景下,耕地面积保持在合理水平,建设用地蔓延扩张趋势得到遏制,土地利用布局总体趋向合理。生态保护情景下,耕地、林地和水体得到较好保护,建设用地布局更为合理,土地利用可持续性明显提高。     

Using global node-based velocity in random walk particle tracking in variably saturated porous media: application to contaminant leaching from road constructions

Precise and efficient numerical simulation of transport processes in subsurface systems is a prerequisite for many site investigation or remediation studies. Random walk particle tracking (RWPT) methods have been introduced in the past to overcome numerical difficulties when simulating propagation processes in porous media such as advection-dominated mass transport. Crucial for the precision of RWPT methods is the accuracy of the numerically calculated ground water velocity field. In this paper, a global node-based method for velocity calculation is used, which was originally proposed by Yeh (Water Resour Res 7:1216–1225, 1981). This method is improved in three ways: (1) extension to unstructured grids, (2) significant enhancement of computational efficiency, and (3) extension to saturated (groundwater) as well as unsaturated systems (soil water). The novel RWPT method is tested with numerical benchmark examples from the literature and used in two field scale applications of contaminant transport in saturated and unsaturated ground water. To evaluate advective transport of the model, the accuracy of the velocity field is demonstrated by comparing several published results of particle pathlines or streamlines. Given the chosen test problem, the global node-based velocity estimation is found to be as accurate as the CK method (Cordes and Kinzelbach in Water Resour Res 28(11):2903–2911, 1992) but less accurate than the mixed or mixed-hybrid finite element methods for flow in highly heterogeneous media. To evaluate advective–diffusive transport, a transport problem studied by Hassan and Mohamed (J Hydrol 275(3–4):242–260, 2003) is investigated here and evaluated using different numbers of particles. The results indicate that the number of particles required for the given problem is decreased using the proposed method by about two orders of magnitude without losing accuracy of the concentration contours as compared to the published numbers.     

顾及尺度变化和数据更新的道路网匹配算法

道路网数据匹配是地理空间数据库进行变化探测和数据更新的重要前提,不同比例尺下的道路网之间的匹配是一个非常重要的部分。本文总结和分析了道路网匹配的已有算法,针对不同比例尺道路网之间的匹配可能存在的问题和难点,设计了一个融合多种匹配技术的算法。在考虑不同比例尺下道路网数据的特点基础上,改进了空间场景结构的评价方法;分析了stroke匹配算法在不同比例尺道路网数据下的局限性,提出了一种可针对不同比例尺下道路数据存在变化与更新的stroke部分匹配算法。试验表明,文中所提出的方法能够适应不同比例尺下道路网的匹配,匹配效果较好,运行效率较高。     

基于直线特征检测的道路边线自动提取方法

针对如何从车载激光点云数据中快速、准确地提取道路边线的问题,本文提出一种基于直线特征检测的道路边线自动提取方法。首先对原始点云进行地面滤波,删除非地面点,获取包含道路信息的地面点云,接着把点云投影到二维图像上,根据反射强度获得平均强度图像,对平均强度图像进行LSD直线检测,获得道路边线的直线段,然后进行直线连接,把检测出来的短线段连接成长直线,最后根据直线特征提取出道路边线,并且利用定量指标对提取结果进行定量分析。实验证明,该方法提取的道路边线具有较高的准确率和完整性。     

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4–5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R²) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha⁻¹)): C_VH, C_HV, or the combined used of X_HH and C_HH, C_HH and C_HV, or green reflectance and C_HH. Nevertheless, the best accurate forecast (R² = 0.76 and RMSE = 7.0 q ha⁻¹) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (C_VV and C_VH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat.     

道路网约束下的城市兴趣点选取

针对道路对城市内部兴趣点数据分布有密切的约束关系,故城市内POI数据往往呈现出沿着道路分布的特征这一现象,提出一种在道路网约束下城市内部POI数据的选取算法。通过建立道路网的拓扑结构,根据POI数据的分布位置分为道路周边分布的和远离道路分布的两大类,对道路周边的点使用"间苗法"选取,对远离道路分布的点根据其分布模式决定采用"随机法"或其他方法选取。算法采用实际数据进行实验,切实可行,并得到了合理的结果。     

适用于大河流域的栅格DEM洼地填充算法

针对地势平坦且空间范围较大的流域水系自动提取任务中,采用常规的洼地填充算法难以提取出完整水系,形成大量"断头河"的问题,提出了一种能够导出整个数字高程模型(DEM)所有像元被淹没的次序表的方法,由该次序表构成的矩阵代替原有DEM来实现流向的计算,进而提取出累积流向及分级河道。经用覆盖中心流域的高精度DEM以及覆盖黄河和长江等大型流域的DEM来测试,结果表明:用其他算法提取结果均发生了"断头河"错误,而该方法则能提取出完整的水系。这种方法能正确实施洼地填充和像元填平处理,可以适应于任意规模和精度的DEM填洼问题,具有较强的鲁棒性,克服了以往算法难以处理大河流域DEM洼地填充的不足。     

基于地理实体的道路网络面向对象数据模型研究

城市交通迅速发展,道路网络呈现出空间结构复杂、数据量大、时效性强等特点。本文讨论了传统路网模型的特点,顾及道路的信息完整性、数据冗余性、管理高效性3个指标,研究了以道路实体为基本元素的路网模型;运用面向对象技术对路网模型进行了数据建模,并通过动态交通信息更新机制对路网的动态属性进行管理;构建了城市道路实体化实验系统,验证了该模型的有效性和可行性。