A method for evaluating renovation potential of existing buildings based on multi-source remote sensing data

Abstract

The application provides a kind of existing building renewal potential evaluation method based on multi-source remote sensing data, first, multi-source remote sensing data are acquired and preprocessed, according to the multi-source remote sensing data after preprocessing, deformation inversion is carried out on the target area using interferometric synthetic aperture radar technology, the deformation characteristic index of the target area is obtained, and the building completion year is estimated and the building height characteristic and building area characteristic are calculated through the multi-source remote sensing data after preprocessing, different structure aging grades are obtained according to the building completion year, the building renewal potential is comprehensively evaluated according to the above deformation characteristic index, structure aging grade, building height characteristic and building area characteristic, the building renewal potential index is obtained, the renewal potential result is obtained according to the preset potential classification rule and building renewal potential index, and the renewal potential evaluation of existing building is completed. Provide decision and technical support for urban renewal planning and reconstruction.

Description

Technical Field

[0001] This invention relates to the field of urban renewal and intelligent building assessment technology, specifically to a method for assessing the renewal potential of existing buildings based on multi-source remote sensing data. Background Technology

[0002] As my country's urbanization enters a new stage focused on optimizing existing infrastructure, the emphasis of urban construction is gradually shifting from new construction to the renovation and quality improvement of existing buildings. Many existing buildings, due to their age, low design standards, material degradation, and long-term load changes, are gradually revealing safety hazards and functional decline, becoming a significant factor restricting sustainable urban development.

[0003] In urban renewal practice, how to scientifically identify renewal targets and rationally determine renewal priorities at the city scale is one of the core issues faced by urban management and planning decision-making departments. Existing assessment and decision-making methods often rely on manual surveys and on-site testing, resulting in high costs and long cycles, making it difficult to achieve rapid coverage of large-scale building complexes. At the same time, assessment indicator systems are mostly based on single indicators, often focusing on building structural safety or ground subsidence monitoring, which is difficult to comprehensively reflect the overall renewal potential of buildings. In addition, existing schemes generally lack unified, quantitative, and comparable assessment models, making it difficult to form objective and consistent comparative criteria between different buildings or different areas. Remote sensing technologies in engineering applications are mostly limited to monitoring and display, and have not yet been effectively transformed into decision-making results that can directly support the selection of renewal targets and project prioritization.

[0004] In recent years, Synthetic Aperture Radar Interferometry (InSAR) technology, with its millimeter-level deformation monitoring capabilities and wide-area coverage, has been widely used in the field of land subsidence and building deformation monitoring. Meanwhile, multi-source remote sensing data, such as optical imagery, impermeable surface data, and building vector data, have also shown great potential in building attribute extraction. However, existing technologies often use this data in isolation, lacking a technical solution that organically integrates deformation information, building temporal attributes, and urban renewal needs. This makes it impossible to achieve automated acquisition, quantitative calculation, and hierarchical representation of the renewal potential of existing buildings at the urban scale. Summary of the Invention

[0005] To overcome the problems of the prior art, such as the single evaluation index, reliance on human experience and strong subjectivity, difficulty in automatic acquisition, quantitative calculation and hierarchical expression, the present invention provides a method for evaluating the renewal potential of existing buildings based on multi-source remote sensing data.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows, including the following steps:

[0007] Step 1: Acquire multi-source remote sensing data and preprocess the multi-source remote sensing data, which includes satellite image time series data, building outline vector data, and annual impermeable surface remote sensing data. Step 2: Based on the preprocessed satellite image time series data and building outline vector data, the target area is subjected to deformation inversion using interferometric synthetic aperture radar technology to obtain the deformation characteristic index of the target area; Step 3: Estimate the building's construction year based on the preprocessed building outline vector data and annual impermeable surface remote sensing data, and obtain different structural aging levels based on the building's construction year. Step 4: Obtain building height and building footprint characteristics based on the preprocessed building outline vector data; Step 5: Based on the deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics, a comprehensive assessment of the building renewal potential is conducted to obtain the building renewal potential index; Step 6: Obtain the renovation potential result based on the preset potential grading rules and the building renovation potential index, and complete the renovation potential assessment of the existing building.

