A performance evaluation method and system for natural resource financial projects

Abstract

The present invention discloses a performance evaluation method and system for natural resource financial projects, which relate to the technical field of engineering supervision. By decomposing ecological restoration indicators, process targets at different stages are obtained; based on high-altitude remote sensing and low-altitude remote sensing, the project construction process is monitored to obtain the ecological restoration area at the current stage; the ecological restoration area includes the newly added forest area and the newly added cultivated land area; the ecological restoration area at the current stage is compared with the process target to generate a performance report for the current stage. By monitoring through high-altitude remote sensing and low-altitude remote sensing technology, ecological restoration data in a wide area can be quickly obtained, avoiding the inefficient process of manual access and greatly improving the efficiency of data collection. Quantitative analysis of remote sensing data avoids possible errors in manually reporting data, thereby ensuring the accuracy of performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering supervision, and in particular to a natural resource financial project performance evaluation method and system. Background Art

[0002] Performance evaluation, a key component of natural resource management and ecological restoration projects, effectively measures whether projects have achieved their intended ecological restoration goals and helps project managers track and adjust restoration progress and effectiveness. Performance evaluation not only provides a basis for project optimization but also offers valuable experience for future similar projects.

[0003] However, traditional ecological restoration performance evaluation methods usually rely on manual surveys and on-site surveys, facing a series of problems that seriously affect the efficiency and accuracy of the evaluation. First, manual surveys are inefficient. In large-scale ecological restoration projects, the restoration area usually covers a wide geographical range. Investigators need to visit the restoration areas one by one, measure the area, record the data manually, and conduct step-by-step statistics and analysis. This process is very time-consuming, resulting in low overall work efficiency. Secondly, manual surveys often rely on investigators to record on-site and fill in data forms. This process is easily affected by many factors, resulting in the inability to guarantee the accuracy of the data and inaccurate performance evaluation. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of low efficiency and low accuracy mentioned in the above background technology, and to propose a natural resource financial project performance evaluation method and system.

[0005] The first aspect of the present invention provides a method for evaluating the performance of natural resource financial projects, the method comprising:

[0006] Determine the ecological restoration indicators for the target project based on the preset performance goals; the ecological restoration indicators include cultivated land area indicators and forest area indicators;

[0007] According to the preset project plan, the ecological restoration indicators are decomposed to obtain the process goals at different stages;

[0008] Based on high-altitude and low-altitude remote sensing, the project construction process is monitored to obtain the ecological restoration area at the current stage; the ecological restoration area includes the newly added forest area and the newly added cultivated land area;

[0009] Compare the ecological restoration area in the current stage with the progress target and generate a performance report for the current stage.

[0010] Optionally, the project construction process is monitored based on high-altitude remote sensing and low-altitude remote sensing to obtain the ecological restoration area at the current stage, including:

[0011] Obtain a high-altitude remote sensing image of the target area of ​​the current stage as the stage image;

[0012] Comparing the stage image with a reference image to obtain a changed area; the reference image is a high-altitude remote sensing image of the target area before the project construction;

[0013] According to the change areas of the previous stage and the current stage, the stage change area is obtained;

[0014] Obtain low-altitude remote sensing images of the phase-changing area as images to be classified;

[0015] Identify the image to be classified to obtain land cover types and newly added areas in different regions; land cover types include cultivated land, forest land, and others;

[0016] Based on the newly added areas of cultivated land and forest land, the ecological restoration area at the current stage is obtained.

[0017] Optionally, comparing the stage image with the reference image to obtain the changed area includes:

[0018] Preprocessing the stage image to obtain a stage image that is consistent with the reference image space;

[0019] The processed stage image and the reference image are used as inputs of the pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area;

[0020] The image coordinates of the change area are mapped to geographic coordinates to form a geographic polygon of the change area.

[0021] Optionally, the low-altitude remote sensing image of the region where the phase change occurs is acquired as the image to be classified, including:

[0022] Plan the flight path and collection interval of the drone based on the geographic information of the phase change area;

[0023] Conduct drone aerial photography based on flight paths and acquisition intervals to obtain multiple low-altitude remote sensing images;

[0024] According to the sensor parameters and flight data of the UAV, each low-altitude remote sensing image is geometrically calibrated to obtain an orthophoto image set;

[0025] Multiple images are stitched together according to the geographic information of the orthophoto images to obtain the image to be classified.

