CN113313418A - Urban building roof greening suitability evaluation grading method - Google Patents
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Abstract
The invention discloses a method for evaluating and grading the roof greening suitability of urban buildings, which comprises the following steps: acquiring a high-resolution remote sensing image, acquiring urban space element data through high-resolution remote sensing, and then constructing an urban building roof greening adaptive index system based on the urban space element data; then, quantitatively calculating a building roof greening adaptive index system of the urban building to construct a building roof index information table; and finally, constructing a rule tree formed by connecting root nodes, child nodes and leaf nodes based on the building roof index information table, and finishing greening suitability evaluation and grading on the roof of each city building based on the rule tree. The method effectively improves the accuracy and intelligent level of excavation of the urban building roof greening adaptability characteristics, and lays an important foundation for compiling and implementing special urban three-dimensional greening plans.
Description
Technical Field
The invention relates to the field of urban planning and urban greening, in particular to a method for evaluating and grading the roof greening suitability of urban buildings.
Background
Reasonably evaluating the roof greening suitability of a building is an important basis for developing green roof planning on a city scale, most of the building roof greening suitability evaluation methods in the existing research are analyzed from a single building or a block scale, for example, 19 widely-applied indexes for evaluating the roof greening potential are systematically analyzed by Silva, and the roof type, the green roof type, the number of layers, the sunlight direction and the building function are found to be the most commonly used indexes of the building scale; respectively selecting the roof direction, the building height, the roof gradient, the bearing capacity, the building function and the like as the evaluation indexes of the greening suitability of the building roof by Shao-Natural and Wang Xinjun and the like; wong and Lau analyze two variables of sunshine condition and roof structure through focus group discussion and a three-dimensional simulation model, manually identify roof equipment through Google images, and perform preliminary research on green roof transformation potential of Chinese hong Kong Wang corner small walking block; wilkinson and Reed use Australian Melbourne Central Business district as the subject, have selected building position, roof direction, roof height, roof slope, roof bearing capacity, plant, eight evaluation indexes of building sustainability and maintenance level. In the city scale, Grunwald and the like utilize building vector data and a digital elevation model, and the building suitability of the green roof in Braun-Schweige, Germany is evaluated by calculating the slope and the building suitability area of the roof. Zhou et al evaluated the current building rooftop that can be converted to green rooftop in beijing five rings from two perspectives, building age and rooftop structure, using ZY-3 satellite images.
The index system of the building and block scale is usually detailed and comprehensive, but is accompanied by the problems of partial overlapping between indexes, large data collection and processing amount and the like, and the evaluation index of the city scale is too simple and rough. How to construct an index system with roof greening suitability, which has relatively complete index number and reliable and easily obtained index calculation, by using high-resolution remote sensing data in an urban scale, and according to the principle of scientificity, hierarchy, easy operation and popularization, objective and hierarchical quantitative evaluation indexes are adopted, the evaluation fineness is improved, the operability is enhanced, and the problem to be solved at present is solved urgently.
Disclosure of Invention
The invention aims to provide a method for evaluating and grading the roof greening suitability of urban buildings, which is characterized by acquiring a roof image through high-resolution remote sensing, extracting various factors influencing greening, carrying out evaluation analysis by combining comprehensive consideration of the factors, confirming the roof greening suitability of various houses and giving out evaluation of suitability or unsuitability, thereby effectively improving the precision and intelligent level of excavation of the roof greening suitability characteristics of the buildings.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a method for evaluating and grading the roof greening suitability of an urban building, which comprises the following steps:
acquiring a high-resolution remote sensing image, and acquiring urban space element data through the high-resolution remote sensing image;
constructing a roof greening adaptive index system of the urban building based on the urban space element data;
carrying out quantitative calculation on the urban building roof greening adaptive index system to construct a building roof index information table;
and finishing the evaluation and grading of the urban building roof greening suitability based on the building roof index information table.
