CN110222586A - A kind of calculating of depth of building and the method for building up of urban morphology parameter database - Google Patents
A kind of calculating of depth of building and the method for building up of urban morphology parameter database Download PDFInfo
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Abstract
公开了一种建筑物高度的计算及城市形态参数数据库的建立方法。该方法包括以下步骤:步骤一:采用光谱特征对建筑物遥感图像进行分类并提取阴影信息,使用阴影矢量图来分割太阳投射方向上的直线,以计算出建筑物阴影长度:步骤二:根据阴影长度计算建筑物高度;以及步骤三:对建筑物遥感图像进行图像平滑处理,将处理后的图像进行边缘检测和边缘连接,并去除阴影和植被,随后采用区域识别提取建筑物目标,并对图像进行矢量化以得到建筑物轮廓平面图;通过处理单张卫星遥感图像,并进行建筑物高度和轮廓提取的方法具有操作简单、快捷高效、提取精度相对较高等优势。
A method for calculating the height of a building and establishing a database of urban form parameters is disclosed. The method includes the following steps: Step 1: Use spectral features to classify remote sensing images of buildings and extract shadow information, and use shadow vector graphics to segment the straight line in the direction of the sun's projection to calculate the length of the building's shadow: Step 2: According to the shadow Calculate the height of the building from the length; and step 3: perform image smoothing processing on the remote sensing image of the building, perform edge detection and edge connection on the processed image, and remove shadows and vegetation, and then use area recognition to extract the building target, and the image Vectorization is performed to obtain the outline plan of the building; the method of extracting the height and outline of the building by processing a single satellite remote sensing image has the advantages of simple operation, fast and efficient, and relatively high extraction accuracy.
Description
技术领域technical field
本发明涉及建筑物高度的计算技术领域,特别涉及一种建筑物高度的计算及城市形态参数数据库的建立方法。The invention relates to the technical field of building height calculation, in particular to a building height calculation method and a method for establishing a city form parameter database.
背景技术Background technique
随着遥感技术的快速发展,高分辨率遥感影像能够识别更小的地物目标,地物目标的组织结构也能够更加清晰的反映出来。对于建筑物、道路、水体和绿地等城市重要组成部分,人们通过遥感影像可以更加容易地观察其结构,为三维制图、城市规划等领域的发展提供数据来源。目前对于绿地和水体的遥感图像提取已经比较成熟,而建筑物和道路由于其结构的复杂性其提取难度较大,现有的研究较难实现城市内大批量建筑物高度和轮廓的提取,并构建相应的建筑物三维数字地图。With the rapid development of remote sensing technology, high-resolution remote sensing images can identify smaller objects, and the organizational structure of objects can be more clearly reflected. For important parts of a city such as buildings, roads, water bodies and green spaces, people can more easily observe their structures through remote sensing images, providing data sources for the development of 3D mapping, urban planning and other fields. At present, the extraction of remote sensing images of green spaces and water bodies is relatively mature, while buildings and roads are difficult to extract due to the complexity of their structures. Construct the corresponding three-dimensional digital map of the building.
