Urban environment detection and comprehensive evaluation method based on remote sensing data

Abstract

The present invention discloses a method for urban environment detection and comprehensive evaluation based on remote sensing data, which relates to the technical field of urban environment detection, and includes a remote sensing platform, a data processing unit and an environment determination unit. The output end of the remote sensing platform is connected to a cloud server, and the output end of the cloud server is connected to an environment detection center. The data processing unit is electrically connected to the output end of the environment detection center, and the output end of the remote sensing data acquisition module is electrically connected to a remote sensing image analysis and processing module. The invention can perform atmospheric environment detection, vegetation environment detection, and water environment detection on the urban environment. While detecting, it can also draw a time series distribution diagram of the environmental pollution status of the city according to the time series relationship to reflect the changes in the urban environment, and can predict the environment according to the environmental detection data, so as to timely treat the pollution of the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of urban environment detection, and in particular to an urban environment detection and comprehensive evaluation method based on remote sensing data. Background Art

[0002] Remote sensing technology is a comprehensive technology that uses various sensing instruments to collect, process, and finally image the electromagnetic wave information radiated and reflected by distant targets based on the theory of electromagnetic waves, so as to detect and identify various ground scenes. Remote sensing can be applied to the field of urban environmental monitoring. Urban environmental monitoring can monitor the quality and noise, electromagnetic waves, and radioactivity of urban atmosphere, water bodies, soil, organisms, etc., determine the pollution sources and their impact range, channels, and hazards; regularly observe urban pollution sources, grasp the dynamic changes of pollution, and find and analyze the causes.

[0003] The existing urban environment detection and comprehensive assessment methods are not comprehensive enough when detecting urban environment. They mostly use targeted sensor detection methods to conduct single detection and assessment of air quality or water bodies, and there is no corresponding detection and prediction of the time-series changes of the environment.

[0004] In view of this, the existing structure and deficiencies are studied and improved, and a method for urban environment detection and comprehensive evaluation based on remote sensing data is proposed. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an urban environment detection and comprehensive evaluation method based on remote sensing data, which solves the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for urban environment detection and comprehensive evaluation based on remote sensing data, including a remote sensing platform, a data processing unit and an environment determination unit, the output end of the remote sensing platform is connected to a cloud server, and the output end of the cloud server is connected to an environment detection center, the data processing unit is electrically connected to the output end of the environment detection center, and the data processing unit includes a remote sensing data acquisition module, a remote sensing image analysis and processing module, a remote sensing object recognition and classification module, an environment detection data extraction module and a data classification storage module, the output end of the remote sensing data acquisition module is electrically connected to the remote sensing image analysis and processing module, and the output end of the remote sensing image analysis and processing module is electrically connected to the remote sensing object recognition and classification module, the output end of the remote sensing object recognition and classification module is electrically connected to the environment detection data extraction module, and the output end of the environment detection data extraction module is electrically connected to the data classification storage module, the environment determination unit is electrically connected to the output end of the data processing unit, and the output end of the environment determination unit is electrically connected to the abnormal alarm module, and the environment determination unit includes an environment assessment module and an environment prediction module.

[0007] Furthermore, the environmental monitoring center includes an atmospheric environment detection unit, a vegetation environment detection unit and a water environment detection unit, and the atmospheric environment detection unit, the vegetation environment detection unit and the water environment detection unit are connected in parallel. The atmospheric environment detection unit, the vegetation environment detection unit and the water environment detection unit are respectively used to detect the atmospheric environment, vegetation environment and water environment in the urban environment.

[0008] Furthermore, the remote sensing data acquisition module is used to collect and organize remote sensing data of different periods according to the time series relationship, and then convert the digital remote sensing data into spatial resolution images. The remote sensing image analysis and processing module is used to analyze and process the remote sensing images. The analysis and processing process includes row radiation correction, grayscale image conversion, smoothing filtering processing, and then using the closing operation in mathematical morphology to fill the gaps in the contour, integrate the image contour, and obtain a clearer and more complete remote sensing image.

