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301.
DAVID RHIND 《The Geographical journal》2000,166(4):295-305
The vast bulk of topographic mapping - the geographical framework used to underpin many activities of the state and of business - has been created and is maintained to national standards. As a consequence, what is available differs greatly from country to country. Yet there is a range of needs for globally-consistent map information. Such needs have long been recognized: the International Map of the World was an attempt to meet them. A multiplicity of factors has constrained the National Mapping Organizations from meeting this aim. However, new technologies have enhanced our capacity to create a geographical framework almost anywhere in the world. Using such technologies, commercial and other non-commercial bodies (e.g. the US military and its counterparts within NATO) have begun to produce wide-area geographical information. This paper describes the nature of the need for global mapping, the players involved and the drivers and obstacles to progress. It proposes a rapid way to enhance the current situation through public/private sector partnerships based on a combination of skills, information assets and resources. If implemented, this could lead to global coverage of a framework derived from 1:25000 scale mapping within about two years. 相似文献
302.
A Review of Shallow-Water Mapping Systems 总被引:1,自引:0,他引:1
In recent years shallow-water mapping systems have been developing rapidly for various applications of coastal, nearshore, and other shallow-water regions. Some systems can operate in only 0.5 m deep water, while the operating range of some systems is over 600 m depth. This article reviews the characteristics of nine shallow-water mapping systems developed by seven manufacturers. The design and the data collection methods of these systems are different from each other, and each has its advantage. Most of these systems have been tested in the real environment and are in use for various purposes around the world. 相似文献
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304.
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。 相似文献
305.
《地学前缘(英文版)》2022,13(5):101425
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. 相似文献