此外,在气象监测和环境监测等领域,建筑物信息也是十分重要的。与自然界的天然植被不同,在城市中由于道路、建筑物以及人类活动的变化会形成一种新的冠层——城市冠层。在城市冠层中,高层建筑和较高的建筑密度使得地表粗造度增加,道路表面、建筑物墙体和屋顶的反照率与天然植被冠层存在较大差异,这些因素都会影响城市的气象变化。城市冠层对于风速的改变以及城市热岛效应进而会对大气污染物的扩散造成影响,进而改变城市内的空气质量。在气象监测领域,常用的城市尺度气象模型需要高分辨率的城市形态参数作为输入数据,如建筑高度、建筑面积等等。目前美国国家地理建立了描述休斯顿等44个城市的NUDAPT(National Urban Data and Access Portal Tool)城市形态参数数据库,该数据库的建立为城市尺度的WRF模型以及其他城市气象学模型、空气质量模型和气候模型系统提供了高分辨率网格化的城市形态参数作为输入数据。国内对于城市形态参数数据库相关的研究较少,没有能够应用于中国城市的城市气象学模型的城市形态参数数据库。因此,发明一种建筑物高度的计算及城市形态参数数据库的建立方法来解决上述问题很有必要。In addition, in the fields of weather monitoring and environmental monitoring, building information is also very important. Different from the natural vegetation in nature, a new type of canopy, the urban canopy, will be formed in the city due to changes in roads, buildings and human activities. In the urban canopy, high-rise buildings and higher building density increase the surface roughness, and the albedo of road surfaces, building walls and roofs is quite different from the natural vegetation canopy, all of which affect the urban meteorology. Variety. The change of the urban canopy on the wind speed and the urban heat island effect will have an impact on the diffusion of air pollutants, thereby changing the air quality in the city. In the field of meteorological monitoring, commonly used urban-scale meteorological models require high-resolution urban morphological parameters as input data, such as building height, building area, and so on. At present, National Geographic has established a NUDAPT (National Urban Data and Access Portal Tool) urban morphological parameter database describing 44 cities including Houston. The database is established as a city-scale WRF model and other urban meteorological models, air quality models and climate models The model system provides high-resolution gridded urban form parameters as input data. There are few domestic researches related to the urban morphological parameter database, and there is no urban morphological parameter database that can be applied to the urban meteorological model of Chinese cities. Therefore, it is necessary to invent a method for calculating the height of buildings and establishing a database of urban form parameters to solve the above problems.
发明内容SUMMARY OF THE INVENTION
本发明的一个目的在于提供一种建筑物高度的计算及城市形态参数数据库的建立方法,以解决上述背景技术中提出的问题。One object of the present invention is to provide a method for calculating the height of a building and establishing a database of urban morphological parameters, so as to solve the problems raised in the above-mentioned background art.
根据本发明的一个方面,提供了一种建筑物高度的计算及城市形态参数数据库的建立方法,其特征在于,包括以下步骤:According to one aspect of the present invention, there is provided a method for calculating the height of a building and establishing a database of urban morphological parameters, characterized in that it includes the following steps:
步骤一:采用光谱特征对建筑物遥感图像进行分类并提取阴影信息,使用阴影矢量图来分割太阳投射方向上的直线,以计算出建筑物阴影长度:Step 1: Use spectral features to classify remote sensing images of buildings and extract shadow information, and use shadow vector graphics to segment the straight line in the direction of the sun's projection to calculate the shadow length of buildings:
步骤二:根据阴影长度计算建筑物高度;以及Step 2: Calculate the building height based on the shadow length; and
步骤三:对建筑物遥感图像进行图像平滑处理,将处理后的图像进行边缘检测和边缘连接,并去除阴影和植被,随后采用区域识别提取建筑物目标,并对图像进行矢量化以得到建筑物轮廓平面图;Step 3: Perform image smoothing on the remote sensing images of buildings, perform edge detection and edge connection on the processed images, and remove shadows and vegetation, then use area recognition to extract building targets, and vectorize the image to obtain buildings. outline plan;
其中,步骤三还包括将建筑物轮廓平面图和提取的建筑物高度数据进行匹配,以获得城市高分辨率的三维建筑地图。The third step further includes matching the building outline plan with the extracted building height data to obtain a high-resolution three-dimensional building map of the city.
根据一个实施例,所述建筑物高度的计算及城市形态参数数据库的建立方法,还包括对步骤一、步骤二和步骤三中所计算出的建筑物高度进行城市形态参数数据库建立。According to an embodiment, the method for calculating the building height and establishing the urban form parameter database further includes establishing the urban form parameter database for the building heights calculated in the first step, the second step and the third step.