[0009] Furthermore, the remote sensing object recognition and classification module is used to perform object classification detection on the image, extract urban water bodies according to the reflection wave characteristics of the objects, and extract urban vegetation using the normalized difference vegetation index. Then, a multi-spectral processing system is used to perform semi-quantitative estimation of the situation of atmospheric pollutants, thereby realizing the detection of the atmospheric environment, vegetation environment and water environment.

[0010] Furthermore, the environmental monitoring data extraction module is used to extract urban water body data, urban vegetation data and urban air pollutant data based on remote sensing object identification and classification, and the data classification storage module is used to classify and store the extracted data in the remote sensing database of the environmental monitoring center.

[0011] Furthermore, the environmental assessment module includes a current environmental assessment module, a comparative detection assessment module and an assessment result output module, and the output ends of the current environmental assessment module and the comparative detection assessment module are electrically connected to the assessment result output module.

[0012] Furthermore, the environmental assessment module is used to detect and evaluate the current urban environmental data, the comparative detection and evaluation module is used to detect and evaluate the changes in the urban environment based on the time series relationship, and the evaluation result output module is used to output the evaluation results and classify the urban environmental conditions according to the urban environment detection results.

[0013] Furthermore, the environmental prediction module includes a data collection module, an environmental model training module, an environmental model verification module and an environmental prediction output module, the output end of the data collection module is electrically connected to the environmental model training module, and the output end of the environmental model training module is electrically connected to the environmental model verification module, and the output end of the environmental model verification module is electrically connected to the environmental prediction output module.

[0014] Furthermore, the current environmental assessment module includes an environmental assessment model construction module, an evaluation data substitution module, an evaluation result calculation module and a visualization analysis module. The output end of the environmental assessment model construction module is electrically connected to the evaluation data substitution module, and the output end of the evaluation data substitution module is electrically connected to the evaluation result calculation module, and the output end of the evaluation result calculation module is electrically connected to the visualization analysis module.

[0015] Furthermore, the contrast detection and evaluation module includes a change feature extraction module, an image registration detection module, a timing distribution map drawing module and a data structured analysis module, the output end of the change feature extraction module is electrically connected to the image registration detection module, and the output end of the image registration detection module is electrically connected to the timing distribution map drawing module, and the output end of the timing distribution map drawing module is electrically connected to the data structured analysis module.

[0016] The present invention provides a method for urban environment detection and comprehensive evaluation based on remote sensing data, which has the following beneficial effects:

[0017] This urban environment monitoring and comprehensive assessment method based on remote sensing data can conduct atmospheric environment monitoring, vegetation environment monitoring, and water environment monitoring of the urban environment. While monitoring, it can also draw a time-series distribution map of the city's environmental pollution status based on the time series relationship to reflect changes in the urban environment. It can also predict the environment based on the environmental monitoring data to facilitate timely pollution control of the environment.

[0018] 1. The urban environment detection and comprehensive evaluation method based on remote sensing data is provided with a data processing unit. The remote sensing data acquisition module collects and organizes remote sensing data of different periods according to the time series relationship, and then converts the digital remote sensing data into spatial resolution images. The remote sensing image analysis and processing module analyzes and processes the remote sensing images. The analysis and processing process includes line radiation correction, grayscale image conversion, smoothing filter processing, and then uses the closed operation in mathematical morphology to fill the gaps of the contour, integrate the image contour, and obtain a relatively clear and complete remote sensing image; the remote sensing ground object recognition and classification module performs ground object classification detection on the image, extracts urban water bodies according to the ground object reflection wave spectrum characteristics, and extracts urban vegetation by using the normalized difference vegetation index, and then uses the multi-spectral processing system to semi-quantitatively estimate the situation of atmospheric pollutants. The environmental detection data extraction module extracts urban water body data, urban vegetation data and urban atmospheric pollutant data on the basis of remote sensing ground object recognition and classification, including surface temperature and humidity data, pH value, smoke concentration, vegetation coverage, water temperature and water level data, etc. The data classification storage module classifies and stores the extracted data in the remote sensing database of the environmental detection center.