Preferably, the specific method for acquiring the urban space element data is as follows:
extracting a control network of the urban scene partition based on the high-resolution remote sensing image, and dividing the high-resolution remote sensing image into a plurality of independent task blocks based on the control network;
merging the types of the buildings according to the visual characteristic difference reflected by each task block in the high-resolution remote sensing image, classifying and extracting patch objects from each task block by adopting a depth network extraction model, performing incremental learning on the depth network extraction model through the patch objects, and iterating to the next classified extraction; the blob object is used to represent the urban space element data.
Preferably, the control network of the urban scene partition comprises a road network of a city and a water system network of a water surface.
Preferably, the method for incrementally learning the deep network extraction model comprises:
and supplementing the extracted plaque object into training sample data of the deep network extraction model, finishing updating the training sample data, and finishing incremental learning of the deep network extraction model through the updated training sample data.
Preferably, the urban building roof greening adaptive index system is constructed from two layers of building body and roof attribute.
Preferably, the index of the building body comprises building function and building height; the index of the roof property comprises the roof area, the roof material and the roof gradient.
Preferably, the building function and building height information is acquired by a space connection tool of ArcGIS; the roof area, the roof material and the roof gradient are obtained by a high-resolution remote sensing information extraction method.
Preferably, the evaluation and grading of the building roof greening suitability are completed by constructing a classification rule tree.
Preferably, the classification rule tree is formed by connecting root nodes, child nodes and leaf nodes.
The invention discloses the following technical effects:
the invention provides a method for evaluating and grading urban building roof greening suitability, which comprises the steps of extracting visual feature differences in high-resolution remote sensing images into urban space elements by adopting a depth network model in the intelligent extraction process of the high-resolution remote sensing images, constructing the urban space elements into an urban building roof greening suitability index system, carrying out quantitative calculation to obtain building roof index information, and finishing evaluation and grading of the urban building roof greening suitability based on the building roof index information, thereby effectively improving the accuracy and intelligent level of excavation of the building roof greening suitability features and laying an important foundation for compiling and implementing special urban three-dimensional greening planning.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for assessing and grading the suitability of urban building roof greening in an embodiment of the present invention;
FIG. 2 is a diagram illustrating the evaluation and analysis of the suitability of the roof greening of a building based on a regular tree according to an embodiment of the present invention;
FIG. 3 is a schematic view of a building rooftop layout of a mansion island according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating the evaluation and analysis of the building roof greening suitability of the mansion island in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the invention provides a method for assessing and grading the roof greening suitability of an urban building, which comprises the following steps:
the first step is as follows: the method comprises the following steps of rapidly and accurately extracting urban space element data: acquiring a high-resolution remote sensing image, semi-automatically extracting a road network of a city based on basic data such as the road network of the high-resolution remote sensing image and navigation data, acquiring a water system network by a water surface extraction and self-adaptive correction method, forming a control network of an urban scene partition after superposing the high-resolution remote sensing image and the navigation data, and dividing the space in the high-resolution remote sensing image into a plurality of independent task blocks according to the control network; in the matrix of each subarea block, the types of the inlaid buildings are complex and various, after the types of the buildings are merged according to the visual characteristic difference reflected in the high-resolution remote sensing image, a depth network extraction model is adopted, patch objects are extracted from the blocks in a classified mode, and urban space element data are represented by the patch objects; and respectively selecting areas with high confidence coefficient and low confidence coefficient from the prediction results of the building classification extraction, supplementing the areas into training sample data of the deep network extraction model, completing the updating of the training sample data, completing incremental learning of the deep network extraction model through the updated training sample data, and further iterating to the next classification extraction to gradually make the building plaque extraction result become optimal and stable.
The second step is that: the method comprises the following steps of (1) constructing a roof greening constructability index system of the urban building: on the basis of developing the accurate extraction of urban building patches, an urban building roof greening adaptive index system is constructed from two layers of 'building body-roof attribute' on the basis of the principles of scientificity, popularization and easy operation. The indexes of the building body include: building function, building height; the indices of "roof properties" include: roof area, roof material, roof slope. The method comprises the steps of obtaining information such as building height and building functions by using an ArcGIS space connection tool, obtaining information such as roof area, roof material and roof gradient by using a high-resolution remote sensing information extraction technology, and forming a building roof index information table shown in table 1.
