Building model rapid generation method based on GIS attribute enhancement and dynamic matching

Abstract

The invention discloses a rapid building model generation method based on GIS attribute enhancement and dynamic matching, and belongs to the technical field of geographic information systems and three-dimensional modeling. Aiming at the problems of low construction efficiency and lack of building details of a traditional three-dimensional scene, the method is realized through the following technical scheme: firstly, expanding a semantic attribute tag of an SHP file, and newly adding fields such as a building color, a unit number and a bay window identifier; the buildings are divided into standardized buildings (4 points) and non-standardized buildings (gt; 4 points) and abnormal data (lt; 3 point); a dynamic matching algorithm is adopted for the standardized building, matching score = alpha * height fit value + beta * length fit value + gamma * width fit value + delta * feature tag matching number (weight alpha belongs to [0.4, 0.6], delta belongs to [0.1, 0.2]), and a TOP3 model is selected for bottom surface scaling and dynamic material generation; and the non-standardized building generates a base Actor to support the dragging of the prefabricated components (playground and bell tower) for automatic positioning. According to the method, the construction time of a 10km < 2 > scene is shortened from 7 days to 2 hours, the height error is smaller than or equal to 1.8%, the color rendition accuracy of the outer facade is larger than or equal to 95%, and the method is suitable for rapid construction of digital twinborn scenes such as smart city management and city planning.

Description

Technical Field

[0001] This invention relates to the fields of Geographic Information Systems (GIS) and 3D modeling technology, specifically a method for dynamically matching building models and rapidly constructing high-precision digital twin scenes by enhancing GIS attribute data, applicable to fields such as smart city management and urban planning. Background Technology

[0002] The following technical bottlenecks currently exist in the construction of digital twin scenarios:

[0003] 1. Low modeling efficiency: Traditional methods rely on manual modeling, which is inefficient for 10km. 2 Modeling urban building scenes takes several weeks.

[0004] 2. Lack of detail: Existing automated building model generation tools produce "white model" buildings that lack exterior facade colors, textures, and functional features (such as bay windows and the number of units);

[0005] 3. Insufficient matching accuracy: Existing technologies rely only on geometric attributes (height, outline) and ignore semantic attributes (building type, color), resulting in poor scene realism.

[0006] The fundamental question is: how to efficiently generate 3D models with both geometric accuracy and semantic detail using lightweight 2D GIS data (such as SHP files). Summary of the Invention

[0007] I. Technical problems to be solved

[0008] 1. Increases the efficiency of architectural model making by more than 10 times;

[0009] 2. Enhanced property attributes to restore the details of the building's exterior facade;

[0010] 3. Establish a hierarchical matching mechanism to control geometric error to <2%.

[0011] II. Core Technical Solution

[0012] 1. GIS attribute enhancement mechanism

[0013] • Expand SHP standard attribute fields and add semantic tags:

[0014] • Building color (RGB value), number of units, bay window signage, and function type (residential / hospital / station);

[0015] • Extended fields are stored in the SHP attribute table and are associated with geometric data.

[0016] Grading rules:

[0017] Outline Points category Treatment ＝4 Standardized buildings Dynamic matching + automatic generation >4 Non-standard buildings Base Actor+ component drag and drop <3 Abnormal data Transparent box warning label

[0018] Dynamic matching algorithm:

[0019] Overall matching score = α × height matching value + β × length matching value + γ × width matching value + δ × number of feature label matches.

[0020] Weight priority: α∈[0.4,0.6]>β∈[0.2,0.3]>γ∈[0.1,0.2]>δ∈[0.1,0.2].

[0021] 2. Lightweighting of non-standard buildings

[0022] • Generate a planar base Actor with attribute information;

[0023] • Provides a drag-and-drop interface for adding prefabricated components (playground, clock tower), with components automatically aligned by carrying geographical offset parameters.