[0019] 2. The urban environment detection and comprehensive evaluation method based on remote sensing data is equipped with an environmental detection center. The atmospheric environment detection unit, the vegetation environment detection unit and the water environment detection unit detect the atmospheric environment, the vegetation environment and the water environment in the urban environment respectively. The abnormal alarm module can give a timely alarm when the environmental judgment unit detects and predicts abnormal environmental data or environmental pollution, so as to facilitate timely maintenance of the urban environment. The environmental evaluation module detects and evaluates the current urban environmental data. The comparative detection and evaluation module detects and evaluates the changes in the urban environment according to the time series relationship. The evaluation result output module outputs the evaluation results and grades the urban environmental conditions according to the urban environment detection results.

[0020] 3. The urban environment detection and comprehensive assessment method based on remote sensing data is provided with a current environment assessment module. The environmental assessment model construction module can respectively construct environmental assessment models for atmospheric environment detection, vegetation environment detection, and water environment detection, and assign different weights to different detection factors. The evaluation data substitution module can extract the valid data inside the remote sensing database of the environmental detection center and substitute it into the environmental assessment model. The evaluation result calculation module can output the evaluation results of the environmental assessment model. The visualization analysis module can display the environmental evaluation results in the form of visual charts.

[0021] 4. The urban environment detection and comprehensive evaluation method based on remote sensing data is provided with a comparative detection and evaluation module. The change feature extraction module can extract the time change features of the ground environment of the remote sensing image according to the time change features. The image registration detection module can obtain the corresponding image space coordinate transformation parameters through the over-matched feature point pairs and determine the environmental change plots. The time series distribution map drawing module can draw the time series distribution map of the city's environmental pollution status according to the time series relationship to reflect the changes in the urban environment. The data structured analysis module can parse the urban environmental change data and express it in a pre-structured form to achieve a high degree of logic of the data.

[0022] 5. The urban environment detection and comprehensive assessment method based on remote sensing data is provided with an environmental prediction module. The data collection module can collect valid data in the remote sensing database of the environmental detection center. The environmental model training module can be trained according to historical remote sensing data. The machine learning methods for model training include but are not limited to KNN, decision tree, logistic regression, support vector machine, neural network, deep neural network. After selecting the best algorithm, the model training is continuously performed. The environmental model verification module can verify the training results of the environmental model training module according to the actual environmental development conditions until the output result error meets the requirements, and then continuously improve the prediction model, so that the final environmental prediction output module can output the predicted environmental data results according to the current environmental data to predict the environmental development. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A system flow diagram of a method for urban environment detection and comprehensive evaluation based on remote sensing data according to the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of a data processing unit of a method for urban environment detection and comprehensive evaluation based on remote sensing data of the present invention;

[0025] Figure 3 A schematic diagram of the structure of an environment determination unit of a method for urban environment detection and comprehensive evaluation based on remote sensing data of the present invention;

[0026] Figure 4 It is a schematic diagram of the structure of an environmental assessment module of an urban environment detection and comprehensive assessment method based on remote sensing data of the present invention;

[0027] Figure 5 The present invention is a schematic diagram of the structure of an environmental prediction module of an urban environment detection and comprehensive evaluation method based on remote sensing data.

[0028] Figure: 1, remote sensing platform; 2, cloud server; 3, environmental detection center; 301, atmospheric environment detection unit; 302, vegetation environment detection unit; 303, water environment detection unit; 4, data processing unit; 401, remote sensing data acquisition module; 402, remote sensing image analysis and processing module; 403, remote sensing object recognition and classification module; 404, environmental detection data extraction module; 405, data classification and storage module; 5, environmental judgment unit; 6, abnormal alarm module; 7, environmental assessment module; 8, environmental prediction module; 801, data collection module Collection module; 802, environmental model training module; 803, environmental model verification module; 804, environmental prediction output module; 9, current environment assessment module; 901, environmental assessment model construction module; 902, evaluation data substitution module; 903, evaluation result calculation module; 904, visualization analysis module; 10, contrast detection and evaluation module; 1001, change feature extraction module; 1002, image registration detection module; 1003, time series distribution map drawing module; 1004, data structured analysis module; 11, evaluation result output module. DETAILED DESCRIPTION