TABLE 1
The third step: building roof greening suitability evaluation grading based on regular trees: with the building roof greening suitability evaluation grading as a target, a classification rule tree starting from a root node and reaching leaf nodes through child nodes is constructed, and the building roof is given with the scores of 'unsuitable construction', 'suitable construction' and 'extremely suitable construction'. Firstly, inputting a building roof greening suitability index set serving as an original data set from a root node; and then sequentially matching the rules of the roof material, the roof gradient, the building height and 4 sub-nodes of the building function until the leaf nodes meet the stop condition and making decision judgment of 'unsuitable construction', 'suitable construction' or 'extremely suitable construction'. And (3) completing greening suitability evaluation and grading of each building roof based on the rule tree, as shown in figure 2. The method comprises the following specific steps:
1. according to different roof materials, if the roof is made of special materials such as glass, the roof is not suitable for construction; if the roof is made of common materials such as concrete, steel and the like, the roof is 'suitable for construction';
2. based on a building roof which is made of roof materials and is suitable for construction, if the slope of the roof is more than 30 degrees, the roof is not suitable for construction; if the slope of the roof is greater than 0 degree and less than or equal to 30 degrees, the roof is 'suitable for construction'; if the slope of the roof is 0 degree, the roof is extremely suitable for construction;
3. based on the building roof with the roof slope as 'suitable' and 'extremely suitable', if the building height is more than 50 meters, the roof is 'unsuitable'; if the building height is more than 24 meters and less than or equal to 50 meters, the roof is 'suitable for construction'; if the building height is less than or equal to 24 meters, the roof is extremely suitable for construction;
4. the roof is extremely suitable for building if the building functions are public building (commercial, office, public facilities and the like), factory buildings (industrial buildings) and the like on the basis of the building roof with the roof height of the "suitable building" and the "extremely suitable building"; if the building functions as a house, the roof is "built-in".
5. In the judgment path of 'roof slope-building height-building function', if the 'extremely suitable construction' condition is met twice or more, the roof is 'extremely suitable construction'; otherwise, the roof is 'suitable for construction'.
For a better understanding of the present invention, the following examples are given to illustrate the present model in further detail:
and evaluating the building roof greening suitability of the building of the mansion gate island according to the urban building roof greening suitability evaluation grading method.
The first step is as follows: the method comprises the following steps of rapidly and accurately extracting urban space element data of Fujian mansion islands: based on high-resolution remote sensing images, the road network of the city is semi-automatically extracted by referring to the basic data such as the road network of navigation data and the like, and then the road network is filtered by waterA water system network is obtained by a method of surface extraction and self-adaptive correction, the water system network and the water system network are overlapped to form a control network of an urban scene partition, and the space of a high-resolution remote sensing image is divided into a plurality of independent task blocks; in the matrix of each partitioned block, merging the types of buildings according to the visual characteristic difference reflected in the high-resolution remote sensing image, extracting a model by adopting a depth network, classifying and extracting patch objects from the blocks, and expressing urban space element data by the patch objects; and respectively selecting areas with high confidence coefficient and low confidence coefficient from the prediction result of the building classification extraction, supplementing the areas into training sample data of the deep network extraction model, completing the updating of the training sample data, completing incremental learning of the deep network extraction model through the updated training sample data, and further iterating to the next classification extraction. Finally, the total roof number of the building in the mansion island in 2017 is 2421.65 km2(Simming district 1121.36 km21300.29 km in a lake region2) About 17.1% of the area of the island, as shown in FIG. 3.
The second step is that: constructing a roof greening adaptive index system of the urban building of the Fujian Xiamen island: on the basis of developing the accurate extraction of urban building pattern spots, a building body-roof attribute two-level construction method is used for constructing a roof greening adaptive index system of urban buildings of Fujian Xiamening island on the basis of the principles of scientificity, popularization and easy operation. The indexes of the building body include: building function, building height; the indices of "roof properties" include: roof area, roof material, roof slope. The method comprises the steps of obtaining information such as building height and building functions by using an ArcGIS space connection tool, obtaining information such as roof area, roof material and roof gradient by using a high-resolution remote sensing information extraction technology, and forming a building roof index information table shown in table 2.