[0024] III. Beneficial Effects

[0025] 1. Efficiency improvement: 10km 2 Scene generation time has been reduced from 7 days to 2 hours;

[0026] 2. Detail Reproduction: Materials are dynamically generated using RGB values ​​to reproduce the colors of the building's exterior facade;

[0027] 3. Precision control: The height-weighted priority strategy ensures that the height error is ≤1.8%;

[0028] 4. Strong compatibility: Supports importing CAD / BIM models into extended building libraries. IV. Detailed Implementation

[0029] Step 1: GIS Data Preprocessing

[0030] Use the GeoTools library to parse the SHP file and extract the vertex coordinates of the building outline polygon;

[0031] Example of extended attribute field storage:

[0032] field name type Example value building_color String "255,200,150" unit_count Intege 3 has_bay_window Boolean true

[0033] The data is categorized by the number of outline points and stored in three arrays: standard building array, non-standard building array, and abnormal data array.

[0034] Step 2: Building the Architectural Model Library

[0035] The standardized model was created using parameterization based on a 3-meter floor height benchmark.

[0036] Model pre-labeled attribute tags:

[0037]

[0038] Step 3: Dynamic Matching Engine

[0039] Four-level matching process:

[0040] ① Type filtering library: Filter all "Residential" type models;

[0041] ② High degree of matching: Calculate the absolute error rate "|H oe "-H| / H" retains models with errors ≤ 5%;

[0042] ③ Length and width calibration: Calculate the building orientation based on the outline points (vector cross product method), and adapt the rotating model;

[0043] ④ Feature weighting: Count the number of matching tags, and multiply the weight of combined tags (such as "red + bay window") by 1.5.

[0044] Matching example:

[0045] SHP Attributes: `Height = 21m, Color = "255,0,0", Bay Window = true`

[0046] Model A fit value: height 0.98 (2% error) + color matching 1 + window matching 1 → number of features = 2

[0047] Overall Score = 0.5 × 0.98 + 0.25 × 1 + 0.15 × 1 + 0.1 × 2 = 0.49 + 0.25 + 0.15 + 0.2 = 1.09 Step 4: Scene Generation and Interaction

[0048] Standardized building treatment:

[0049] Take the top 3 matching models and scale the bottom vertices to match the SHP contour;

[0050] Lock other channels: Normal / spectral maps retain 100% of their original data.

[0051] Non-standard building treatment:

[0052] Base Actor Generation: Construct a triangular mesh plane based on the contour points;

[0053] Component drag and drop: Users drag "playground" from the library to the school base, and the system automatically calculates the offset coordinates for positioning.

[0054] Step 5: Abnormal Data Handling

[0055] Generate a semi-transparent red box (10m×10m×10m);

[0056] Link the original SHP attribute, click the box to bring up the verification panel. V. Description of the attached drawings

[0058] 1. Figure 1 This diagram illustrates the architecture for building classification matching, showing the process from reading shapefile data to matching building classifications.

[0059] Key process nodes description:

[0060] ①SHP Data Input Module: Parses GIS data containing extended attributes.

[0061] ② Determine and classify contour points:

[0062] • Branch 1 (Contour Points = 4): Standardized building processing flow;

[0063] • Branch 2 (outline points > 4): Non-standard building processing flow;

[0064] • Branch 3 (Contour Points < 3): Abnormal data marker stream.

[0065] ③ Standardized building matching engine:

[0066] • Type screening unit;

[0067] • Height / Length / Width Calibration Unit;

[0068] • Feature weighted calculation unit.

[0069] ④ Non-standard building processors:

[0070] • Base Actor generation unit;

[0071] • Component drag-and-drop interface.

[0072] ⑤ 3D scene output: Generate a digital twin scene containing details of the building facade.

[0073] 2. Figure 2 This demonstrates the process of generating dynamic materials.

[0074] Key process nodes description:

[0075] ①Analyze the original material:

[0076] • Extract shader parameters (diffuse / spectral / normal channels);

[0077] • Read texture maps (diffuse map, normal map, roughness map).

[0078] ② Create a material instance:

[0079] • Duplicate the original material to create a new instance (non-destructive editing);

[0080] • Preserving UV mapping ensures texture coordinate alignment;

[0081] Key mechanism: Instantiation isolation (to avoid contaminating the original resources).

[0082] ③ Inject RGB values

[0083] Convert the RGB string to a normalized vector;

[0084] • The diffuse base color parameter of the overlay material instance;

[0085] • Technical constraints: Only the base color is modified; lighting calculations are not affected.