[0008] Furthermore, the multi-source remote sensing data also includes digital elevation model data, and the preprocessing of the multi-source remote sensing data includes: Ensure that all multi-source remote sensing data are in the same geographic coordinate reference system; Radiometric calibration, denoising, and orbital correction are performed on time-series satellite imagery data, and terrain phase removal is performed using digital elevation model data. Perform topology checks and spatial corrections on building outline vector data; Time series consistency verification was performed on the annual remote sensing data of impermeable surfaces.

[0009] Furthermore, the deformation characteristic indicators include the annual average settlement rate and cumulative deformation, and the deformation characteristic indicators of the target area are obtained as follows: Based on the preprocessed satellite imagery time series data, an interferometric pair combination is constructed according to the constraints of time and space baselines; An interferogram is obtained by combining the aforementioned interferometric pairs. The interferogram is then subjected to phase unwrapping and atmospheric delay correction to obtain deformation phase information. The pixel-level surface deformation time series is obtained by inverting the deformation phase information using the interferometric synthetic aperture radar technology. The annual average settlement rate and cumulative deformation of individual buildings are calculated from the pixel-level surface deformation time series. The pixel-level surface deformation time series refers to a continuous sequence of cumulative deformation of the surface at each pixel location along the radar line of sight, reconstructed over time, using each pixel as the basic observation unit.

[0010] Furthermore, the step of inverting the deformation phase information using interferometric synthetic aperture radar technology to obtain the pixel-level surface deformation time series includes: The deformation phase information is subjected to the SBAS time series inversion strategy for the first rate inversion to obtain the initial deformation sequence and residual estimate. Based on the initial deformation sequence and residual estimate, atmospheric phase removal is further carried out, and then the second rate inversion is performed to obtain the pixel-level surface deformation time series.

[0011] Furthermore, the calculation of the annual average settlement rate and cumulative deformation of a single building based on the pixel-level surface deformation time series includes: The deformation results of the pixel-level surface deformation time series are projected and geocoded, unified to the same geographic coordinate reference system, and spatially superimposed with building outline vector data. Taking individual buildings as statistical units, the deformation pixels within the building area are aggregated and analyzed to calculate and analyze the building's average annual settlement rate and cumulative deformation. The deformed pixel refers to the basic spatial unit that carries the time series of surface deformation at the pixel level, that is, each image pixel with deformation time series information.

[0012] Furthermore, the deformation characteristic index also includes the surrounding differential settlement index, which includes: Based on the outline of a single building, a buffer zone of a preset width is constructed. Effective deformation pixels within the building area and the buffer zone are aggregated and statistically analyzed to obtain the deformation statistics of the building interior and the deformation statistics of the surrounding area. The absolute value of the difference between the two is the differential settlement index of the building's perimeter. The effective deformable pixel refers to a deformable pixel with an average temporal coherence of not less than 0.6 and a standard deviation of the deformation rate inversion residual of less than 2 mm.

[0013] Furthermore, the construction date of the building is estimated based on the preprocessed building outline vector data and annual impermeable surface remote sensing data. Different structural aging levels are then determined based on the construction date, including: Extract pixels from the annual remote sensing data of impermeable surfaces within the range of the building outline vector data; The proportion of pixels in the remote sensing data of impermeable surfaces within the building area for each year is statistically analyzed to construct a time series of impermeable surfaces. Analyze the time series variation trend of impervious surfaces to identify the time points when the proportion of impervious surfaces first shows a significant increase and then remains stable in subsequent years; The significant increase refers to the increase in the proportion of impermeable surface pixels within the building outline vector data range reaching or exceeding a preset 10% threshold between two adjacent years, and the proportion remaining above 90% of the peak value for at least two years thereafter, which is considered a significant increase. The year corresponding to the aforementioned time point is used to determine the year in which the building was constructed; Based on this, buildings are classified into different structural aging levels according to their construction year.

[0014] Furthermore, the building renewal potential is comprehensively evaluated based on deformation characteristic indicators, structural aging level, building height characteristics, and building footprint characteristics to obtain a building renewal potential index, including: The deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics are standardized by integrating multiple evaluation factors and unifying the index direction to obtain standardized index values. Then, a weighted fusion method is used to comprehensively analyze the standardized index values ​​to obtain the building renewal potential index.