[0026] Optionally, identifying the image to be classified to obtain the surface cover types of different regions and their newly added areas includes:

[0027] Segmenting the image to be classified according to a preset size to obtain multiple window images;

[0028] The target window image is used as the input of the pre-trained classification detection model to obtain a classification result map of the target window image; the target window image is any window image; each pixel value of the classification result map is a type code corresponding to the pixel;

[0029] splicing the classification result images of the multiple window images to obtain a first result image;

[0030] Extracting a target type pixel set according to each pixel value in the first result image; the target type is forest land or cultivated land;

[0031] Calculate the distance between target type pixels and connect two target type pixels whose distance is less than a preset threshold to obtain a closed area set;

[0032] Based on the closed area set, the newly added area of ​​the target type is calculated.

[0033] A second aspect of the present invention provides a natural resource financial project performance evaluation system, the system comprising:

[0034] An indicator determination module is used to determine the ecological restoration indicators of the target project based on preset performance goals; the ecological restoration indicators include cultivated land area indicators and forest area indicators;

[0035] An indicator decomposition module is used to decompose the ecological restoration indicators according to the preset project plan to obtain the progress goals at different stages;

[0036] A monitoring module is used to monitor the project construction process based on high-altitude and low-altitude remote sensing to obtain the ecological restoration area at the current stage; the ecological restoration area includes the newly added forest area and the newly added cultivated land area;

[0037] The data analysis module is used to compare the ecological restoration area in the current stage with the process target and generate a performance report for the current stage.

[0038] Optionally, the monitoring module includes:

[0039] The first data acquisition module is used to acquire a high-altitude remote sensing image of the target area in the current stage as a stage image;

[0040] a change detection module for comparing the stage image with a reference image to obtain a changed area; the reference image is a high-altitude remote sensing image of the target area before project construction;

[0041] A new change module is added to obtain the stage change area based on the change areas of the previous stage and the current stage;

[0042] The second data acquisition module is used to acquire low-altitude remote sensing images of the phase change area as images to be classified;

[0043] A classification calculation module is used to identify the image to be classified and obtain the land cover types and newly added areas of different regions; the land cover types include cultivated land, forest land and others;

[0044] The area accumulation module is used to obtain the ecological restoration area in the current stage based on the newly added areas of cultivated land and forest land.

[0045] Optionally, the change detection module includes:

[0046] a registration module, configured to pre-process the stage image to obtain a stage image that is spatially consistent with the reference image;

[0047] An image detection module is used to use the processed stage image and the reference image as input to a pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area;

[0048] The geographic mapping module is used to map the image coordinates of the change area to geographic coordinates to form a geographic polygon of the change area.

[0049] Optionally, the second data acquisition module includes:

[0050] The path planning module is used to plan the flight path and collection interval of the UAV based on the geographical information of the stage-changing area;

[0051] The data acquisition module is used to perform drone aerial photography based on the flight path and acquisition interval to obtain multiple low-altitude remote sensing images;

[0052] The calibration and alignment module is used to perform geometric calibration on each low-altitude remote sensing image based on the UAV’s sensor parameters and flight data to obtain an orthophoto image set;

[0053] The stitching module is used to stitch multiple images according to the geographic information of the orthophoto image to obtain the image to be classified.

[0054] Optionally, the classification calculation module includes:

[0055] An image segmentation module is used to segment the image to be classified according to a preset size to obtain multiple window images;

[0056] A classification recognition module is configured to use a target window image as input to a pre-trained classification detection model to obtain a classification result map of the target window image; the target window image is any window image; and each pixel value of the classification result map is a type code corresponding to the pixel;

[0057] A result synthesis module is used to splice the classification result images of multiple window images to obtain a first result image;

[0058] A pixel integration module is used to extract a target type pixel set according to each pixel value in the first result image; the target type is forest land or cultivated land;

[0059] The region classification module is used to calculate the distance between target type pixels and connect two target type pixels whose distance is less than a preset threshold to obtain a closed region set;

[0060] The area calculation module is used to calculate the newly added area of ​​the target type according to the closed area set.

[0061] Beneficial effects of the present invention:

[0062] The present invention proposes a performance evaluation method for natural resource financial projects, which includes: determining ecological restoration indicators of target projects based on preset performance targets; the ecological restoration indicators include cultivated land area indicators and forest area indicators; decomposing the ecological restoration indicators according to a preset project plan to obtain process targets at different stages; monitoring the project construction process based on high-altitude remote sensing and low-altitude remote sensing to obtain the ecological restoration area at the current stage; the ecological restoration area includes newly added forest area and newly added cultivated land area; comparing the ecological restoration area at the current stage with the process target to generate a performance report for the current stage.

