Method for exploring easy-to-observe points in urban and rural landscape environment

Abstract

The invention discloses a kind of urban and rural landscape environment overlook easy to detect point excavation method, comprising the following steps: extracting settlement boundary and water system boundary from settlement remote sensing image, calculating settlement geometric center in settlement boundary;The settlement geometric center is mapped to DEM data, the walking time matrix of each grid of DEM is calculated with settlement center as starting point, and the reachable domain is obtained according to the set walking time threshold t0;The visual field of each grid point is calculated within the scope of reachable domain, and finally the overlook easy to detect point is obtained by the video frequency of each grid visual field Substacking and sorting processing, this method uses remote sensing image, DEM data, and intelligently excavates the high-quality overlook easy to detect point in urban and rural landscape environment by means of topographic analysis, simple parameter setting during operation, easy to use, and has good universality for different types of settlement landscape site selection.

Description

Technical Field

[0001] This invention belongs to the field of digital terrain analysis and intelligent planning of urban and rural landscape environment, and relates to a method for discovering easily observable points in urban and rural landscape environment. Background Technology

[0002] Across China's diverse natural landscapes and varied environmental styles, the spatial order and patterns of mountains and rivers in different regions have created their unique and irreplaceable regional identities. Based on geographic information, the discovery of important topographical points and superior viewing patterns in the mountain and water environment has significant theoretical and practical value for industries such as settlement site selection, landscape construction, and tourism planning.

[0003] To quantify the landscape perspective from the viewpoint of urban and rural planning more precisely, Wang Shusheng et al. published an academic paper titled "Undeterred by Climbing: A Spirit of Dedicated Observation and Exploration of Natural Landscapes" in the journal *Urban Planning* in 2018. This paper describes the influence of the spirit of "undeterred by climbing"—a spirit of observing and exploring natural landscapes—on ancient Chinese urban planning, and extracts the ideas of traditional Chinese planning experience in exploring landscape resources and potential order. It clarifies the significant impact of observation areas and easily observable points on the laws of settlement construction within the natural landscape environment; however, current technologies cannot automatically discover easily observable points in urban and rural landscape environments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for discovering easily observable points in urban and rural landscape environments. This method is capable of discovering easily observable points in urban and rural landscape environments.

[0005] To achieve the above objectives, the method and system for discovering easily observable observation points in urban and rural landscape environments according to the present invention includes the following steps:

[0006] 1) Acquire a coordinate-aligned remote sensing image Y and a digital elevation model G of the target settlement and its surrounding area within a radius of N kilometers;

[0007] 2) Extract settlement boundaries and water system boundaries from remote sensing image Y to obtain settlement mask image F and water system mask image H;

[0008] 3) Calculate the mean of the relative coordinates of each mask pixel within the mask image F of the settlement. And take this as the geometric center of the settlement.

[0009] 4) Calculate the slope factor for the digital elevation model G of the target settlement to obtain the slope matrix S;

[0010] 5) The geometric center of the settlement The geometric center of the settlement is calculated by mapping it to the digital elevation model G of the target settlement, which serves as the starting point for the regional survey of the settlement. The path lengths between grid points in the digital elevation model G leading to the target settlement are used to construct the distance matrix D.

[0011] 6) Calculate the walking speed on each grid in the digital elevation model G of the target settlement based on the slope matrix S, and obtain the walking speed matrix V;

[0012] 7) Divide the distance matrix D obtained in step 5) by the travel velocity matrix V obtained in step 6) to obtain the geometric center of the settlement. The walking time matrix T for each grid in the digital elevation model G leading to the target settlement is defined, where a walking time threshold t0 is set. When the element value T(i,j) in the walking time matrix T ≤ t0, it indicates that the grid belongs to the settlement center. Given the reachable regions after departure and walking time t0 hours, construct a reachable region labeling matrix A;

[0013] 8) Calculate the visible domain label matrix W for each point A(i,j) in the reachable domain label matrix A. ij The set of visible field matrices W{W 11 W 12 ,…,W mn}, where, when the visible area W ij When the element value is 1, it means that the grid is visible to A(i,j). When the visible area W ij When the value of an element is 0, it means that the grid point is not visible to A(i,j);

[0014] 9) For all visible field marker matrices W in the visible field matrix set W in step 8), ij Perform summation operations on the corresponding elements to obtain the viewing frequency matrix X, where X(i,j) represents the frequency of the grid being viewed by other grids in the digital elevation model G within the reachable domain. Sort the values ​​of X(i,j)>0 in descending order, select the grid points with the highest ranking σ, and use them to construct the set of easily observable viewing points Z in the set of visible domain matrix W.