[0015] Furthermore, the integration of multiple evaluation factors includes: It integrates four evaluation factors: building deformation characteristics, structural aging level, building height characteristics, and building footprint characteristics; Among them, the building deformation characteristic index includes one or more of the following: annual average settlement rate, cumulative deformation, and surrounding differential settlement index; the structural aging level includes different structural aging levels; the building height characteristic includes the building height value, which is obtained from the building outline vector data; the building footprint characteristic includes calculating the footprint value by performing area calculation on the building outline polygon based on the building outline vector data, and further classifying the footprint value into footprint levels according to a preset threshold, which are the building footprint characteristics.

[0016] Furthermore, obtaining the renewal potential result based on the preset potential grading rules and the building renewal potential index includes: matching the corresponding preset potential grading rules with the numerical distribution of the building renewal potential index in different building areas to obtain the classification level of different building areas; mapping the classification level to the same geographic coordinate reference system to form the geographic spatial distribution of building renewal potential, and the geographic spatial distribution of building renewal potential is the renewal potential result.

[0017] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention acquires and preprocesses multi-source remote sensing data to construct a complete assessment process for building renewal potential. It can generate a unified renewal potential index and spatial classification results at the city scale, directly supporting the screening of renewal targets, project priority ranking, and identification of key areas. It significantly reduces the reliance on manual surveys and subjective experience, improves assessment efficiency and comparability, and realizes automated, large-scale, unified, and quantitative assessment of building renewal potential at the city scale.

[0018] By obtaining deformation characteristic indicators of the target area, an effective mapping from "pixel monitoring" to "individual building risk indicators" can be achieved. This can more accurately identify potential structural risks caused by differential settlement, enhance the reliability and interpretability of the determination of the urgency of urban renewal, provide a more engineering-significant risk characterization basis for the selection and ranking of urban renewal objects, improve the accuracy of the characterization of potential structural risks of buildings, and realize the individualized expression and interpretable interpretation of differential settlement risks.

[0019] By determining the building's construction date, different structural aging levels can be obtained. Key prior attributes can be automatically acquired without relying on manual door-to-door surveys or historical archives. This provides stable and scalable basic factors for assessing renewal potential, improving the completeness of assessment elements, the objectivity of results, and the verifiability of results. In the absence of archives or census data, the building's age and aging information can be automatically supplemented, thus improving the completeness of assessment elements. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for assessing the potential for renovating existing buildings based on multi-source remote sensing data.

[0021] Figure 2 This is a schematic diagram of the overall technical path for the temporal inversion of building deformation, a method for assessing the renewal potential of existing buildings based on multi-source remote sensing data.

[0022] Figure 3 This is a schematic diagram illustrating the overall technical path for obtaining the construction year of a building as part of a method for assessing the renovation potential of existing buildings based on multi-source remote sensing data.

[0023] Figure 4 This is a spatial distribution map of the average annual settlement rate of buildings, which is a method for assessing the potential for renewal of existing buildings based on multi-source remote sensing data.

[0024] Figure 5 This is a spatial distribution map of building construction years, representing a method for assessing the potential for renovating existing buildings based on multi-source remote sensing data.

[0025] Figure 6 This is a spatial distribution map of building height characteristics, representing a method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data.

[0026] Figure 7This is a spatial distribution map of building footprint characteristics for an existing building renovation potential assessment method based on multi-source remote sensing data.

[0027] Figure 8 This is a spatial distribution map of building renewal potential levels, representing a method for assessing the renewal potential of existing buildings based on multi-source remote sensing data. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0030] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] In a first embodiment of the present invention, a method for assessing the potential for renewal of existing buildings based on multi-source remote sensing data is provided, comprising the following steps, and combined with Figure 1 Explanation: Step 1: Acquire multi-source remote sensing data and preprocess the multi-source remote sensing data, which includes satellite image time series data, building outline vector data, and annual impermeable surface remote sensing data. Furthermore, the multi-source remote sensing data also includes digital elevation model data, and the preprocessing of the multi-source remote sensing data includes: Ensure that all multi-source remote sensing data are in the same geographic coordinate reference system; Radiometric calibration, denoising, and orbital correction are performed on time-series satellite imagery data, and terrain phase removal is performed using digital elevation model data. Perform topology checks and spatial corrections on building outline vector data; Time series consistency verification was performed on the annual remote sensing data of impermeable surfaces.