根据一个实施例,其中,步骤一还包括对建筑物遥感图像进行图片增强处理,并且对经图片增强处理后的建筑物遥感图像进行分类,其中,图片增强处理包括采用增强真彩色融合法进行影像融合,对融合影像进行自然色彩变化,并进行线性拉伸、色彩平衡、饱和度、对比度调整。According to an embodiment, the step 1 further includes performing image enhancement processing on the remote sensing image of the building, and classifying the remote sensing image of the building after the image enhancement processing, wherein the image enhancement processing includes using an enhanced true color fusion method to perform image enhancement processing. Fusion, perform natural color changes on the fused image, and perform linear stretching, color balance, saturation, and contrast adjustments.
根据一个实施例,根据阴影长度计算建筑物高度包括:According to one embodiment, calculating the building height based on the shadow length includes:
当太阳和卫星位于建筑物同一侧时,建筑物高度为:When the sun and the satellite are on the same side of the building, the building height is:
并且当太阳和卫星位于建筑物两侧时,建筑物高度为:And when the sun and satellites are on either side of the building, the building height is:
H=L2*tanβ;H=L 2 *tanβ;
其中:L2为建筑物遥感图像可见的阴影高度;Among them: L 2 is the shadow height visible in the remote sensing image of the building;
α为卫星高度角;α is the altitude angle of the satellite;
β为太阳高度角。β is the altitude angle of the sun.
根据一个实施例,对建筑物遥感图像进行图像平滑处理包括对建筑物遥感图像进行直方图均衡化和滤波处理。According to one embodiment, performing image smoothing processing on the remote sensing image of the building includes performing histogram equalization and filtering processing on the remote sensing image of the building.
根据一个实施例,所述建筑物高度的计算及城市形态参数数据库的建立方法,还包括:采用特征量测和区域分割对提取的建筑物目标进行后处理。According to an embodiment, the method for calculating the height of a building and establishing a database of urban morphological parameters further includes: using feature measurement and area segmentation to perform post-processing on the extracted building target.
根据一个实施例,城市形态参数数据库建立包括:基于三维建筑地图,建立1km*1km分辨率的网格,以计算城市形态参数。According to one embodiment, the establishment of the urban form parameter database includes: based on the three-dimensional building map, establishing a grid with a resolution of 1km*1km to calculate the urban form parameter.
根据一个实施例,城市形态参数包括每个网格中的平均建筑高度,并且所述平均建筑高度计算公式如下:According to one embodiment, the urban form parameter includes the average building height in each grid, and the calculation formula of the average building height is as follows:
其中:in:
hi为第i个建筑的建筑高度;hi is the building height of the i-th building;
N是网格区域内所有建筑的数量。N is the number of all buildings in the grid area.
根据一个实施例,所述城市形态参数包括每个网格中的面积加权平均建筑高度,所述面积加权平均建筑高度计算公式如下:According to one embodiment, the urban form parameter includes the area-weighted average building height in each grid, and the formula for calculating the area-weighted average building height is as follows:
其中:in:
Ai为第i个建筑平面面积。Ai is the floor area of the ith building.
根据一个实施例,所述城市形态参数包括每个网格中的建筑高度标准偏差,所述建筑高度标准偏差计算公式如下:According to an embodiment, the urban form parameter includes the standard deviation of building heights in each grid, and the formula for calculating the standard deviation of building heights is as follows:
其中:in:
H为平均建筑高度。H is the average building height.
根据一个实施例,所述城市形态参数包括每个网格中的建筑平面面积分数,所述建筑平面面积分数计算公式如下:According to one embodiment, the urban form parameter includes the building plane area fraction in each grid, and the calculation formula of the building plane area fraction is as follows:
其中:in:
Ap为网格区域内所有建筑平面面积;A p is the plane area of all buildings in the grid area;
AT为网格区域总面积。A T is the total area of the grid area.
根据一个实施例,所述城市形态参数包括每个网格中的建筑表面面积与规划面积比,所述建筑表面面积与规划面积比计算公式如下:According to one embodiment, the urban form parameter includes the ratio of the building surface area to the planned area in each grid, and the formula for calculating the ratio of the building surface area to the planned area is as follows:
其中:in:
AR为网格区域内所有建筑物屋顶面积;A R is the roof area of all buildings in the grid area;
AW为网格区域内所有建筑物非水平平面表面积。A W is the non-horizontal surface area of all buildings in the grid area.