[0029] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0030] See also Figures 1 to 5 The present invention provides a technical solution: a method for urban environment detection and comprehensive evaluation based on remote sensing data, comprising a remote sensing platform 1, a data processing unit 4 and an environment determination unit 5, the output end of the remote sensing platform 1 is connected to a cloud server 2, and the output end of the cloud server 2 is connected to an environment detection center 3, the data processing unit 4 is electrically connected to the output end of the environment detection center 3, and the data processing unit 4 comprises a remote sensing data acquisition module 401, a remote sensing image analysis and processing module 402, a remote sensing object recognition and classification module 403, an environment detection data extraction module 404 and a data classification storage module 405, the output end of the remote sensing data acquisition module 401 is electrically connected to the remote sensing image analysis and processing module 402, and the output end of the remote sensing image analysis and processing module 402 is electrically connected to the remote sensing object recognition and classification module 403, the output end of the remote sensing object recognition and classification module 403 is electrically connected to the environment detection data extraction module 404, and the output end of the environment detection data extraction module 404 is electrically connected to the data classification storage module 405.

[0031] The specific operation is as follows: the remote sensing data acquisition module 401 collects and organizes remote sensing data of different periods according to the time series relationship, and then converts the digital remote sensing data into spatial resolution images; the remote sensing image analysis and processing module 402 analyzes and processes the remote sensing images, and the analysis and processing process includes line radiation correction, grayscale image conversion, smoothing filter processing, and then uses the closing operation in mathematical morphology to fill the gaps in the contour, integrate the image contour, and obtain a relatively clear and complete remote sensing image; the remote sensing ground object recognition and classification module 403 performs ground object classification detection on the image, extracts urban water bodies according to the ground object reflection wave spectrum characteristics, and extracts urban vegetation by using the normalized difference vegetation index, and then uses the multi-spectral processing system to semi-quantitatively estimate the situation of atmospheric pollutants; the environmental detection data extraction module 404 extracts urban water body data, urban vegetation data and urban atmospheric pollutant data on the basis of remote sensing ground object recognition and classification, including surface temperature and humidity data, pH value, smoke concentration, vegetation coverage, water temperature and water level data, etc.; the data classification storage module 405 classifies and stores the extracted data in the remote sensing database on the environmental detection center 3.

[0032] See also Figure 1 The environmental detection center 3 includes an atmospheric environment detection unit 301, a vegetation environment detection unit 302 and a water environment detection unit 303, and the atmospheric environment detection unit 301, the vegetation environment detection unit 302 and the water environment detection unit 303 are connected in parallel. The atmospheric environment detection unit 301, the vegetation environment detection unit 302 and the water environment detection unit 303 detect the atmospheric environment, vegetation environment and water environment in the urban environment respectively.

[0033] See also Figure 1 , Figure 3 and Figure 4 The environment determination unit 5 is electrically connected to the output end of the data processing unit 4, and the output end of the environment determination unit 5 is electrically connected to the abnormal alarm module 6. The environment determination unit 5 includes an environment assessment module 7 and an environment prediction module 8, and the environment assessment module 7 includes a current environment assessment module 9, a comparative detection and assessment module 10 and an evaluation result output module 11. The output ends of the current environment assessment module 9 and the comparative detection and assessment module 10 are electrically connected to the evaluation result output module 11.

[0034] The specific operations are as follows: the abnormal alarm module 6 can issue a timely alarm when the environmental judgment unit 5 detects and predicts abnormal environmental data or environmental pollution, so as to facilitate timely maintenance of the urban environment; the environmental assessment module 7 detects and evaluates the current urban environmental data; the comparative detection and evaluation module 10 detects and evaluates the changes in the urban environment according to the time series relationship; the evaluation result output module 11 outputs the evaluation results and grades the urban environmental conditions according to the urban environment detection results.

[0035] See also Figure 5 , the environment prediction module 8 includes a data collection module 801, an environment model training module 802, an environment model verification module 803 and an environment prediction output module 804, the output end of the data collection module 801 is electrically connected to the environment model training module 802, the output end of the environment model training module 802 is electrically connected to the environment model verification module 803, and the output end of the environment model verification module 803 is electrically connected to the environment prediction output module 804;

[0036] The specific operations are as follows: the data collection module 801 can collect valid data in the remote sensing database of the environmental monitoring center 3; the environmental model training module 802 can be trained based on historical remote sensing data; the machine learning methods for model training include but are not limited to KNN, decision tree, logistic regression, support vector machine, neural network, and deep neural network; after selecting the best algorithm, the model training is continued; the environmental model verification module 803 can verify the training results of the environmental model training module 802 according to the actual environmental development conditions until the output result error meets the requirements, and then continuously improve the prediction model, so that the final environmental prediction output module 804 can output the predicted environmental data results based on the current environmental data to predict the environmental development.