[0032] Step 2: Based on the preprocessed satellite image time series data and building outline vector data, the target area is subjected to deformation inversion using interferometric synthetic aperture radar technology to obtain the deformation characteristic index of the target area; Furthermore, the deformation characteristic indicators include the annual average settlement rate and cumulative deformation, and the deformation characteristic indicators of the target area are obtained as follows: Based on the preprocessed satellite imagery time series data, an interferometric pair combination is constructed according to the constraints of time and space baselines; The process involves baseline estimation of the preprocessed satellite image time series data, with a super master image selected as the reference image; under the constraints of preset temporal and spatial baseline thresholds, a small baseline interferometric network is constructed to generate a set of interferometric pairs; the interferometric network is then edited for connectivity and checked for connectivity, and interferometric pairs with baselines exceeding limits or expected poor coherence are removed to form a stable combination of interferometric pairs. An interferogram is obtained by combining the aforementioned interferometric pairs. The interferogram is then subjected to phase unwrapping and atmospheric delay correction to obtain deformation phase information. In this process, after generating the interferogram, a DEM is introduced to perform differential interferometry to remove the influence of terrain phase, resulting in differential interferometric phase. Next, GCP points are selected to assist in orbit refinement and re-leveling, and residual flat-ground phase / system phase is eliminated to reduce the impact of orbit and geometric errors on the phase. Finally, the interferometric phase is unwrapped, and atmospheric delay correction, i.e., atmospheric phase removal, is performed using spatiotemporal filtering and other methods to obtain deformation phase information for time series inversion. The pixel-level surface deformation time series is obtained by inverting the deformation phase information using the interferometric synthetic aperture radar technology. The annual average settlement rate and cumulative deformation of individual buildings are calculated from the pixel-level surface deformation time series. The pixel-level surface deformation time series refers to a continuous sequence of cumulative deformation of the surface at each pixel location along the radar line of sight, reconstructed over time, using each pixel as the basic observation unit.

[0033] Furthermore, the step of inverting the deformation phase information using interferometric synthetic aperture radar technology to obtain the pixel-level surface deformation time series includes: The deformation phase information is subjected to the SBAS time series inversion strategy for the first rate inversion to obtain the initial deformation sequence and residual estimate. Based on the initial deformation sequence and residual estimate, atmospheric phase removal is further carried out, and then the second rate inversion is performed to obtain the pixel-level surface deformation time series.

[0034] Furthermore, the calculation of the annual average settlement rate and cumulative deformation of a single building based on the pixel-level surface deformation time series includes: The deformation results of the pixel-level surface deformation time series are projected and geocoded, unified to the same geographic coordinate reference system, and spatially superimposed with building outline vector data. Taking individual buildings as statistical units, the deformation pixels within the building area are aggregated and analyzed to calculate and analyze the building's average annual settlement rate and cumulative deformation. The deformed pixel refers to the basic spatial unit that carries the time series of surface deformation at the pixel level, that is, each image pixel with deformation time series information.

[0035] Furthermore, the deformation characteristic index also includes the surrounding differential settlement index, which includes: Based on the outline of a single building, a buffer zone of a preset width is constructed. Effective deformation pixels within the building area and the buffer zone are aggregated and statistically analyzed to obtain the deformation statistics of the building interior and the deformation statistics of the surrounding area. The absolute value of the difference between the two is the differential settlement index of the building's perimeter. The effective deformable pixel refers to a deformable pixel with an average temporal coherence of not less than 0.6 and a standard deviation of the deformation rate inversion residual of less than 2 mm. The surrounding differential subsidence index D diff It can be calculated using the following formula:

[0036] In the formula, The statistical mean of the annual settlement rate of the effective deformation pixels within the building outline area; This index represents the statistical mean of the annual settlement rate of effective deformation pixels within a buffer zone formed by extending outwards by a predetermined width of 10m based on the building's outline. The larger this index, the more significant the difference in deformation between the building and its surrounding foundation, and the higher the risk of differential settlement.