根据一个实施例,所述城市形态参数包括每个网格中的建筑高度分布直方图,所述建筑高度分布直方图的计算方法如下:According to an embodiment, the urban form parameter includes a building height distribution histogram in each grid, and the calculation method of the building height distribution histogram is as follows:
从地表0m开始,以5m组距为间隔设置15个区间,最高75m为止,然后计算出每个网格区域内所有建筑在15个区间的比例分数,从而获取建筑高度分布直方图,其中,大于75m的建筑以75m计算。Starting from 0m on the ground surface, set 15 intervals at intervals of 5m, up to 75m, and then calculate the proportional score of all buildings in each grid area in the 15 intervals, thereby obtaining the building height distribution histogram, among which, greater than A 75m building is calculated as 75m.
本发明的技术效果和优点:Technical effects and advantages of the present invention:
1、本发明通过处理单张卫星遥感图像,并进行建筑物高度和轮廓提取的方法具有操作简单、快捷高效、提取精度相对较高等优势;1. The present invention has the advantages of simple operation, quickness and high efficiency, and relatively high extraction accuracy by processing a single satellite remote sensing image and extracting the height and outline of buildings;
2、本发明通过使用不同的卫星遥感图像可以获得大批量的城市建筑高度数据和建筑物轮廓图,之后将他们想匹配可以构建城市三维建筑地图;2. The present invention can obtain a large number of urban building height data and building outline maps by using different satellite remote sensing images, and then match them to build a three-dimensional urban building map;
本发明基于城市三维建筑地图建立一定分辨率的网格,并且计算各个网格内的城市形态参数,同时建立城市形态参数数据库,该数据库可应用于WRF等其他先进的城市气象模型、空气质量模型和气候模型等系统中。The invention establishes grids with a certain resolution based on the city three-dimensional architectural map, calculates the urban morphological parameters in each grid, and establishes a database of urban morphological parameters, which can be applied to other advanced urban meteorological models and air quality models such as WRF. and climate models.
附图说明Description of drawings
图1为本发明的系统框架图。FIG. 1 is a system frame diagram of the present invention.
图2为本发明步骤一监督分类法提取建筑物阴影案例图。FIG. 2 is a diagram of a case of extracting building shadows by a supervised classification method in step 1 of the present invention.
图3为本发明步骤一建筑物阴影提取后处理案例图。FIG. 3 is a case diagram of post-processing of building shadow extraction in step 1 of the present invention.
图4为本发明步骤二太阳和卫星位于建筑物同一侧情景图。FIG. 4 is a scene diagram of the sun and the satellite being located on the same side of the building in step 2 of the present invention.
图5为本发明步骤二太阳和卫星位于建筑物两侧情景图。FIG. 5 is a scene diagram showing that the sun and satellites are located on both sides of the building in step 2 of the present invention.
图6为本发明步骤三建筑物轮廓信息矢量化提取案例图。FIG. 6 is a case diagram of the vectorized extraction of building outline information in step 3 of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
首先,参考图1,其示出了根据本发明的建筑物高度的计算及城市形态参数数据库的建立方法的框图。本发明将高分辨率的卫星遥感图像进行预处理,并采用光谱特征分类法提取建筑物阴影,消除细小图斑等干扰因素影响并计算建筑物阴影长度;之后结合卫星遥感图像对应的太阳高度角和卫星高度角计算建筑物高度;然后将卫星遥感图像进行边缘化检测、边缘连接、阴影和植被去除、区域识别、图像矢量化等操作获取建筑物轮廓平面图;最后将建筑物轮廓平面图和建筑物高度数据进行匹配,建立三维建筑地图;并且在基于上述三位建筑地图,建立1km*1km分辨率的网格,计算每个网格中的平均建筑高度、面积加权平均建筑高度、建筑高度标准偏差、建筑平面面积分数、建筑表面面积与规划面积比、建筑高度分布直方图等城市形态参数,最后通过计算城市形态参数建立城市形态参数数据库。First, referring to FIG. 1 , it shows a block diagram of a method for calculating the height of a building and establishing a database of urban form parameters according to the present invention. The invention preprocesses high-resolution satellite remote sensing images, adopts spectral feature classification method to extract building shadows, eliminates the influence of interference factors such as small image spots, and calculates building shadow lengths; Calculate the height of the building with the satellite altitude angle; then perform edge detection, edge connection, shadow and vegetation removal, area identification, image vectorization and other operations on the satellite remote sensing image to obtain the building outline plan; The height data is matched to establish a three-dimensional building map; and based on the above three building maps, a grid with a resolution of 1km*1km is established, and the average building height, area-weighted average building height, and building height standard deviation in each grid are calculated. , building plane area fraction, building surface area to planning area ratio, building height distribution histogram and other urban morphological parameters, and finally establish an urban morphological parameter database by calculating urban morphological parameters.
