A deep learning-based glacier information extraction method

Abstract

The application discloses a kind of glacier information extraction methods based on deep learning, it is related to information extraction technical field, including the following steps: according to the required remote sensing image data of research area acquisition, and construct sample library, wherein remote sensing image data includes real-time data and historical data, the remote sensing image data collected is preprocessed, the glacier feature of preprocessed remote sensing image data is extracted, and the abnormal situation in extraction process is analyzed, obtain abnormal feature analysis table, the remote sensing image data in sample library is labeled, determines glacier boundary and classification label.The application reduces the interference brought by natural factors such as cloud and mist shelter, snow cover and shadow change by the optimization and customization of deep learning model, the model can learn the texture, shape and context information specific to glacier, even in complex and changeable environment, can accurately distinguish glacier area and non-glacier area, thereby significantly improve the accuracy of glacier information extraction.

Description

Technical Field

[0001] The present invention relates to the technical field of information extraction, and in particular to a glacier information extraction method based on deep learning. Background Art

[0002] As Earth's largest reservoir of freshwater, glaciers have a profound and complex impact on the global water cycle and climate system. Glaciers not only serve as reservoirs of water but also participate in and influence the global water cycle through meltwater flowing into rivers, lakes, and oceans. Furthermore, changes in glaciers, including their area, volume, and flow rate, are important indicators of climate change, revealing regional and even global trends. In the context of global warming, glaciers face unprecedented challenges of retreat and melting. As temperatures rise, glacier melt accelerates, leading to a significant decrease in glacier area and ice reserves. This not only reduces the freshwater resources available for human use but also has potential ripple effects on local and even global climate systems, such as altering ocean circulation and affecting precipitation patterns. Researchers can leverage advanced technologies such as deep learning for glacier monitoring. Deep learning, with its powerful image recognition and feature extraction capabilities, can monitor glacier dynamics in real time or near real time. With the continued development and improvement of deep learning technology, its application prospects in glacier monitoring, climate research, and disaster warning will expand, providing strong technical support for global climate change response and sustainable development.

[0003] In existing technologies, the complex terrain, changeable climate environment and unstable lighting conditions unique to glacier areas, such as frequent cloud cover, snow cover, and shadow changes, pose significant technical challenges and directly affect the accuracy of deep learning models in extracting glacier information. What is more complicated is that the morphology and coverage of glaciers will change significantly with the change of seasons, which requires deep learning models to have a high degree of flexibility and adaptability to capture and accurately reflect these dynamic changes in real time. To address the above problems, reducing interference factors and improving model adaptability are important directions for the development of current glacier information extraction technology, and are also the key to achieving accurate glacier monitoring and early warning. Summary of the Invention

[0004] The purpose of the present invention is to provide a glacier information extraction method based on deep learning to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A glacier information extraction method based on deep learning includes the following steps:

[0007] Step 1: Collect the required remote sensing image data according to the study area and build a sample library, where the remote sensing image data includes real-time data and historical data;

[0008] Step 2: preprocess the collected remote sensing image data, extract glacier features from the preprocessed remote sensing image data, and analyze abnormalities in the extraction process to obtain an abnormal feature analysis table;

[0009] Step 3: Label the remote sensing image data in the sample library, determine the glacier boundaries and classification labels, and build a deep learning model based on the abnormal feature analysis table to obtain the environmental anomaly index and seasonal anomaly index;

[0010] Step 4: Obtain a glacier warning assessment coefficient based on the environmental anomaly index and the seasonal anomaly index, determine different warning levels based on preprocessed historical data, set warning assessment thresholds for different warning levels, and obtain a warning assessment sequence table;

[0011] Step 5: Analyze the status of the glacier warning assessment coefficient at different warning levels based on the warning assessment sequence table, obtain an assessment report, and perform analysis and warning based on the extracted glacier information.