[0037] Step 3: Estimate the building's construction year based on the preprocessed building outline vector data and annual impermeable surface remote sensing data, and obtain different structural aging levels based on the building's construction year. Furthermore, the construction date of the building is estimated based on the preprocessed building outline vector data and annual impermeable surface remote sensing data. Different structural aging levels are then determined based on the construction date, including: Extract pixels from the annual remote sensing data of impermeable surfaces within the range of the building outline vector data; The proportion of pixels in the remote sensing data of impermeable surfaces within the building area for each year is statistically analyzed to construct a time series of impermeable surfaces. Analyze the time series variation trend of impervious surfaces to identify the time points when the proportion of impervious surfaces first shows a significant increase and then remains stable in subsequent years; The significant increase refers to the increase in the proportion of impermeable surface pixels within the building outline vector data range reaching or exceeding a preset 10% threshold between two adjacent years, and the proportion remaining above 90% of the peak value for at least two years thereafter, which is considered a significant increase. The year corresponding to the aforementioned time point is used to determine the year in which the building was constructed; Based on this, buildings are classified into different structural aging levels according to their construction year.

[0038] Based on the building's construction date, buildings are classified into different structural aging levels, as follows: High aging level: Corresponds to buildings constructed before 1980, mainly brick-wood and brick-concrete structures, mostly low-rise buildings with low design standards, severe material aging, and low structural redundancy. Higher aging level: Corresponds to buildings constructed between 1980 and 1990, where brick-concrete structures were prevalent, reinforced concrete structures began to be used, multi-story buildings were the main type, and overall durability was limited. Medium aging level: Corresponds to buildings constructed between 1990 and 2000, where reinforced concrete structures have become the mainstream, frame and shear wall systems are widely used, low-rise residential buildings have increased, and the structural performance is relatively reliable but has entered the mid-term service stage. Lower aging level: Corresponds to buildings constructed between 2000 and 2010, with a large number of high-rise buildings constructed, steel structures gradually applied, energy-saving and environmental protection concepts initially introduced, and good material and structural performance; Low aging rating: This rating applies to buildings constructed after 2010, mainly high-rise and super high-rise buildings, which widely adopt green building and intelligent technologies, have excellent material properties, and high structural durability.

[0039] Step 4: Obtain building height and building footprint characteristics based on the preprocessed building outline vector data; Step 5: Based on the deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics, a comprehensive assessment of the building renewal potential is conducted to obtain the building renewal potential index; Furthermore, the building renewal potential is comprehensively evaluated based on deformation characteristic indicators, structural aging level, building height characteristics, and building footprint characteristics to obtain a building renewal potential index, including: The deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics are standardized by integrating multiple evaluation factors and unifying the index direction to obtain standardized index values. Then, a weighted fusion method is used to comprehensively analyze the standardized index values ​​to obtain the building renewal potential index.

[0040] Furthermore, the integration of multiple evaluation factors includes: It integrates four evaluation factors: building deformation characteristics, structural aging level, building height characteristics, and building footprint characteristics; Among them, the building deformation characteristic index includes one or more of the following: annual average settlement rate, cumulative deformation, and surrounding differential settlement index; the structural aging level includes different structural aging levels; the building height characteristic includes the building height value, which is obtained from the building outline vector data; the building footprint characteristic includes calculating the footprint value by performing area calculation on the building outline polygon based on the building outline vector data, and further classifying the footprint value into footprint levels according to a preset threshold, which are the building footprint characteristics.

[0041] Step 6: Obtain the renovation potential result based on the preset potential grading rules and the building renovation potential index, and complete the renovation potential assessment of the existing building.

[0042] Furthermore, obtaining the renewal potential result based on the preset potential grading rules and the building renewal potential index includes: matching the corresponding preset potential grading rules according to the numerical distribution of the building renewal potential index in different building areas to obtain the classification level of different building areas, mapping the classification level to the same geographic coordinate reference system to form the geographic spatial distribution of building renewal potential, and the geographic spatial distribution of building renewal potential is the renewal potential result. The preset potential grading rules include the following 5 levels: High-level: The building exhibits high comprehensive risks or impacts in terms of deformation characteristics, structural aging level, building height characteristics, and building footprint characteristics, and there are obvious structural safety hazards or urgent need for renovation. It should be given priority in the near-term renovation implementation plan. Higher level: Buildings that exhibit characteristics such as severe aging but stable deformation or high deformation risk but relatively new structure in one or more of the above indicators, have a strong need for renovation and can be considered as key targets for medium-term renovation. Medium level: The overall condition of the building is controllable, the level of deformation and aging is within a moderate range, there is no significant safety risk, and it can be planned and promoted in stages when urban renewal resources permit; Lower level: The building has slight deformation, the structure is below the medium aging level, the size is moderate, the risk is low, there is no urgent need for replacement, and it is advisable to include it in the long-term monitoring or long-term reserve list. Low level: All building renewal potential indices are low, structural safety and performance are excellent, and no renewal intervention is needed in the short term.