其具体建立方法步骤如下:Its specific establishment method steps are as follows:
步骤一:提取卫星遥感图像内建筑物阴影:Step 1: Extract building shadows in satellite remote sensing images:
将高分辨率的卫星遥感图像进行图片增强处理,采用增强真彩色融合法进行影像融合,对融合影像进行自然色彩变化,形成与地物、色调基本一致的自然色彩图像,并进行线性拉伸、色彩平衡、饱和度、对比度调整。Perform image enhancement processing on high-resolution satellite remote sensing images, use enhanced true color fusion method for image fusion, and perform natural color changes on the fused images to form natural color images that are basically consistent with ground objects and hues, and perform linear stretching, Color balance, saturation, contrast adjustment.
对处理过的卫星遥感图像采用光谱特征分类法进行图像分类,采用监督分类的最大似然分类方法提取阴影信息,以清华大学环境学院及周边的建筑物遥感影像为例,采用上述方法进行处理,结果如图2所示。The processed satellite remote sensing images are classified by spectral feature classification, and the shadow information is extracted by the maximum likelihood classification method of supervised classification. Taking the remote sensing images of the School of Environment of Tsinghua University and surrounding buildings as an example, the above methods are used for processing. The results are shown in Figure 2.
之后,对分类后的图像进行后处理,设定面积阀值消除散落、孤立的细小斑点,结果如图3所示。After that, post-processing is performed on the classified images, and the area threshold is set to eliminate scattered and isolated small spots. The result is shown in Figure 3.
最后将提取的建筑物阴影进行矢量化,使用得到的建筑物阴影矢量图来分割太阳投射方向上的直线,从而计算出建筑物阴影长度。Finally, the extracted building shadows are vectorized, and the obtained building shadow vector graphics are used to segment the straight line in the direction of the sun's projection, so as to calculate the building shadow length.
步骤二:建筑物高度提取:通过阴影长度计算建筑物高度的方法分为两种情景:Step 2: Building height extraction: There are two scenarios for calculating the building height through the shadow length:
(1)太阳和卫星位于建筑物同一侧:(1) The sun and the satellite are on the same side of the building:
如图4所示,卫星遥感图像上可见的阴影部分会被建筑物本身所遮挡,因此卫星遥感图像如图上可见的阴影高度为实际生活中太阳照射建筑物产生的阴影减去卫星所在角度照射建筑物产生的阴影;而上述的阴影长度与建筑物高度、卫星高度角、太阳高度角均存在联系,因此可以测量遥感图像上的阴影长度,查找遥感图像中卫星高度角与太阳高度角的数值,反算出建筑物高度,其具体计算公式如下:As shown in Figure 4, the visible shadow part on the satellite remote sensing image will be occluded by the building itself, so the shadow height visible on the satellite remote sensing image as shown in the figure is the shadow generated by the sun shining on the building in real life minus the angle of the satellite. The shadow generated by the building; the above shadow length is related to the height of the building, the altitude angle of the satellite, and the altitude angle of the sun, so the shadow length on the remote sensing image can be measured, and the value of the satellite altitude angle and the sun altitude angle in the remote sensing image can be found. , inversely calculate the height of the building, and its specific calculation formula is as follows:
卫星遥感图像可见的阴影高度为:The shadow heights visible from satellite remote sensing images are:
建筑物高度为:The building height is:
其中:α为卫星高度角;Where: α is the altitude angle of the satellite;
β为太阳高度角。β is the altitude angle of the sun.