[0012] A further improvement of the technical solution of the present invention is that the process of constructing the sample library is:

[0013] Step 101: clarify the geographical location of the study area, such as latitude and longitude ranges, administrative boundaries, or specific geographical features, and mark the specific location of the study area on the map to facilitate subsequent data query and download, as well as the time range required for real-time data and historical data;

[0014] Step 102: Acquire the latest remote sensing image data through satellite sensors, aerial photography, and ground observation stations. The remote sensing image data includes remote sensing images, terrain data, and climate data. The real-time data is filtered according to the study area and the time range of the remote sensing image data.

[0015] Step 103: Use the data published by the remote sensing data platform to obtain historical data of remote sensing image data, and integrate the screened real-time data with the obtained historical data to build a sample library.

[0016] A further improvement of the technical solution of the present invention is that the process of obtaining the abnormal feature analysis table is as follows:

[0017] Step 201: Correct the collected real-time data and historical data, perform data enhancement, and perform cloud and shadow processing. The correction includes radiation correction to eliminate the influence of factors such as sensor characteristics, solar altitude angle, and atmospheric conditions on the data; atmospheric correction to remove the influence of the atmosphere on the image, such as aerosol scattering and water vapor absorption, so that the image data is closer to the actual reflectivity or emissivity of the surface; and geometric correction to correct the geometric distortion of the image, such as pixel position offset caused by satellite attitude changes, earth curvature, and terrain undulations.

[0018] Step 202: GIS processing technology is used to extract features from real-time data and historical data, specifically for glacier area, glacier texture, such as roughness and directionality, glacier shape, such as area, perimeter, aspect ratio, and spatial distribution.

[0019] Step 203: Analyze the abnormality based on the extracted features and record the abnormality type, location, impact range, and other information to obtain abnormal interference factors. The abnormal interference factors include cloud data, shadow data, avalanche data, and missing value data. For cloud data and shadow data, record their detection methods, mask processing results, and impact on glacier feature extraction. For avalanche data and missing value data, record their causes, impact range, and possible remedial measures.

[0020] Step 204 , compile an abnormality feature analysis table based on the abnormal interference factors, and record detailed information of each abnormal situation, including abnormality type, occurrence time, location, scope, impact degree, and treatment measures.

[0021] A further improvement of the technical solution of the present invention is that the process of obtaining the environmental anomaly index and the seasonal anomaly index is as follows:

[0022] Step 301: Combine the glacier area, glacier texture, glacier shape and spatial distribution characteristics, and crop the real-time data and historical data in the sample library to obtain image features, such as glaciers, water bodies, bare land, vegetation, etc., and automatically annotate the extracted image features to identify glacier areas, abnormal areas and specific abnormal types;

[0023] Step 302: Integrate the associated data of abnormal interference factors and annotated image features to form a unified data set;

[0024] Step 303: Use the abnormal feature analysis table to identify abnormal situations in the data set, encode the abnormal situations, and build a deep learning model.

[0025] In step 304, the integrated data set is divided into a training set, a validation set and a test set, the deep learning model is trained using the training set data, and the trained deep learning model is verified using the data of the validation set and the test set, and the performance of the deep learning model is evaluated.

[0026] In step 305, using the trained model, the environmental anomaly index and the seasonal anomaly index are obtained combined with the preprocessed real-time data, and the overall abnormal condition of the glacier area is analyzed, the seasonal anomaly index of different seasons is compared, the regularity and characteristics of seasonal change are identified, the abnormal degree difference of the glacier area in different seasons is evaluated, and a related analysis report of the environmental anomaly index and the seasonal anomaly index is output.