[0015] The remote sensing image Y and the digital elevation model G in step 1) are respectively:

[0016]

[0017]

[0018] In this case, Y has l×k pixels and G has m×n grids.

[0019] The settlement mask marker image F in step 2) is:

[0020]

[0021] The mask image H of the water system is:

[0022]

[0023] Settlement geometric center in step 3) middle,

[0024] The slope matrix S in step 4) is:

[0025]

[0026]

[0027] Where G(i,j) is the grid with relative coordinates i and j in the digital elevation model G, and e is the grid accuracy of the digital elevation model G.

[0028] The distance matrix D in step 5) is:

[0029]

[0030] Where D(i,j)=d1+d2+…+d u (8)

[0031]

[0032] In the formula, e is the grid accuracy of the digital elevation model G, h is the elevation difference between G(i1,j1) and G(i2,j2), u is the number of grids that need to be traversed from c0 to G(i,j) in P, d1 is the path length from c0 to G(1,0), and d2 is the path length from G(1,0) to G(1,1).

[0033] The travel velocity matrix V in step 6) is:

[0034]

[0035] Where V(i,j) represents the value from... The average speed of travel from the starting point to the grid point G(i,j).

[0036] The walking time matrix T and reachability domain labeling matrix A in step 7) are as follows:

[0037]

[0038]

[0039] The specific operation of step 9) is as follows:

[0040] 9a) For all visible field label matrices W in the visible field set W in step 8), ij Perform the summation operation on the corresponding elements to obtain the observation frequency matrix X. The value of X(i,j) represents the frequency of the grid being observed by other grids in the digital elevation model G within the reachable domain.

[0041]

[0042] 9b) Sort all X(i,j)>0 elements in descending order, select the top σ grid points, and use them to construct the set of easily observable points Z in the visible field set W.

[0043] The present invention has the following beneficial effects:

[0044] The method for identifying easily observable observation points in urban and rural landscape environments, as described in this invention, involves extracting settlement boundaries and water system boundaries from remote sensing images of settlements. The geometric center of the settlement is calculated within the settlement boundaries and mapped onto DEM data. The walking time matrix from the settlement center to each grid cell in the DEM is calculated, and the reachable domain is obtained based on a set walking time threshold t0. Within the reachable domain, the visible area of ​​each grid cell is calculated. By summing the elements of the visible areas of each grid cell and sorting them by observation frequency, easily observable observation points are finally obtained. This invention intelligently identifies easily observable observation points in urban and rural landscape environments using remote sensing images and DEM data through terrain analysis. The operating parameters are simple to set and easy to use, and it has good universality for analyzing different types of settlement environments and selecting easily observable observation points. Attached Figure Description

[0045] Figure 1 A schematic diagram of the set of street travel points P that start from the settlement center c0 and reach grid G(i,j);

[0046] Figure 2 This is a flowchart of the analysis and calculation process of the present invention;

[0047] Figure 3 This is a flowchart of the walking distance matrix calculation in reachability analysis;

[0048] Figure 4 This is a flowchart of the velocity matrix calculation process in reachability analysis.

[0049] Figure 5 The image shown is a remote sensing image of Qingmuchuan Town in the example.

[0050] Figure 6 This is a schematic diagram of the DEM data of Qingmuchuan Town in the embodiment;

[0051] Figure 7 This is a mask image of the settlement of Qingmuchuan Town in the embodiment;

[0052] Figure 8 The image shown is a water system mask image of Qingmuchuan Town in the example.

[0053] Figure 9 This is a diagram showing the 30-minute reachability analysis results for Qingmuchuan Town in the example;

[0054] Figure 10 This is a map showing the observation points within a 30-minute reach of Qingmuchuan Town in the example;

[0055] Figure 11 The image shown is a remote sensing image of Taixiangsi Village in the example.

[0056] Figure 12 The example uses DEM data of Taixiangsi Village.

[0057] Figure 13 This is a mask image of the settlement of Taixiangsi Village in the embodiment;

[0058] Figure 14 This is a diagram showing the 45-minute reachability analysis results for Taixiangsi Village in the example.