(2)太阳和卫星位于建筑物两侧:(2) The sun and satellites are located on both sides of the building:
如图5所示,卫星遥感图像上可见的阴影部分不会被建筑物本身所遮挡,因此卫星遥感图像上可见的阴影高度与实际生活中太阳照射建筑物产生的阴影长度一致;而上述的建筑物高度与阴影长度和太阳高度角相关,因此可以测量遥感图像上的阴影长度,查找遥感图像中太阳高度角的数值,反算出建筑物高度,其具体计算公式如下:As shown in Figure 5, the shadow part visible on the satellite remote sensing image will not be occluded by the building itself, so the height of the shadow visible on the satellite remote sensing image is consistent with the shadow length produced by the sun irradiating the building in real life; The height of the object is related to the shadow length and the sun height angle, so the shadow length on the remote sensing image can be measured, the value of the sun height angle in the remote sensing image can be found, and the building height can be calculated inversely. The specific calculation formula is as follows:
卫星遥感图像可见的阴影高度为:The shadow heights visible from satellite remote sensing images are:
建筑物高度为:The building height is:
H=L2*tanβ;H=L 2 *tanβ;
其中:α为卫星高度角;Where: α is the altitude angle of the satellite;
β为太阳高度角。β is the altitude angle of the sun.
步骤三:建筑物轮廓信息矢量化提取:Step 3: Vectorized extraction of building outline information:
如图6所示,以清华大学环境学院及周边的建筑物遥感影像为例,将高分辨率的卫星遥感图像进行直方图均衡化和滤波处理对原图像进行图像平滑处理,去除图像噪声和个别孤立点,并设定一个灰度限值,过滤部分干扰因素;As shown in Figure 6, taking the remote sensing image of Tsinghua University School of Environment and surrounding buildings as an example, the high-resolution satellite remote sensing image is subjected to histogram equalization and filtering, and the original image is smoothed to remove image noise and individual Isolated points, and set a grayscale limit to filter some interference factors;
将处理后的图像进行边缘检测和边缘连接,利用归一化植被指数和监督分类法设定合适的阈值对阴影和植被去除;Perform edge detection and edge connection on the processed images, and use normalized vegetation index and supervised classification to set appropriate thresholds to remove shadows and vegetation;
之后采用区域识别提取建筑物目标,采用特征量测和区域分割对提取目标后处理,最后进行图像矢量化可得到建筑物轮廓平面图;Then, the building target is extracted by region recognition, and the extracted target is post-processed by feature measurement and region segmentation, and finally the image vectorization can be used to obtain the building outline plan;
将建筑物轮廓平面图和提取的建筑物高度数据进行匹配,可获得城市高分辨率的三维建筑地图。By matching the building outline plan with the extracted building height data, a high-resolution 3D building map of the city can be obtained.