[0037] See also Figure 4 The current environmental assessment module 9 includes an environmental assessment model construction module 901, an assessment data input module 902, an assessment result calculation module 903 and a visualization analysis module 904. The output end of the environmental assessment model construction module 901 is electrically connected to the assessment data input module 902, and the output end of the assessment data input module 902 is electrically connected to the assessment result calculation module 903, and the output end of the assessment result calculation module 903 is electrically connected to the visualization analysis module 904;

[0038] The specific operations are as follows: the environmental assessment model construction module 901 can respectively construct environmental assessment models for atmospheric environment detection, vegetation environment detection, and water environment detection, and assign different weights to different detection factors; the evaluation data substitution module 902 can extract valid data inside the remote sensing database of the environmental detection center 3 and substitute it into the environmental assessment model; the evaluation result calculation module 903 can output the evaluation results of the environmental assessment model; and the visualization analysis module 904 can display the environmental assessment results in the form of visual charts.

[0039] See also Figure 4The contrast detection and evaluation module 10 includes a change feature extraction module 1001, an image registration detection module 1002, a time series distribution map drawing module 1003 and a data structured analysis module 1004. The output end of the change feature extraction module 1001 is electrically connected to the image registration detection module 1002, and the output end of the image registration detection module 1002 is electrically connected to the time series distribution map drawing module 1003, and the output end of the time series distribution map drawing module 1003 is electrically connected to the data structured analysis module 1004.

[0040] The specific operations are as follows: the change feature extraction module 1001 can extract the time change features of the ground environment of the remote sensing image according to the time change features; the image registration detection module 1002 can obtain the corresponding image space coordinate transformation parameters through the over-matched feature point pairs to determine the environmental change blocks; the time series distribution map drawing module 1003 can draw a time series distribution map of the city's environmental pollution status according to the time series relationship to reflect the changes in the urban environment; the data structured analysis module 1004 can analyze the urban environmental change data and express it in a pre-structured form to achieve a high degree of logicization of the data.

[0041] In summary, when the urban environment detection and comprehensive evaluation method based on remote sensing data is used, the remote sensing platform 1 first uploads the urban remote sensing data to the cloud server 2, and then the environmental detection center 3 downloads the remote sensing data of the cloud server 2, so that the atmospheric environment detection unit 301, the vegetation environment detection unit 302 and the water environment detection unit 303 respectively detect the atmospheric environment, vegetation environment and water environment in the urban environment. During the detection, the data is processed by the data processing unit 4. In this process, the remote sensing data acquisition module 401 collects and organizes the remote sensing data of different periods according to the time series relationship, and then converts the digital remote sensing data into spatial resolution images. The remote sensing image analysis and processing module 402 analyzes and processes the remote sensing images. The analysis and processing process includes row radiation correction, grayscale image conversion, smoothing, etc. After filtering, the closing operation in mathematical morphology is used to fill the gaps in the contour, and the image contour is integrated to obtain a clearer and more complete remote sensing image; the remote sensing object recognition and classification module 403 performs object classification detection on the image, extracts urban water bodies according to the reflection wave characteristics of the objects, and extracts urban vegetation by using the normalized difference vegetation index, and then uses the multi-spectral processing system to perform semi-quantitative estimation of the situation of atmospheric pollutants. The environmental detection data extraction module 404 extracts urban water body data, urban vegetation data and urban atmospheric pollutant data on the basis of remote sensing object recognition and classification, including surface temperature and humidity data, pH value, smoke concentration, vegetation coverage, water temperature and water level data, and finally the data classification storage module 405 classifies and stores the extracted data in the remote sensing database on the environmental detection center 3.

