Digital Elevation Models (DEMs) are constructed using altitude point data and various interpolation techniques. The quality and accuracy of DEMs depend on data point density and the interpolation technique used. Usual...Digital Elevation Models (DEMs) are constructed using altitude point data and various interpolation techniques. The quality and accuracy of DEMs depend on data point density and the interpolation technique used. Usually however, altitude point data especially in plain areas do not provide realistic DEMs, mainly due to errors produced as a result of the interpolation technique, resulting in imprecise topographic representation of the landscape. Such inconsistencies, which are mainly in the form of surface depressions, are especially crucial when DEMs are used as input to hydrologic modeling for impact studies, as they have a negative impact on the model’s performance. This study presents a Geographical Information System (GIS) tool, named LAN (Line Attribute Network), for the improvement of DEM construction techniques and their spatial accuracy, using drainage network attributes. The developed tool does not alter the interpolation technique, but provides higher point density in areas where most DEM problems occur, such as lowland areas or places where artificial topographic features exist. Application of the LAN tool in two test sites showed that it provides considerable DEM improvement.展开更多
A new geometric modeling approach is introduced in this paper.First the principle of modeling of 3D pipe network is discussed in detail.Then the procedures of implementing pipe network visualization and system functio...A new geometric modeling approach is introduced in this paper.First the principle of modeling of 3D pipe network is discussed in detail.Then the procedures of implementing pipe network visualization and system functions are presented.Last,several efficient methods for speeding up display of graphics are introduced.The new geometric modeling approach offers to people a new way to solve 3D visualization of complex urban pipe network.展开更多
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probabi...Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.展开更多
Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering stru...Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.展开更多
语义图像检索为填补图像低层视觉特征和用户高层语义之间的鸿沟而产生,图像语义描述和提取是其关键。提出了一种基于G IS语义的遥感图像检索(G IS sem antics-based remote sensing im age retrieval,简称G ISSB IR)方法,主要涉及空间...语义图像检索为填补图像低层视觉特征和用户高层语义之间的鸿沟而产生,图像语义描述和提取是其关键。提出了一种基于G IS语义的遥感图像检索(G IS sem antics-based remote sensing im age retrieval,简称G ISSB IR)方法,主要涉及空间对象的语义表达和语义匹配两方面内容。利用面向对象G IS语义模型和概念语义网络共同表达空间对象的语义,设计了语义调解器处理用户与系统之间的语义不一致。通过对G IS原子查询结果进行布尔运算得到矢量查询结果,在此基础上得到与G IS数据具有统一坐标框架的遥感图像检索结果。实验结果表明G ISSB IR方法是有效的。展开更多
文摘Digital Elevation Models (DEMs) are constructed using altitude point data and various interpolation techniques. The quality and accuracy of DEMs depend on data point density and the interpolation technique used. Usually however, altitude point data especially in plain areas do not provide realistic DEMs, mainly due to errors produced as a result of the interpolation technique, resulting in imprecise topographic representation of the landscape. Such inconsistencies, which are mainly in the form of surface depressions, are especially crucial when DEMs are used as input to hydrologic modeling for impact studies, as they have a negative impact on the model’s performance. This study presents a Geographical Information System (GIS) tool, named LAN (Line Attribute Network), for the improvement of DEM construction techniques and their spatial accuracy, using drainage network attributes. The developed tool does not alter the interpolation technique, but provides higher point density in areas where most DEM problems occur, such as lowland areas or places where artificial topographic features exist. Application of the LAN tool in two test sites showed that it provides considerable DEM improvement.
文摘A new geometric modeling approach is introduced in this paper.First the principle of modeling of 3D pipe network is discussed in detail.Then the procedures of implementing pipe network visualization and system functions are presented.Last,several efficient methods for speeding up display of graphics are introduced.The new geometric modeling approach offers to people a new way to solve 3D visualization of complex urban pipe network.
基金National Natural Science Foundation of China, No.49971066.
文摘Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.
文摘Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.
文摘语义图像检索为填补图像低层视觉特征和用户高层语义之间的鸿沟而产生,图像语义描述和提取是其关键。提出了一种基于G IS语义的遥感图像检索(G IS sem antics-based remote sensing im age retrieval,简称G ISSB IR)方法,主要涉及空间对象的语义表达和语义匹配两方面内容。利用面向对象G IS语义模型和概念语义网络共同表达空间对象的语义,设计了语义调解器处理用户与系统之间的语义不一致。通过对G IS原子查询结果进行布尔运算得到矢量查询结果,在此基础上得到与G IS数据具有统一坐标框架的遥感图像检索结果。实验结果表明G ISSB IR方法是有效的。