[0086] ④Preserve texture channels

[0087] Diffuse map blending: Use the new RGB value as the base color and blend it with the original map using alpha;

[0088] VI. Examples

[0089] Applications of smart city management platforms:

[0090] 1. Enter 10km of Beijing Yizhuang Economic Development Zone 2 SHP data (including 2000 buildings); 3D scene generated within 2.2 hours:

[0091] • 1850 standardized buildings were automatically generated;

[0092] • Add playgrounds / connecting corridors to 120 non-standard buildings by dragging and dropping;

[0093] • 30 buildings have been flagged for abnormal data and alerts have been issued;

[0094] 3. The accuracy of color reproduction on the facade is ≥95%, and the height error is ≤1.8%.

Claims

1. A method for rapid generation of building models based on GIS attribute enhancement and dynamic matching, characterized in that, Includes the following steps: S1.GIS Data Preprocessing: Parse SHP format building GIS data, extract building outline polygon coordinates and attribute information. The attribute information includes extended semantic tag fields: building color RGB value, number of building units, bay window presence identifier, and building function type. S2. Building Hierarchical Storage: Buildings are classified according to the number of outline points. Buildings with 4 outline points are classified as standardized buildings, buildings with more than 4 outline points are classified as non-standardized buildings, and buildings with less than 3 outline points are marked as abnormal data. S3. Standardized Building Dynamic Matching: a) Type matching: Filter candidate models in the building model library based on the building function type in the SHP data; b) Height matching: Based on the building height attribute in the SHP data, models with height errors within ±5% are selected from the candidate models and sorted by fit. c) Length and width calibration: Calculate the actual length, width and orientation of the building based on the outline points, and adapt the aspect ratio of the height-matched model; d) Feature weighting: Count the number of matches between candidate models and SHP extended attribute labels; e) Calculate the overall matching score: Overall matching score = α × height matching value + β × length matching value + γ × width matching value + δ × number of feature label matches, where the weight coefficients satisfy α > β > γ and δ ∈ [0.1, 0.2]; S4. Model generation and processing: Select the top 3 models with the highest comprehensive matching scores, scale their bottom surfaces to make their outlines completely overlap with the SHP data, and dynamically generate material instances based on the RGB values ​​of the building colors in the SHP. S5. Non-standard building treatment: Generates a planar base Actor with attribute information based on the contour points, and provides a drag-and-drop interface to add iconic elements from the prefabricated component library to the base.

2. The method according to claim 1, characterized in that, The extended semantic tag fields in step S1 also include: air conditioner outdoor unit presence identifier, roof structure type, and exterior wall material code.

3. The method according to claim 1, characterized in that, The weighting coefficients in step S3 are set as follows: α∈[0.4,0.6], β∈[0.2,0.3], γ∈[0.1,0.2].

4. The method according to claim 1, characterized in that, In step S3(d), the feature label matching adopts a combined weighted strategy. When both "bay window presence identifier" and "building color RGB value" are matched at the same time, the weighting coefficient of the feature label matching number is increased to 1.5 times.

5. The method according to claim 1, characterized in that, The dynamic generation of material instances in step S4 specifically includes: parsing the original material shader of the building model, preserving the texture map channel, and injecting the RGB values ​​in the SHP into the diffuse color parameters of the material instance.

6. The method according to claim 1, characterized in that, The prefabricated component library in step S5 includes: playground, clock tower, connecting corridor, and rooftop equipment, and the components carry geographic coordinate offset parameters to achieve automatic alignment with the base.

7. The method according to claim 1, characterized in that, It also includes anomaly data processing steps: for buildings with fewer than 3 outline points, they are marked with a semi-transparent warning cube in the 3D scene and associated with the original SHP attribute data for manual verification.

8. The digital twin scene generation system constructed according to any one of claims 1-7, characterized in that, Includes: ●GIS Attribute Enhancement Module: Used to parse and expand semantic tags in SHP files; ● Hierarchical matching engine: Executes a dynamic matching algorithm for standardized buildings; ● Interactive scene editor: Provides functions such as sorting the building list, batch model replacement, and dragging and dropping non-standard building components.

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