[0042] Then the environment determination unit 5 evaluates, detects and predicts the urban environment according to the remote sensing data. In this process, the current environment status is detected and evaluated through the current environment assessment module 9. During the detection, the environment assessment model construction module 901 can respectively construct environmental assessment models for atmospheric environment detection, vegetation environment detection and water environment detection, and assign different weights to different detection factors. Then the evaluation data substitution module 902 can extract the valid data inside the remote sensing database of the environment detection center 3 and substitute it into the environmental assessment model. Finally, the evaluation result calculation module 903 can output the evaluation result of the environmental assessment model, and then the visualization analysis module 904 can display the environmental assessment result in the form of a visual chart.

[0043] Then, the comparison detection and evaluation module 10 detects and evaluates the urban environment according to the time series relationship. At this time, the change feature extraction module 1001 can extract the time change features of the land environment of the remote sensing image according to the time change features. Then, the image registration detection module 1002 can obtain the corresponding image space coordinate transformation parameters through the over-matched feature point pairs, and determine the environmental change blocks. Then, the time series distribution map drawing module 1003 can draw a time series distribution map of the city's environmental pollution status according to the time series relationship to reflect the changes in the urban environment. Finally, the data structured analysis module 1004 can analyze the urban environmental change data and express it in a pre-structured form to achieve a high degree of logicization of the data. Finally, the evaluation result output module 11 outputs the evaluation results and classifies the urban environmental conditions according to the urban environment detection results.

[0044] Then, the environmental prediction module 8 is used to predict the urban environmental development status based on historical data. First, the data collection module 801 can collect valid data in the remote sensing database of the environmental detection center 3, and then the environmental model training module 802 can be trained based on historical remote sensing data, wherein the machine learning methods for model training include but are not limited to KNN, decision tree, logistic regression, support vector machine, neural network, deep neural network, and the model training is continued after the best algorithm is selected. Then, the environmental model verification module 803 can verify the training results of the environmental model training module 802 according to the actual environmental development status until the output result error meets the requirements, and then continuously improve the prediction model, so that the final environmental prediction output module 804 can output the predicted environmental data results based on the current environmental data to predict the environmental development, and then the abnormal alarm module 6 can issue a timely alarm when abnormal environmental data or environmental pollution is detected and predicted, so as to facilitate timely maintenance of the urban environment, thus completing the entire process of using the urban environmental detection and comprehensive evaluation method based on remote sensing data.