[0059] Figure 15 The image shows the observation points within a 45-minute reach of Taixiangsi Village in the example. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, not all embodiments, and are not intended to limit the scope of the present invention. Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion regarding the concepts disclosed in the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0061] The accompanying drawings show structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not drawn to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0062] refer to Figures 1 to 4The method for discovering easily observable observation points in urban and rural landscape environments as described in this invention includes the following steps:

[0063] 1) Acquire remote sensing images Y and digital elevation model G (DEM data) of the target settlement and its surrounding 50 km radius, aligned to the coordinate system.

[0064]

[0065]

[0066] In this case, Y has l×k pixels and G has m×n grids.

[0067] 2) Extract settlement boundaries and water system boundaries from the remote sensing image Y containing the target settlement to obtain the mask image F of the settlement and the mask image H of the water system.

[0068] The specific operation of step 2) is as follows:

[0069] The settlement boundary is extracted from the remote sensing image Y containing the target settlement to obtain the settlement mask image F. Each pixel inside the settlement boundary is marked with a value of 1 (settlement mask pixel), and each pixel outside the boundary is marked with a value of 0. p and q are the coordinates of the pixel.

[0070]

[0071] Meanwhile, considering the impact of rivers, lakes and other water systems in the landscape on the discovery of easily observable points, water system extraction is performed on Y to obtain the masked image H of the water system, as shown in Equation (4). The pixel value inside the water system boundary is marked as 2 (water system mask pixel), and the pixel value outside the boundary is marked as 0. p and q are the coordinates of the pixel.

[0072]

[0073] 3) The mean of the relative coordinates of each mask pixel within the settlement mask image F. As the geometric center of the settlement Right now

[0074] 4) Calculate the slope factor for the digital elevation model G of the target settlement to obtain the slope matrix S;

[0075] The specific operation of step 4) is as follows:

[0076] The slope factor is calculated from the DEM data G of the target settlement to obtain the slope matrix S, as shown in Equation (5).

[0077]

[0078]

[0079] Where G(i,j) is a certain DEM grid, i and j are its relative coordinates, and e is the grid accuracy of the DEM.

[0080] 5) Mapped into the digital elevation model G, serving as the starting point for the regional survey of the settlement, calculations are performed. The distance matrix D is obtained by calculating the path lengths between grid points in the digital elevation model G.

[0081]

[0082] The specific operation of step 5) is as follows:

[0083] Will Mapped into the digital elevation model G, it serves as the starting point for the area under investigation, with... Figure 1 If G(i,j) is the target point, then according to the street-level movement method, we can obtain the following from... The path point set P{c0,G(1,0),G(1,1),…,G(i,j)} from the starting point to the grid G(i,j) in the digital elevation model G is then calculated according to equation (8). The path length D(i,j) to G(i,j).

[0084] D(i,j)=d1+d2+…+d u (8)

[0085] Where u is the number of grid cells that need to be traversed from c0 to G(i,j) in P, and d1 is... Figure 1 Let c0 be the path length from G(1,0), d2 be the path length from G(1,0) to G(1,1), and d3, ..., d... u This continues until point G(i,j) is reached.

[0086] Let d α Let G(i1,j1) and G(i2,j2) be the path length between two arbitrary adjacent points G(i1,j1) and G(i2,j2) in P, where G(i1,j1) is the path length between them.

[0087]

[0088] Where e is the grid accuracy of the DEM, and h is the elevation difference between G(i1,j1) and G(i2,j2).

[0089] Calculate sequentially from The path length D(i,j) from the starting point to the remaining grid points in the digital elevation model G is obtained. The distance matrix D to each grid point in the digital elevation model G is given by equation (7).

[0090] 6) Based on the slope matrix S obtained in step 4), calculate the walking speed on each grid in the digital elevation model G, and then construct the walking speed matrix V, where the walking speed of a person is set to v0 km / h.

[0091] The specific operation of step 6) is as follows:

[0092] Based on the slope matrix S obtained in step 4), the relative velocity matrix V of the travel in the digital elevation model G is calculated according to equation (10). r ;

[0093]

[0094] For the grid marked as 2 in the water system mask image H obtained in step 2), the relative travel speed V of this grid is... r (i,j) is calculated using equation (10), and then corrected by multiplying the result by the adjustment coefficient ρ of the water system using equation (11).

[0095] V rα (i,j)=V r (i,j)×ρ,ρ∈(0,1) (11)

[0096] according to Figure 1 Calculation from The relative velocities of all grid points in the grid point set P traversed by G(i,j) are accumulated. V sum At this point, according to equation (12), the relative travel speed V of each grid is... rα (i,j) and V sum The ratio F α (i,j) is the difficulty coefficient for traversing the grid, i.e.,

[0097] F α (i,j)=V rα (i,j)÷V sum ,α=1,2…,u (12)

[0098] Where u is the number of grid cells that need to be traversed from c0 to G(i,j) in P.