[0063] Monitoring through high-altitude and low-altitude remote sensing technologies allows for rapid acquisition of ecological restoration data across a wide area, avoiding the inefficient process of manual access and significantly improving data collection efficiency. Quantitative analysis of remote sensing data avoids potential errors associated with manual reporting, thereby ensuring the accuracy of performance evaluations. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below with reference to the accompanying drawings.

[0065] Figure 1 A flowchart of a method for evaluating the performance of natural resource financial projects is provided for an embodiment of the present invention;

[0066] Figure 2 An architectural diagram of a natural resource financial project performance evaluation system is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0068] The embodiment of the present invention provides a method for evaluating the performance of natural resource financial projects. Figure 1 , Figure 1 A flowchart of a method for evaluating the performance of natural resource financial projects provided by an embodiment of the present invention. The method includes the following steps:

[0069] S101. Determine the ecological restoration indicators for the target project based on the preset performance goals.

[0070] S102: According to the preset project plan, the ecological restoration indicators are decomposed to obtain the process goals at different stages.

[0071] S103, based on high-altitude remote sensing and low-altitude remote sensing, monitor the project construction process and obtain the ecological restoration area at the current stage.

[0072] S104, compare the ecological restoration area of ​​the current stage with the process target and generate a performance report for the current stage.

[0073] Among them, ecological restoration indicators include cultivated land area indicators and forest area indicators; the ecological restoration area includes newly added forest area and newly added cultivated land area.

[0074] A performance evaluation method for natural resource fiscal projects, based on an embodiment of the present invention, uses high-altitude and low-altitude remote sensing technologies to rapidly acquire ecological restoration data across a wide area, avoiding the inefficient process of manual access and significantly improving data collection efficiency. Quantitative analysis of remote sensing data avoids errors that may occur with manual data reporting, thereby ensuring the accuracy of performance evaluations.

[0075] In one implementation approach, by setting clear performance goals and breaking them down into phased progress targets, we can ensure that every step of project implementation has clear standards and evaluation criteria. Furthermore, using high-altitude and low-altitude remote sensing technologies for monitoring can provide accurate data support, improving the scientific nature and accuracy of project performance evaluations and enabling timely identification and adjustment of deviations from predetermined targets.

[0076] In one implementation, periodic comparisons and report generation ensure timely feedback during project execution, thereby optimizing management and execution. Performance reports for each phase clearly reflect project progress, increasing transparency in project implementation.

[0077] In one embodiment, step S103 includes:

[0078] Step 1: Obtain a high-altitude remote sensing image of the target area in the current stage as the stage image.

[0079] Step 2: Compare the stage image with the baseline image to obtain the changed area.

[0080] Step 3: Get the stage change area based on the change area of ​​the previous stage and the current stage.

[0081] Step 4: Obtain low-altitude remote sensing images of the phase change area as images to be classified.

[0082] Step 5: Identify the images to be classified and obtain the land cover types and newly added areas of different regions.

[0083] Step six: Based on the newly added areas of cultivated land and forest land, obtain the ecological restoration area at the current stage.

[0084] Among them, the baseline image is the high-altitude remote sensing image of the target area before the project construction; the surface cover types include cultivated land, forest land and others.

[0085] In one implementation, remote sensing technology can cover a wider area than traditional ground survey methods, while reducing labor costs and time consumption. High-altitude remote sensing, using satellite remote sensing, can clearly identify areas of change at different stages by comparing high-altitude remote sensing images with baseline images. This provides a comprehensive understanding of changes in the project area and effectively reduces the exploration area of ​​drones, thereby reducing energy consumption and computing resources.

[0086] In one implementation, the introduction of low-altitude remote sensing imagery enables more refined identification and classification of areas of change, clearly distinguishing between cultivated land, forested land, and other land cover types, and calculating the newly added area. By quantitatively measuring the newly added area of ​​cultivated land and forested land, project progress can be effectively assessed.

[0087] In one embodiment, the stage image and the reference image are compared to obtain the changed area, including:

[0088] Step 1: Preprocess the stage image to obtain a stage image that is consistent with the reference image space.

[0089] In step 2, the processed stage image and the reference image are used as inputs of the pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area.