步骤四:城市形态参数数据库建立:Step 4: Establish the urban form parameter database:
对步骤一、步骤二和步骤三中所计算出的建筑物高度进行城市形态参数数据库建立:Establish the urban form parameter database for the building heights calculated in steps 1, 2 and 3:
基于三维建筑地图,建立1km*1km分辨率的网格,计算城市形态参数,即城市形态参数包括每个网格中的平均建筑高度、面积加权平均建筑高度、建筑高度标准偏差、建筑平面面积分数、建筑表面面积与规划面积比、建筑高度分布直方图的相关参数,最后通过计算城市形态参数建立城市形态参数数据库,并且城市形态参数的计算方法如下:Based on the 3D building map, establish a grid with a resolution of 1km*1km, and calculate the urban form parameters, that is, the urban form parameters include the average building height, area-weighted average building height, standard deviation of building height, and building plane area fraction in each grid. , the ratio of the building surface area to the planned area, the relevant parameters of the building height distribution histogram, and finally establish the urban form parameter database by calculating the urban form parameters, and the calculation method of the urban form parameters is as follows:
所述平均建筑高度计算公式如下:The formula for calculating the average building height is as follows:
其中:in:
hi为第i个建筑的建筑高度;hi is the building height of the i-th building;
N是网格区域内所有建筑的数量;N is the number of all buildings in the grid area;
所述面积加权平均建筑高度计算公式如下:The formula for calculating the area-weighted average building height is as follows:
其中:in:
Ai为第i个建筑平面面积;Ai is the floor area of the i-th building;
所述建筑高度标准偏差计算公式如下:The formula for calculating the standard deviation of the building height is as follows:
其中:in:
H为平均建筑高度;H is the average building height;
所述建筑平面面积分数计算公式如下:The formula for calculating the floor area fraction of the building is as follows:
其中:in:
Ap为网格区域内所有建筑平面面积;A p is the plane area of all buildings in the grid area;
AT为网格区域总面积;A T is the total area of the grid area;
所述建筑表面面积与规划面积比计算公式如下:The formula for calculating the ratio of the building surface area to the planned area is as follows:
其中:in:
AR为网格区域内所有建筑物屋顶面积;A R is the roof area of all buildings in the grid area;
AW为网格区域内所有建筑物非水平平面表面积(如墙体等);A W is the non-horizontal surface area of all buildings in the grid area (such as walls, etc.);
所述建筑高度分布直方图的计算方法如下:The calculation method of the building height distribution histogram is as follows:
从地表0m开始,以5m组距为间隔设置15个区间,最高75m为止,然后计算出每个网格区域内所有建筑在15个区间的比例分数,从而获取建筑高度分布直方图;Starting from 0m on the ground surface, set 15 intervals at 5m intervals, up to 75m, and then calculate the proportional score of all buildings in each grid area in the 15 intervals, thereby obtaining the building height distribution histogram;
其中,大于75m的建筑以75m计算。Among them, buildings larger than 75m are calculated as 75m.
根据另一个实施例,本发明的建筑物高度的计算方法不仅仅局限于对建筑物进行计算,同样也适用于山脉上的各山峰高度的计算和各悬崖断壁处的计算,在此处就不一一的进行详细阐述。According to another embodiment, the method for calculating the height of a building of the present invention is not only limited to the calculation of buildings, but is also applicable to the calculation of the height of each peak on a mountain range and the calculation of the broken walls of each cliff. Not elaborated one by one.
最后应说明的是:以上所述仅为本发明的优选实施例而已,并不用于限制本发明,尽管参照前述实施例对本发明进行了详细的说明,对于本领域的技术人员来说,其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换,凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。Finally, it should be noted that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, the The technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be equivalently replaced, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included. within the protection scope of the present invention.