[0043] A second embodiment of the present invention provides a system for assessing the renewal potential of existing buildings based on multi-source remote sensing data, comprising a data acquisition module, a deformation inversion module, a building attribute inversion module, a renewal potential assessment module, and a result output module, wherein: The data acquisition module is used to acquire and manage satellite imagery data, multi-source remote sensing data, and building spatial coordinate data; The deformation inversion module is used to perform time-series inversion and statistical analysis of building deformation characteristics based on SBAS InSAR technology; The building attribute inversion module is used to invert the building's construction year and determine the building's structural aging level. The renewal potential assessment module is used to integrate building deformation characteristics and building-related attribute information to calculate the renewal potential index of existing buildings. The building-related attribute information includes the building structural aging level, building height characteristics, and building footprint characteristics. The results output module is used to output the results of the potential for the renewal of existing buildings and to display them in a spatial form.

[0044] In the third embodiment of the present invention, a method for assessing the potential for renewal of existing buildings based on multi-source remote sensing data is provided. The experimental area is a district under the jurisdiction of Sihui City, Guangdong Province, with a built-up area of ​​approximately 47.96 square kilometers. The method of the present invention is explained here. This invention first establishes a complete multi-source remote sensing data foundation, including: Sentinel-1A SAR SLC data: collected time-series data of 16 up-orbit satellite images covering the experimental area from 2015 to 2025, serving as the main data source for deformation monitoring.

[0045] Copernicus DEM data: used as an external digital elevation model for terrain phase removal in interferometric processing.

[0046] CMAB multi-attribute building profile data: serving as the building base vector for this experiment, containing the precise spatial profile and basic attributes of all individual buildings within the experimental area.

[0047] GISA 2.0 Annual Impervious Surface Data: As a time-series data source reflecting changes in land cover, it is used to invert the construction dates of buildings.

[0048] The following preprocessing operations were performed on the above multi-source remote sensing data: coordinate system processing to place all data in the same geographic coordinate reference system; radiometric calibration, denoising, and orbital correction were performed on the SAR satellite imagery. Topological checks and spatial corrections were performed on the building outline vector data; time series consistency verification was performed on the annual remote sensing data of impermeable surfaces.

[0049] Then, based on SBAS-InSAR technology, the temporal inversion of building deformation is performed, such as... Figure 2 As shown, it includes: Connection map generation: Spatiotemporal baseline analysis is performed on the 16 input Sentinel-1A SLC images to generate interferometric connection maps, and a super master image is selected.

[0050] Interferometric process: including baseline estimation, image registration, and generation of differential interferograms using Copernicus DEM data.

[0051] Orbit refinement and re-leveling: Orbit refinement is performed by selecting stable ground control points (GCPs) to remove residual orbit errors and long-wavelength phase.

[0052] SBAS rate inversion: After the first deformation rate inversion and atmospheric phase filtering, a second inversion is performed to obtain more accurate time-series deformation rate results.

[0053] Geocoding: The deformation results are projected from the radar coordinate system and transformed to the geographic coordinate system, and finally output time-series deformation rate raster data covering the entire experimental area.

[0054] Subsequently, the geocoded deformation rate raster was spatially overlaid with CMAB building profile data. Using each building profile as a statistical unit, the average deformation rate within it was extracted, thus transforming pixel-level deformation information into a core indicator of building-level annual average settlement rate, providing deformation feature input for subsequent assessment. The distribution map of the calculated annual average settlement rate indicator is shown below. Figure 4 As shown in the figure, the areas of rapid settlement are not uniformly distributed spatially, but rather clustered in patches or strips within urban built-up areas. This is closely related to geological conditions and engineering construction activities. This figure visually illustrates the spatial differentiation of settlement risk across the entire building area, serving as a crucial input for assessing its structural safety.

[0055] Next, based on multi-source remote sensing, the predicted building age and aging characteristics are inverted, such as... Figure 3 As shown: Data preprocessing and alignment: Spatial reference and geometric foundation are unified between CMAB building outline data and GISA 2.0 annual impermeable surface data to ensure accurate spatial overlay.