[0099] Calculate from according to formula (13) The average travel speed V(i,j) from the starting point to grid point G(i,j) is used to obtain the travel speed matrix V for each grid in the DEM.

[0100]

[0101] 7) Divide the distance matrix D obtained in step 5) by the travel velocity matrix V obtained in step 6) to obtain... To reach each grid in the digital elevation model G, the walking time matrix T is used. A walking time threshold t0 is set. When the element value T(i,j) in matrix T ≤ t0, it indicates that the grid belongs to the area from the settlement center. The reachable domains from the start and after walking t0 hours are denoted as the reachable domain label matrix A;

[0102] The specific operation of step 7) is as follows:

[0103] Divide the distance matrix D obtained in step 5) by the travel speed matrix V obtained in step 6) to obtain the travel time matrix T for each DEM grid. Set the travel time threshold t0 and check the relationship between the element values ​​T(i,j) of matrix T and t0 to obtain the walking reachable domain A(i,j) as shown in equation (15). When T(i,j) <= t0, A(i,j) is marked with a value of 1, indicating that the grid belongs to the area from the settlement center. The grid is defined as the reachable region after a walking time of t0 hours. Otherwise, A(i,j) is marked with a value of 0, indicating that the grid is unreachable after a walking time of t0 hours.

[0104]

[0105]

[0106] 8) Calculate the visible domain label matrix W for each point A(i,j) in the reachable domain label matrix A. ij The set of visible field matrices W{W 11 W 12 ,…,W mn}, visible field label matrix W ij A marker matrix of the same size as the digital elevation model G is used. When an element has a value of 1, it means that the grid point is visible to A(i,j), and when an element has a value of 0, it means that the grid point is not visible to A(i,j).

[0107] The specific operation of step 8) is as follows:

[0108] 8a) Install the python-gdal third-party function library (development version 3.3.1), call the gdal.ViewshedGenerate function in the python-gdal third-party function library, pass in the latitude and longitude coordinates of a point A(i,j) = 1 within the reachable domain as the human's observation viewpoint, set the basic parameters such as the observer's height and maximum observation distance, and use this function to calculate and generate the viewable domain marker matrix W of point A(i,j). ij ;

[0109] 8b) Set the observation point selection interval in the DEM data, iterate through the observation points within the reachable domain where A(i,j)=1, calculate the viewpoint label matrix for all observation points, and obtain the viewpoint set W{W 11 W 12 ,…,W mn}

[0110] 9) For all visible field label matrices W in the visible field set W from step 8), ij Perform the summation operation on the corresponding elements to obtain the viewing frequency matrix X, where X(i,j) represents the frequency of the grid being viewed by other grids in the digital elevation model G within the reachable domain. Sort the values ​​of X(i,j)>0 in descending order, select the grid points with the highest ranking σ, and use them to construct the set of easily observable viewing points Z in the visible domain set W.

[0111] The specific operation of step 9) is as follows:

[0112] 9a) For all visible field label matrices W in the visible field set W in step 8), ij Perform the summation operation on the corresponding elements, as shown in Equation (16), to obtain the viewing frequency matrix X, where the value of X(i,j) represents the frequency of the grid being viewed by other grids in the digital elevation model G within the reachable domain.

[0113]

[0114] 9b) Sort all X(i,j)>0 elements in descending order, select the top σ grid points, and use them to construct the set of easily observable points Z in the visible field set W.

[0115] Example

[0116] Reference Figure 2 To verify the rationality and effectiveness of this invention, Qingmuchuan Town in Ningqiang County, Hanzhong City, Shaanxi Province, and Taixiangsi Village in Guanzhuang Town, Yanchuan County, Yan'an City, were selected as test cases. Remote sensing images and DEM data of these two settlements were obtained from 91 Satellite Image Assistant, such as... Figure 5 and Figure 11 As shown, observation points were excavated, and their data information is shown in Table 1. The resolution in the table is pixels / meter or grid / meter; the excavation results are as follows: Figure 10 and Figure 15 The triangular points in the diagram.

[0117] Table 1

[0118]

[0119] Figure 10 When the walking time threshold t0 = 30 minutes, this invention automatically identifies easily observable viewpoints in Qingmuchuan Town. Figure 15When the walking time threshold t0 = 45 minutes, the Taixiangsi Village observation point location map automatically identified by this invention uses diamond-shaped markers as observation points and circular markers as the geometric center of the settlement. For ease of observation, the number of visual observation points is set to 3.