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Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112132729A (en) * | 2020-10-19 | 2020-12-25 | 林文旭 | Intelligent city planning system |
CN112149594A (en) * | 2020-09-29 | 2020-12-29 | 同济大学 | Urban construction evaluation method based on deep learning and high-resolution satellite imagery |
CN112164142A (en) * | 2020-10-21 | 2021-01-01 | 江苏科技大学 | Building lighting simulation method based on smart phone |
CN112883796A (en) * | 2021-01-19 | 2021-06-01 | 中国地质大学(武汉) | SAR image multi-type building height estimation method based on overlapping and masking information |
CN113033484A (en) * | 2021-04-21 | 2021-06-25 | 河北工程大学 | Urban classification method for unmanned aerial vehicle emergency network deployment |
CN114187524A (en) * | 2022-02-16 | 2022-03-15 | 中国科学院地理科学与资源研究所 | Household air conditioner identification method, device, equipment, storage medium and product |
CN114743095A (en) * | 2022-03-19 | 2022-07-12 | 同济大学 | An intelligent identification method of building height based on virtual remote sensing images |
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101996283A (en) * | 2010-11-26 | 2011-03-30 | 上海市浦东新区气象局 | Dynamic forecasting method for street tree city block wind disaster |
CN103729853A (en) * | 2014-01-15 | 2014-04-16 | 武汉大学 | Three-dimensional GIS assisted high-resolution remote sensing image building collapse-damage detecting method |
CN104463970A (en) * | 2014-12-24 | 2015-03-25 | 中国科学院地理科学与资源研究所 | Method for determining three-dimensional gravity center of city based on remote-sensing image and application thereof |
CN107527038A (en) * | 2017-08-31 | 2017-12-29 | 复旦大学 | A kind of three-dimensional atural object automatically extracts and scene reconstruction method |
CN107679441A (en) * | 2017-02-14 | 2018-02-09 | 郑州大学 | Method based on multi-temporal remote sensing image shadow extraction City Building height |
-
2019
- 2019-05-15 CN CN201910400603.3A patent/CN110222586A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101996283A (en) * | 2010-11-26 | 2011-03-30 | 上海市浦东新区气象局 | Dynamic forecasting method for street tree city block wind disaster |
CN103729853A (en) * | 2014-01-15 | 2014-04-16 | 武汉大学 | Three-dimensional GIS assisted high-resolution remote sensing image building collapse-damage detecting method |
CN104463970A (en) * | 2014-12-24 | 2015-03-25 | 中国科学院地理科学与资源研究所 | Method for determining three-dimensional gravity center of city based on remote-sensing image and application thereof |
CN107679441A (en) * | 2017-02-14 | 2018-02-09 | 郑州大学 | Method based on multi-temporal remote sensing image shadow extraction City Building height |
CN107527038A (en) * | 2017-08-31 | 2017-12-29 | 复旦大学 | A kind of three-dimensional atural object automatically extracts and scene reconstruction method |
Non-Patent Citations (2)
Title |
---|
孙自永等: "《青藏高原矿产资源开发的地质环境承载力评价方法研究》", 31 December 2016 * |
葛珊珊: "基于Urban DEM的城市三维形态研究——以南京老城区为例", 《万方在线》 * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112149594A (en) * | 2020-09-29 | 2020-12-29 | 同济大学 | Urban construction evaluation method based on deep learning and high-resolution satellite imagery |
CN112132729A (en) * | 2020-10-19 | 2020-12-25 | 林文旭 | Intelligent city planning system |
CN112164142A (en) * | 2020-10-21 | 2021-01-01 | 江苏科技大学 | Building lighting simulation method based on smart phone |
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CN113033484A (en) * | 2021-04-21 | 2021-06-25 | 河北工程大学 | Urban classification method for unmanned aerial vehicle emergency network deployment |
CN113033484B (en) * | 2021-04-21 | 2022-11-22 | 河北工程大学 | Urban classification method for unmanned aerial vehicle emergency network deployment |
CN114187524A (en) * | 2022-02-16 | 2022-03-15 | 中国科学院地理科学与资源研究所 | Household air conditioner identification method, device, equipment, storage medium and product |
CN114187524B (en) * | 2022-02-16 | 2022-06-10 | 中国科学院地理科学与资源研究所 | Household air conditioner identification method, device, equipment, storage medium and product |
CN114743095A (en) * | 2022-03-19 | 2022-07-12 | 同济大学 | An intelligent identification method of building height based on virtual remote sensing images |
CN116246391A (en) * | 2023-03-20 | 2023-06-09 | 广东便捷神科技股份有限公司 | A goods adjustment optimization method between vending machine stations |
CN116977469A (en) * | 2023-08-02 | 2023-10-31 | 中国水利水电科学研究院 | A batch generation method of community-scale urban morphology data based on random slicing |
CN116977469B (en) * | 2023-08-02 | 2024-01-23 | 中国水利水电科学研究院 | A batch generation method of community-scale urban morphology data based on random slicing |
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