[0090] Step three: Map the image coordinates of the changed area to geographic coordinates to form a geographic polygon of the changed area.

[0091] In one implementation, the stage image is preprocessed to align it spatially with the baseline image, ensuring accurate image comparison. This step eliminates errors caused by differences in image resolution, angle, or time, providing a reliable data foundation for subsequent change detection.

[0092] In one implementation, the change detection model can be a convolutional neural network (CNN) model. By using a pre-trained change detection model to compare images, changed areas can be automatically identified. This approach not only improves detection efficiency but also reduces the errors and complexity of manual operations, enabling the location and extent of changed areas to be quickly and accurately determined.

[0093] In one embodiment, obtaining a low-altitude remote sensing image of a phase change region as an image to be classified includes:

[0094] Step 1: Plan the flight path and collection interval of the drone based on the geographical information of the stage change area.

[0095] Step 2: Use drones to take aerial photos based on the flight path and acquisition interval to obtain multiple low-altitude remote sensing images.

[0096] Step three: Based on the sensor parameters and flight data of the UAV, each low-altitude remote sensing image is geometrically calibrated to obtain an orthophoto image set.

[0097] Step 4: Multiple images are stitched together based on the geographic information of the orthophoto images to obtain the image to be classified.

[0098] In one implementation, the drone's flight path should cover the entire change area, with redundancy at the edges. The acquisition interval should meet a minimum overlap ratio between images, such as 30%, to ensure that the resulting image set covers the entire change area.

[0099] In one implementation, drone aerial photography can provide higher-resolution images and greater detail capture capabilities than satellite remote sensing, thereby improving the precision and accuracy of classification.

[0100] In one implementation, overlapping areas can be eliminated by stitching, thus avoiding repeated calculations caused by classifying a single image.

[0101] In one embodiment, the image to be classified is identified to obtain the land cover types and newly added areas of different regions, including:

[0102] Step 1: Segment the image to be classified according to a preset size to obtain multiple window images.

[0103] Step 2: Use the target window image as the input of the pre-trained classification detection model to obtain the classification result map of the target window image.

[0104] Step three: splice the classification result images of multiple window images to obtain a first result image.

[0105] Step 4: extract a target type pixel set based on each pixel value in the first result image.

[0106] Step 5: Calculate the distance between target type pixels, connect two target type pixels whose distance is less than a preset threshold, and obtain a closed area set.

[0107] Step 6: Calculate the newly added area of ​​the target type based on the closed area set.

[0108] Among them, the target window image is any window image; each pixel value of the classification result image is the type code corresponding to the pixel; the target type is forest land or cultivated land.

[0109] In one implementation, the classification detection model can be a UNet model. The UNet model is a classic deep learning structure that is particularly suitable for image segmentation tasks. Through this model, the image to be classified can be accurately segmented according to different surface cover types.

[0110] In one implementation, the classification result maps from multiple window images are stitched together to create a first result map, ensuring consistent classification information across different regions of the image. The stitched result map provides a complete spatial view, more clearly reflecting the different land cover types and their distribution across the entire image.

[0111] In one implementation, the distance between pixels of the target type is calculated, and pixels with distances less than a preset threshold are connected to form a closed region set. This method effectively identifies connected regions in an image. This clearly defines each type of continuous region, facilitating accurate area calculations.

[0112] The embodiment of the present invention provides a natural resource financial project performance evaluation system. Figure 2 , Figure 2 This is an architecture diagram of a natural resource financial project performance evaluation system provided by an embodiment of the present invention. The system includes:

[0113] The indicator determination module is used to determine the ecological restoration indicators of the target project based on the preset performance goals.

[0114] The indicator decomposition module is used to decompose ecological restoration indicators according to the preset project plan to obtain process goals at different stages.

[0115] The monitoring module is used to monitor the project construction process based on high-altitude remote sensing and low-altitude remote sensing to obtain the ecological restoration area at the current stage.

[0116] The data analysis module is used to compare the ecological restoration area in the current stage with the process target and generate a performance report for the current stage.

[0117] Among them, ecological restoration indicators include cultivated land area indicators and forest area indicators; the ecological restoration area includes newly added forest area and newly added cultivated land area.

[0118] A natural resource fiscal project performance evaluation system, based on an embodiment of the present invention, uses high-altitude and low-altitude remote sensing technologies to rapidly acquire ecological restoration data across a wide area, avoiding the inefficient process of manual access and significantly improving data collection efficiency. Quantitative analysis of remote sensing data avoids errors that may occur with manual data reporting, thereby ensuring the accuracy of performance evaluations.