[0120] To refine the rationality of the observation point discovery method, a quantitative evaluation was conducted based on the number of observation grids and the proportion of the observation area in the reachable domain. The evaluation results of the top 3 observation points are shown in Table 2.

[0121] Table 2

[0122]

Claims

1. A method for identifying easily observable observation points in urban and rural landscape environments, characterized in that, Includes the following steps: 1) Acquire remote sensing images of the target settlement and its surrounding area within a radius of N kilometers, aligned to the coordinate system. Y and digital elevation model G ; 2) For remote sensing images The boundaries of the settlement and the water system are extracted to obtain a mask image of the settlement. and masked images of water systems ; 3) Calculate the mask image of the settlement. Mean of relative coordinates of each mask pixel within the mask And take this as the geometric center of the settlement. ; 4) Digital elevation model of the target settlement G Slope factor calculation is performed to obtain the slope matrix. ; 5) Construct the distance matrix ; 6) Based on the slope matrix Calculate the digital elevation model of the target settlement G The walking speed matrix is ​​obtained by measuring the walking speed on each grid. ; 7) Construct the reachable domain label matrix ; 8) Calculate the reachable domain label matrix various points in the middle visible field label matrix Then, the set of visible field matrices is constructed. Among them, when the visible field of view When the value of an element is 1, it means that the grid is... Visible, when the visible area When the value of an element is 0, it means that the grid is... Invisible; 9) For the set of visible field matrices in step 8) All visible field marker matrices in By summing the corresponding elements, we obtain the observation frequency matrix. , This indicates that the grid is within the reachable domain of the digital elevation model. G The frequency of observations of other grids in the middle, for Sort the element values ​​in descending order and filter out the top-ranked elements. The grid is used to construct the set of visible field matrices. The easily observable observation point set Z in the middle; The specific operation of step 5) is as follows: The geometric center of the settlement Digital elevation model mapped to the target settlement G In the middle, as the starting point for regional investigation of settlements, the geometric center of the settlement is calculated. Digital elevation model of the target settlement G The path lengths between each grid cell are used to construct a distance matrix. ; The specific operation of step 7) is as follows: The distance matrix obtained in step 5) Divide by the travel speed matrix obtained in step 6) The geometric center of the settlement is obtained. Digital elevation model for reaching the target settlement G Walking time matrix of the grid Among them, setting a travel time threshold When the walking time matrix element values ​​in ≤ When the time is right, it indicates that the grid belongs to the area from the settlement center. Departure, on foot Hourly reachable domains, construct reachable domain label matrix ; Walking time matrix and reachable domain label matrix They are respectively: (11) (12)。 2. The method for discovering easily observable observation points in urban and rural landscape environments according to claim 1, characterized in that, Remote sensing image in step 1) Y and digital elevation model G They are respectively: (1) (2) in, Y The number of pixels is l , G The number of grids is .

3. The method for discovering easily observable observation points in urban and rural landscape environments according to claim 1, characterized in that, Masked marker image of settlement for: (3) Masked image of water system for: (4)。 4. The method for discovering easily observable observation points in urban and rural landscape environments according to claim 3, characterized in that, The center of the settlement , .

5. The method for discovering easily observable viewpoints in urban and rural landscape environments according to claim 1, characterized in that, Slope matrix for: (5) (6) in, For digital elevation model G The relative coordinates are , The grid, For digital elevation model G The grid accuracy.

6. The method for discovering easily observable observation points in urban and rural landscape environments according to claim 1, characterized in that, Distance matrix for: (7) in, (8) (9) In the formula, For digital elevation model G Grid accuracy, for and The elevation difference between them for Zhong Cong Departure to The number of grid cells to be traversed. for Walk to Path length, for Walk to The path length.

7. The method for discovering easily observable observation points in urban and rural landscape environments according to claim 1, characterized in that, The specific operation of step 9) is as follows: 9a) For the set of visible fields in step 8) All visible field labeling matrices Perform summation operations on the corresponding elements to obtain the observation frequency matrix. , The value indicates that the grid is within the reachable domain of the digital elevation model. G The frequency of observations of other grids in the middle, among which (13) 9b) For all Sort the element values ​​in descending order and filter out the top-ranked elements. The grid is used to construct the set of visible fields. The easily observable observation point set Z in the middle.

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