TABLE 2
The third step: building door island building roof greening suitability evaluation grading is developed based on the rule tree: greening building roofAnd (4) establishing a classification rule tree which starts from the root node and reaches leaf nodes through child nodes, and giving scores of 'unsuitable construction', 'suitable construction' and 'extremely suitable construction' to the building roof. The greening suitability of each building roof is evaluated and graded based on the rule tree, and the roof area of the extremely-suitable building of the building door island is 570.09 ten thousand meters2The area of the roof of the building is 622.53 ten thousand meters2As in fig. 4.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (9)
1. A method for evaluating and grading the roof greening suitability of an urban building is characterized by comprising the following steps:
acquiring a high-resolution remote sensing image, and acquiring urban space element data through the high-resolution remote sensing image;
constructing a roof greening adaptive index system of the urban building based on the urban space element data;
carrying out quantitative calculation on the urban building roof greening adaptive index system to construct a building roof index information table;
and finishing the evaluation and grading of the urban building roof greening suitability based on the building roof index information table.
2. The method for assessing and grading urban building roof greening suitability according to claim 1, wherein the specific method for acquiring urban space element data is as follows:
extracting a control network of the urban scene partition based on the high-resolution remote sensing image, and dividing the high-resolution remote sensing image into a plurality of independent task blocks based on the control network;
merging the types of the buildings according to the visual characteristic difference reflected by each task block in the high-resolution remote sensing image, classifying and extracting patch objects from each task block by adopting a depth network extraction model, performing incremental learning on the depth network extraction model through the patch objects, and iterating to the next classified extraction; the blob object is used to represent the urban space element data.
3. The method as claimed in claim 2, wherein the control net of the city scene partition comprises a city road net and a water system net.
4. The method for assessing the suitability of urban building roof greening according to claim 2, wherein the method for incrementally learning the deep network extraction model comprises:
and supplementing the extracted plaque object into training sample data of the deep network extraction model, finishing updating the training sample data, and finishing incremental learning of the deep network extraction model through the updated training sample data.
5. The method for assessing the suitability of the roof greening of the urban building according to claim 1, wherein the suitability index system of the roof greening of the urban building is constructed from two layers of building body and roof attribute.
6. The method for assessing the suitability of urban building roof greening according to claim 5, wherein the indexes of the building body comprise building function, building height; the index of the roof property comprises the roof area, the roof material and the roof gradient.
7. The method for assessing the suitability of urban building roof greening according to claim 6, wherein the information of building functions and building heights is obtained by a space connection tool of ArcGIS; the roof area, the roof material and the roof gradient are obtained by a high resolution remote sensing image information extraction method.
8. The method for assessing and grading the roof greening suitability of urban buildings according to claim 1, wherein the assessment and grading of the roof greening suitability of the buildings are completed by constructing a classification rule tree.
9. The method for assessing the suitability of urban building roof greening according to claim 8, wherein the classification rule tree is composed of root nodes, child nodes and leaf nodes connected together.
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Citations (2)
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| US20110033110A1 (en) * | 2008-04-23 | 2011-02-10 | Pasco Corporation | Building roof outline recognizing device, building roof outline recognizing method, and building roof outline recognizing program |
| CN108898298A (en) * | 2018-06-20 | 2018-11-27 | 华南理工大学 | Existing building roof greening Evaluation of Sustainability method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110033110A1 (en) * | 2008-04-23 | 2011-02-10 | Pasco Corporation | Building roof outline recognizing device, building roof outline recognizing method, and building roof outline recognizing program |
| CN108898298A (en) * | 2018-06-20 | 2018-11-27 | 华南理工大学 | Existing building roof greening Evaluation of Sustainability method |
Non-Patent Citations (4)
| Title |
|---|
| NAN XU ET AL.: ""Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods"", 《SUSTAINABILITY》 * |
| 左进等: ""城市再生视野下高密度城区绿地***多维网络化建构规划实践研究"", 《2017城市发展与规划论文集》 * |
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Application publication date: 20210827 |