[0119] In one embodiment, the monitoring module includes:

[0120] The first data acquisition module is used to acquire a high-altitude remote sensing image of the target area in the current stage as a stage image;

[0121] The change detection module is used to compare the stage image with the baseline image to obtain the changed area; the baseline image is a high-altitude remote sensing image of the target area before the project construction.

[0122] A new change module is added to obtain the stage change area based on the change areas of the previous stage and the current stage.

[0123] The second data acquisition module is used to acquire low-altitude remote sensing images of the phase change area as images to be classified.

[0124] The classification calculation module is used to identify the images to be classified and obtain the surface cover types and newly added areas of different regions; the surface cover types include cultivated land, forest land and others.

[0125] The area accumulation module is used to obtain the ecological restoration area in the current stage based on the newly added areas of cultivated land and forest land.

[0126] In one embodiment, the change detection module includes:

[0127] The registration module is used to preprocess the stage image to obtain a stage image that is consistent with the reference image space.

[0128] The image detection module is used to take the processed stage image and the reference image as the input of the pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area.

[0129] The geographic mapping module is used to map the image coordinates of the change area to geographic coordinates to form a geographic polygon of the change area.

[0130] In one embodiment, the second data acquisition module includes:

[0131] The path planning module is used to plan the flight path and collection interval of the UAV based on the geographical information of the stage-changing area.

[0132] The data acquisition module is used to perform drone aerial photography according to the flight path and acquisition interval to obtain multiple low-altitude remote sensing images.

[0133] The calibration and alignment module is used to perform geometric calibration on each low-altitude remote sensing image according to the sensor parameters and flight data of the UAV to obtain an orthophoto image set.

[0134] The stitching module is used to stitch multiple images according to the geographic information of the orthophoto image to obtain the image to be classified.

[0135] In one embodiment, the classification calculation module includes:

[0136] The image segmentation module is used to segment the image to be classified according to a preset size to obtain multiple window images.

[0137] The classification recognition module is used to take the target window image as the input of the pre-trained classification detection model to obtain a classification result map of the target window image; the target window image is any window image; each pixel value of the classification result map is the type code corresponding to the pixel.

[0138] The result synthesis module is used to splice the classification result images of multiple window images to obtain a first result image.

[0139] The pixel integration module is used to extract a target type pixel set according to each pixel value in the first result image; the target type is forest land or cultivated land.

[0140] The region classification module is used to calculate the distance between target type pixels and connect two target type pixels whose distance is less than a preset threshold to obtain a closed region set.

[0141] The area calculation module is used to calculate the newly added area of ​​the target type based on the closed area set.

[0142] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A performance evaluation method for natural resource financial projects, characterized in that: The method comprises: Determine the ecological restoration indicators for the target project based on the preset performance goals; the ecological restoration indicators include cultivated land area indicators and forest area indicators; According to the preset project plan, the ecological restoration indicators are decomposed to obtain the process goals at different stages; Based on high-altitude and low-altitude remote sensing, the project construction process is monitored to obtain the ecological restoration area at the current stage; the ecological restoration area includes the newly added forest area and the newly added cultivated land area; Compare the ecological restoration area in the current phase with the progress target and generate a performance report for the current phase; Based on high-altitude and low-altitude remote sensing, the project construction process is monitored and the ecological restoration area at the current stage is as follows: Obtain a high-altitude remote sensing image of the target area of ​​the current stage as the stage image; Comparing the stage image with a reference image to obtain a changed area; the reference image is a high-altitude remote sensing image of the target area before the project construction; According to the change areas of the previous stage and the current stage, the stage change area is obtained; Obtain low-altitude remote sensing images of the phase-changing area as images to be classified; Identify the image to be classified to obtain land cover types and newly added areas in different regions; land cover types include cultivated land and forest land; Based on the newly added areas of cultivated land and forest land, the ecological restoration area at the current stage is obtained; The identification of the image to be classified to obtain the surface coverage types of different regions and their newly added areas includes: Segmenting the image to be classified according to a preset size to obtain multiple window images; The target window image is used as the input of the pre-trained classification detection model to obtain a classification result map of the target window image; the target window image is any window image; each pixel value of the classification result map is a type code corresponding to the pixel; splicing the classification result images of the multiple window images to obtain a first result image; Extracting a target type pixel set according to each pixel value in the first result image; the target type is forest land or cultivated land; Calculate the distance between target type pixels and connect two target type pixels whose distance is less than a preset threshold to obtain a closed area set; The newly added area of ​​the target type is calculated based on the closed area set.