[0056] Spatial overlay and information extraction: Taking each building outline as a unit, the pixel values ​​of the internal GISA 2.0 impermeable surface data are extracted year by year, the proportion of impermeable surface of each building in each year is calculated, and a temporal feature sequence of "building-year-proportion of impermeable surface" is constructed.

[0057] Construction year extraction: The time series analysis of the proportion of impermeable surface area for each building is performed. The year in which the proportion of impermeable surface area first increases significantly and remains at a high value in subsequent years is determined as the major construction year of the building.

[0058] Output: The determined construction year is assigned to each building outline, generating a building age database containing key fields such as "Building ID - Construction Year". For example... Figure 5 The spatial distribution of building ages in the study area is shown. Based on the year of construction and referring to existing standards or experience, the structural aging levels of the buildings are further classified as shown in Table 1, which serves as another core assessment indicator.

[0059] Table 1

[0060] Based on the data obtained above, a multi-factor assessment of the building renewal potential is conducted, and the results of the extracted core indicators are integrated. For each building, an assessment vector is established, including: deformation characteristic indicators, aging characteristics, and building-specific attributes. Building-specific attributes include: area and height, obtained from CMAB data, such as... Figure 6 , Figure 7 As shown, a weighted fusion model is used to comprehensively analyze these multiple factors and calculate a building renewal potential index that comprehensively reflects the urgency and priority of building renewal.

[0061] Statistical analysis was performed on the obtained building renewal potential index. Based on the natural discontinuity method, the renewal potential was divided into five different levels: high, relatively high, medium, relatively low, and low. Finally, the classification results were combined with the building's spatial location to generate a spatial distribution map of the building renewal potential levels, as shown below. Figure 8 As shown in the figure, this map visually illustrates the clusters of architectural spaces requiring priority for renewal and individual high-risk buildings within the experimental zone, providing direct quantitative data and spatial decision support for urban renewal planning, risk management, and priority project site selection in Sihui City.

[0062] The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the renewal potential of existing buildings based on multi-source remote sensing data, characterized in that, Includes the following steps: Step 1: Acquire multi-source remote sensing data and preprocess the multi-source remote sensing data, which includes satellite image time series data, building outline vector data, and annual impermeable surface remote sensing data. Step 2: Based on the preprocessed satellite image time series data and building outline vector data, the target area is subjected to deformation inversion using interferometric synthetic aperture radar technology to obtain the deformation characteristic index of the target area; Step 3: Estimate the building's construction year based on the preprocessed building outline vector data and annual impermeable surface remote sensing data, and obtain different structural aging levels based on the building's construction year. Step 4: Obtain building height and building footprint characteristics based on the preprocessed building outline vector data; Step 5: Based on the deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics, a comprehensive assessment of the building renewal potential is conducted to obtain the building renewal potential index; Step 6: Obtain the renovation potential result based on the preset potential grading rules and the building renovation potential index, and complete the renovation potential assessment of the existing building.

2. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 1, characterized in that, The multi-source remote sensing data also includes digital elevation model data, and the preprocessing of the multi-source remote sensing data includes: Ensure that all multi-source remote sensing data are in the same geographic coordinate reference system; Radiometric calibration, denoising, and orbital correction are performed on time-series satellite imagery data, and terrain phase removal is performed using digital elevation model data. Perform topology checks and spatial corrections on building outline vector data; Time series consistency verification was performed on the annual remote sensing data of impermeable surfaces.

3. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 2, characterized in that, The deformation characteristic indicators include the average annual settlement rate and cumulative deformation. The deformation characteristic indicators for obtaining the target area include: Based on the preprocessed satellite imagery time series data, an interferometric pair combination is constructed according to the constraints of time and space baselines; An interferogram is obtained by combining the aforementioned interferometric pairs. The interferogram is then subjected to phase unwrapping and atmospheric delay correction to obtain deformation phase information. The pixel-level surface deformation time series is obtained by inverting the deformation phase information using the interferometric synthetic aperture radar technology. The annual average settlement rate and cumulative deformation of individual buildings are calculated from the pixel-level surface deformation time series. The pixel-level surface deformation time series refers to a continuous sequence of cumulative deformation of the surface at each pixel location along the radar line of sight, reconstructed over time, using each pixel as the basic observation unit.

4. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 3, characterized in that, The process of inverting deformation phase information using interferometric synthetic aperture radar (IAPR) technology to obtain a pixel-level surface deformation time series includes: The deformation phase information is subjected to the SBAS time series inversion strategy for the first rate inversion to obtain the initial deformation sequence and residual estimate. Based on the initial deformation sequence and residual estimate, atmospheric phase removal is further carried out, and then the second rate inversion is performed to obtain the pixel-level surface deformation time series.

5. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 3, characterized in that, The calculation of the pixel-level surface deformation time series yields the average annual settlement rate and cumulative deformation of individual buildings, including: The deformation results of the pixel-level surface deformation time series are projected and geocoded, unified to the same geographic coordinate reference system, and spatially superimposed with building outline vector data. Taking individual buildings as statistical units, the deformation pixels within the building area are aggregated and analyzed to calculate and analyze the building's average annual settlement rate and cumulative deformation. The deformed pixel refers to the basic spatial unit that carries the time series of surface deformation at the pixel level, that is, each image pixel with deformation time series information.

6. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 3, characterized in that, The deformation characteristic index also includes the surrounding differential settlement index, which includes: Based on the outline of a single building, a buffer zone of a preset width is constructed. Effective deformation pixels within the building area and the buffer zone are aggregated and statistically analyzed to obtain the deformation statistics of the building interior and the deformation statistics of the surrounding area. The absolute value of the difference between the two is the differential settlement index of the building's perimeter. The effective deformable pixel refers to a deformable pixel with an average temporal coherence of not less than 0.6 and a standard deviation of the deformation rate inversion residual of less than 2 mm.

7. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 1, characterized in that, The construction date of the building is estimated based on the preprocessed building outline vector data and annual remote sensing data of impermeable surfaces. Different structural aging levels are obtained based on the construction date, including: Extract pixels from the annual remote sensing data of impermeable surfaces within the range of the building outline vector data; The proportion of pixels in the remote sensing data of impermeable surfaces within the building area for each year is statistically analyzed to construct a time series of impermeable surfaces. Analyze the time series variation trend of impervious surfaces to identify the time points when the proportion of impervious surfaces first shows a significant increase and then remains stable in subsequent years; The significant increase refers to the increase in the proportion of impermeable surface pixels within the building outline vector data range reaching or exceeding a preset 10% threshold between two adjacent years, and the proportion remaining above 90% of the peak value for at least two years thereafter, which is considered a significant increase. The year corresponding to the aforementioned time point is used to determine the year in which the building was constructed; Based on this, buildings are classified into different structural aging levels according to their construction year.

8. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 1, characterized in that, The building renewal potential is comprehensively evaluated based on deformation characteristic indicators, structural aging level, building height characteristics, and building footprint characteristics to obtain a building renewal potential index, including: The deformation characteristic index, structural aging level, building height characteristics, and building footprint characteristics are standardized by integrating multiple evaluation factors and unifying the index direction to obtain standardized index values. Then, a weighted fusion method is used to comprehensively analyze the standardized index values ​​to obtain the building renewal potential index.

9. The method for assessing the potential for renewal of existing buildings based on multi-source remote sensing data according to claim 8, characterized in that, The integration of multiple evaluation factors includes: It integrates four evaluation factors: building deformation characteristics, structural aging level, building height characteristics, and building footprint characteristics; Among them, the building deformation characteristic index includes one or more of the following: annual average settlement rate, cumulative deformation, and surrounding differential settlement index; the structural aging level includes different structural aging levels; the building height characteristic includes the building height value, which is obtained from the building outline vector data; the building footprint characteristic includes calculating the footprint value by performing area calculation on the building outline polygon based on the building outline vector data, and further classifying the footprint value into footprint levels according to a preset threshold, which are the building footprint characteristics.

10. The method for assessing the potential for redevelopment of existing buildings based on multi-source remote sensing data according to claim 1, characterized in that, The step of obtaining the renovation potential result based on the preset potential grading rules and the building renovation potential index includes: matching the corresponding preset potential grading rules according to the numerical distribution of the building renovation potential index in different building areas to obtain the classification level of different building areas; mapping the classification level to the same geographic coordinate reference system to form the geographic spatial distribution of building renovation potential, and the geographic spatial distribution of building renovation potential is the renovation potential result.

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