[0045] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A method for urban environment detection and comprehensive assessment based on remote sensing data, comprising a remote sensing platform (1), a data processing unit (4) and an environment determination unit (5), Features: The output end of the remote sensing platform (1) is connected to a cloud server (2), and the output end of the cloud server (2) is connected to an environmental detection center (3). The data processing unit (4) is electrically connected to the output end of the environmental detection center (3), and the data processing unit (4) comprises a remote sensing data acquisition module (401), a remote sensing image analysis and processing module (402), a remote sensing object recognition and classification module (403), an environmental detection data extraction module (404), and a data classification and storage module (405). The output end of the remote sensing data acquisition module (401) is electrically connected to the remote sensing image analysis and processing module (402), and the remote sensing The output end of the image analysis and processing module (402) is electrically connected to the remote sensing object recognition and classification module (403), the output end of the remote sensing object recognition and classification module (403) is electrically connected to the environment detection data extraction module (404), and the output end of the environment detection data extraction module (404) is electrically connected to the data classification storage module (405), the environment determination unit (5) is electrically connected to the output end of the data processing unit (4), and the output end of the environment determination unit (5) is electrically connected to the abnormal alarm module (6), and the environment determination unit (5) includes an environment assessment module (7) and an environment prediction module (8); The environmental detection center (3) comprises an atmospheric environment detection unit (301), a vegetation environment detection unit (302) and a water environment detection unit (303), and the atmospheric environment detection unit (301), the vegetation environment detection unit (302) and the water environment detection unit (303) are connected in parallel, and the atmospheric environment detection unit (301), the vegetation environment detection unit (302) and the water environment detection unit (303) are used to detect the atmospheric environment, the vegetation environment and the water environment in the urban environment respectively; The environmental assessment module (7) comprises a current environmental assessment module (9), a comparative detection and assessment module (10) and an assessment result output module (11), and the output ends of the current environmental assessment module (9) and the comparative detection and assessment module (10) are electrically connected to the assessment result output module (11); The environment prediction module (8) comprises a data collection module (801), an environment model training module (802), an environment model verification module (803) and an environment prediction output module (804), wherein the output end of the data collection module (801) is electrically connected to the environment model training module (802), the output end of the environment model training module (802) is electrically connected to the environment model verification module (803), and the output end of the environment model verification module (803) is electrically connected to the environment prediction output module (804); The contrast detection and evaluation module (10) comprises a change feature extraction module (1001), an image registration detection module (1002), a time series distribution map drawing module (1003) and a data structured analysis module (1004), wherein the output end of the change feature extraction module (1001) is electrically connected to the image registration detection module (1002), and the output end of the image registration detection module (1002) is electrically connected to the time series distribution map drawing module (1003), and the output end of the time series distribution map drawing module (1003) is electrically connected to the data structured analysis module (1004); When in use, the remote sensing platform (1) first uploads the urban remote sensing data to the cloud server (2), and then the environmental detection center (3) downloads the remote sensing data from the cloud server (2), so that the atmospheric environment detection unit (301), the vegetation environment detection unit (302) and the water environment detection unit (303) respectively detect the atmospheric environment, the vegetation environment and the water environment in the urban environment. During the detection, the data is processed by the data processing unit (4). In this process, the remote sensing data acquisition module (401) collects and organizes the remote sensing data of different periods according to the time series relationship, and then converts the digital remote sensing data into a spatial resolution image. The remote sensing image analysis and processing module (402) analyzes and processes the remote sensing image. The analysis and processing process includes radiation correction, grayscale image conversion, smoothing filter processing, and then The closing operation in mathematical morphology is used to fill the gaps in the contour, integrate the image contour, and obtain a relatively clear and complete remote sensing image; the remote sensing ground object recognition and classification module (403) performs ground object classification detection on the image, extracts urban water bodies according to the ground object reflection wave spectrum characteristics, and extracts urban vegetation by using the normalized difference vegetation index method, and then uses the multi-spectral processing system to perform semi-quantitative estimation of the situation of atmospheric pollutants. The environmental detection data extraction module (404) extracts urban water body data, urban vegetation data and urban atmospheric pollutant data, including surface temperature and humidity data, pH value, smoke concentration, vegetation coverage rate, water temperature and water level data, based on the remote sensing ground object recognition and classification. Finally, the data classification storage module (405) classifies and stores the extracted data in the remote sensing database of the environmental detection center (30); Subsequently, the detection and evaluation module (10) detects and evaluates the urban environment according to the timing relationship. At this time, the change feature extraction module (1001) can extract the temporal change features of the ground object environment from the remote sensing image according to the temporal change features. Then, the image registration and detection module (1002) can obtain the corresponding image space coordinate transformation parameters through the matched feature points to determine the changed land parcels of the environment. Subsequently, the temporal distribution map drawing module (1003) can draw the temporal distribution map of the urban environmental pollution status according to the timing relationship to reflect the urban environmental changes. Finally, the data structured analysis module (1004) can analyze the urban environmental change data and express it in a pre-structured form to achieve a high degree of logic of the data. Finally, the evaluation result output module (11) outputs the evaluation result and classifies and grades the urban environmental status according to the urban environmental detection result; Then, the environmental prediction module (8) predicts the urban environmental development status according to the historical data. First, the data collection module (801) can collect the valid data in the remote sensing database of the environmental detection center (3). Then, the environmental model training module (802) can train according to the historical remote sensing data. The machine learning methods for model training include KNN, decision tree, logistic regression, support vector machine, neural network, and deep neural network. After selecting the best algorithm, the model training is continuously carried out. Subsequently, the environmental model verification module (803) can verify the training results of the environmental model training module (802) according to the actual environmental development status until the error of the output result meets the requirements. Furthermore, the prediction model is continuously improved, so that the final environmental prediction output module (804) can output the predicted environmental data result according to the current environmental data to predict the environmental development. Then, the abnormal alarm module (6) can give an alarm in time when abnormal environmental data or environmental pollution is detected and predicted, so as to facilitate the timely maintenance of the urban environment. In this way, the use process of the entire urban environmental detection and comprehensive evaluation method based on remote sensing data is completed.

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