2. A natural resource financial project performance evaluation method according to claim 1, characterized in that: The step of comparing the stage image with the reference image to obtain the changed area comprises: Preprocessing the stage image to obtain a stage image that is consistent with the reference image space; The processed stage image and the reference image are used as inputs of the pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area; The image coordinates of the change area are mapped to geographic coordinates to form a geographic polygon of the change area.

3. A natural resource financial project performance evaluation method according to claim 1, characterized in that: The low-altitude remote sensing images of the region where the phase change occurs are acquired, and the images to be classified include: Plan the flight path and collection interval of the drone based on the geographic information of the phase change area; Conduct drone aerial photography based on flight paths and acquisition intervals to obtain multiple low-altitude remote sensing images; According to the sensor parameters and flight data of the UAV, each low-altitude remote sensing image is geometrically calibrated to obtain an orthophoto image set; Multiple images are stitched together according to the geographic information of the orthophoto images to obtain the image to be classified.

4. A natural resource financial project performance evaluation system, characterized in that: The system comprises: An indicator determination module is used to determine the ecological restoration indicators of the target project based on preset performance goals; the ecological restoration indicators include cultivated land area indicators and forest area indicators; An indicator decomposition module is used to decompose the ecological restoration indicators according to the preset project plan to obtain the progress goals at different stages; A monitoring module is used to monitor the project construction process based on high-altitude and low-altitude remote sensing to obtain the ecological restoration area at the current stage; the ecological restoration area includes the newly added forest area and the newly added cultivated land area; Data analysis module, used to compare the ecological restoration area in the current stage with the progress target and generate a performance report for the current stage; The monitoring module includes: The first data acquisition module is used to acquire a high-altitude remote sensing image of the target area in the current stage as a stage image; a change detection module for comparing the stage image with a reference image to obtain a changed area; the reference image is a high-altitude remote sensing image of the target area before project construction; A new change module is added to obtain the stage change area based on the change areas of the previous stage and the current stage; The second data acquisition module is used to acquire low-altitude remote sensing images of the phase change area as images to be classified; A classification calculation module is used to identify the image to be classified and obtain the land cover types and newly added areas of different regions; the land cover types include cultivated land and forest land; The area accumulation module is used to obtain the ecological restoration area of ​​the current stage based on the newly added areas of cultivated land and forest land; The classification calculation module includes: An image segmentation module is used to segment the image to be classified according to a preset size to obtain multiple window images; A classification recognition module is configured to use a target window image as input to a pre-trained classification detection model to obtain a classification result map of the target window image; the target window image is any window image; and each pixel value of the classification result map is a type code corresponding to the pixel; A result synthesis module is used to splice the classification result images of multiple window images to obtain a first result image; A pixel integration module is used to extract a target type pixel set according to each pixel value in the first result image; the target type is forest land or cultivated land; The region classification module is used to calculate the distance between target type pixels and connect two target type pixels whose distance is less than a preset threshold to obtain a closed region set; The area calculation module is used to calculate the newly added area of ​​the target type according to the closed area set.

5. A natural resource financial project performance evaluation system according to claim 4, characterized in that: The change detection module includes: a registration module, configured to pre-process the stage image to obtain a stage image that is spatially consistent with the reference image; An image detection module is used to use the processed stage image and the reference image as input to a pre-trained change detection model to obtain a binary mask image and determine the image coordinates of the changed area; The geographic mapping module is used to map the image coordinates of the change area to geographic coordinates to form a geographic polygon of the change area.

6. A natural resource financial project performance evaluation system according to claim 4, characterized in that: The second data acquisition module includes: The path planning module is used to plan the flight path and collection interval of the UAV based on the geographical information of the stage-changing area; The data acquisition module is used to perform drone aerial photography based on the flight path and acquisition interval to obtain multiple low-altitude remote sensing images; The calibration and alignment module is used to perform geometric calibration on each low-altitude remote sensing image based on the UAV’s sensor parameters and flight data to obtain an orthophoto image set; The stitching module is used to stitch multiple images according to the geographic information of the orthophoto image to obtain the image to be classified.

Images