The urban boundary-layer field campaign in marseille (ubl/clu-escompte): set-up and first results

The UBL/CLU (urban boundary layer/couche limite urbaine) observation and modelling campaign is a side-project of the regional photochemistry campaign ESCOMPTE. UBL/CLU focuses on the dynamics and thermodynamics of the urban boundary layer of Marseille, on the Mediterranean coast of France. The objective of UBL/CLU is to document the four-dimensional structure of the urban boundary layer and its relation to the heat and moisture exchanges between the urban canopy and the atmosphere during periods of low wind conditions, from June 4 to July 16, 2001. The project took advantage of the comprehensive observational set-up of the ESCOMPTE campaign over the Berre–Marseille area, especially the ground-based remote sensing, airborne measurements, and the intensive documentation of the regional meteorology. Additional instrumentation was installed as part of UBL/CLU. Analysis objectives focus on (i) validation of several energy balance computational schemes such as LUMPS, TEB and SM2-U, (ii) ground truth and urban canopy signatures suitable for the estimation of urban albedos and aerodynamic surface temperatures from satellite data, (iii) high resolution mapping of urban land cover, land-use and aerodynamic parameters used in UBL models, and (iv) testing the ability of high resolution atmospheric models to simulate the structure of the UBL during land and sea breezes, and the related transport and diffusion of pollutants over different districts of the city. This paper presents initial results from such analyses and details of the overall experimental set-up.     

Effect of different tropospheric mapping functions on the TRF, CRF and position time-series estimated from VLBI

This paper compares estimated terrestrial reference frames (TRF) and celestial reference frames (CRF) as well as position time-series in terms of systematic differences, scale, annual signals and station position repeatabilities using four different tropospheric mapping functions (MF): The NMF (Niell Mapping Function) and the recently developed GMF (Global Mapping Function) consist of easy-to-handle stand-alone formulae, whereas the IMF (Isobaric Mapping Function) and the VMF1 (Vienna Mapping Function 1) are determined from numerical weather models. All computations were performed at the Deutsches Geodätisches Forschungsinstitut (DGFI) using the OCCAM 6.1 and DOGS-CS software packages for Very Long Baseline Interferometry (VLBI) data from 1984 until 2005. While it turned out that CRF estimates only slightly depend on the MF used, showing small systematic effects up to 0.025 mas, some station heights of the computed TRF change by up to 13 mm. The best agreement was achieved for the VMF1 and GMF results concerning the TRFs, and for the VMF1 and IMF results concerning scale variations and position time-series. The amplitudes of the annual periodical signals in the time-series of estimated heights differ by up to 5 mm. The best precision in terms of station height repeatability is found for the VMF1, which is 5–7% better than for the other MFs.     

加快科技进步和产业结构调整开启测绘事业发展的新篇章

2000年,随着国家测绘局基础设施建设项目的完成并投入生产,标志着模拟测图时代的结束,测绘行业进入了数字化测绘的时代。从2000到2006年,测绘系统利用现代化的测图系统(主要包括影像扫描仪、PAT-B空中三角测量系统、磁盘阵列、服务器、网络系统、VirtuoZo与JX-4全数字摄影测量工作站等)陆续完成了包括大地水准面精化、全国骨干交通网数据采集、全国1:25万比例尺地形图数据库建设、1:5万比例尺地形图数据库建设等大型基础测绘建设项目,为国民经济建设与社会发展提供了有力的保障。社会的发展及民众的需求,向测绘行业提出了更高的要求,而技术的进步,则为解决这个问题提供了可能。     

测图精灵在大比例尺校园平面图测绘中的应用

首先,介绍了测图精灵的的特点和功能;然后,结合河南农业职业学院校园平面图的测绘实践,总结了利用测图精灵测绘校园平面图的方法步骤;最后,提出了测图过程中出现的问题并指出了解决的办法。     

内外业一体化技术在江都市龙川广场施工放线测量中的应用

应用内外业一体化技术，对龙川广场整个建筑物特征点进行了施工放线测量，以实例说明该方法是实用可行的。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.