[0027] Further improvement of the technical scheme of the application is that the calculation formula of the environmental anomaly index is:

[0028]

[0029] Wherein, EAI is the environmental anomaly index, A i is the reflectivity anomaly value of the i-th pixel point, μ A is the average value of the reflectivity anomaly value of all pixel points, N is the total number of pixel points of the reflectivity anomaly value, C i is the cloud coverage of the j-th pixel point, μ c is the average value of the cloud coverage of all pixel points, M is the total number of pixel points of the cloud coverage;

[0030] The calculation formula of the seasonal anomaly index is:

[0031]

[0032] Wherein, SAI is the seasonal anomaly index, G t is the glacier coverage measurement value of the t-th time point, G a is the long-term average glacier coverage, G s is the standard deviation of the glacier coverage, ω t is the weight coefficient of the t-th time point, r t is the seasonal change coefficient of the t-th time point, and t is the total number of considered time points.

[0033] Further improvement of the technical scheme of the application is that the acquisition process of the early warning evaluation sequence table is:

[0034] In step 401, the environmental anomaly index and the seasonal anomaly index are combined by combining the preprocessed historical data, and the correlation degree between the environmental anomaly index and the seasonal anomaly index is analyzed.

[0035] Step 402: assign different weights to the environmental anomaly index and the seasonal anomaly index, perform weighted calculation on the two indices, and obtain a glacier early warning assessment coefficient. Different weights are assigned to the anomaly index and the seasonal anomaly index based on their importance in affecting glacier change.

[0036] Step 403: Classify the glacier warning assessment coefficient into different warning levels, such as low risk, medium risk, and high risk, based on the distribution of the environmental anomaly index and the seasonal anomaly index in the historical data;

[0037] Step 404: Match the result of the glacier early warning assessment coefficient with each warning level and set the corresponding assessment threshold;

[0038] Step 405: Arrange all glacier warning assessment coefficients in descending order, assign a corresponding warning level to each value, and create a warning assessment sequence table, listing the corresponding relationship between the environmental anomaly index and the seasonal anomaly index.

[0039] A further improvement of the technical solution of the present invention is that the calculation formula of the glacier early warning assessment coefficient is:

[0040]

[0041] Among them, ω EAI and ω SAI are the weight coefficients of the environmental anomaly index and seasonal anomaly index, and ω SAI +ω EAI =1, EAI is the environmental anomaly index, SAI is the seasonal anomaly index, β and γ are the adjustment parameters that control the speed of index decrease and increase, and μSAI is the average value of seasonal anomaly index.

[0042] A further improvement of the technical solution of the present invention is that the warning levels and their evaluation threshold ranges are:

[0043] Low risk warning level: GAAC <Q1;

[0044] Medium risk warning level: Q1≤GAAC <Q2;

[0045] High-risk warning level: GAAC>Q2.

[0046] A further improvement of the technical solution of the present invention is that the process of analyzing and warning based on the extracted glacier information is as follows:

[0047] Step 501: Classify the glacier warning assessment coefficient according to the warning level, analyze the distribution range of different warning levels under each level standard, determine the frequency and characteristics of each warning level, and obtain the analysis results. Examine the correlation between the GAC value and other environmental parameters, such as temperature, precipitation, and glacier thickness change rate, to further understand the physical mechanism behind each level.

[0048] Step 502: Output an assessment report based on the analysis results and compile a risk assessment report, marking low risk, medium risk, and high risk in the report, and analyzing the influencing factors and possible consequences of different warning levels;

[0049] Step 503: formulate response measures based on the assessment report and risk analysis results, continuously monitor the glacier early warning assessment coefficient, and regularly update the early warning assessment sequence table and assessment report.

[0050] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art:

[0051] 1. The present invention provides a glacier information extraction method based on deep learning. By optimizing and customizing the deep learning model, the interference caused by natural factors such as cloud obstruction, snow cover and shadow changes is reduced. The model can learn the unique texture, shape and contextual information of glaciers, and can accurately distinguish between glacier areas and non-glacier areas even in complex and changing environments, thereby significantly improving the accuracy of glacier information extraction and providing reliable data support for glacier monitoring and protection.

[0052] 2. This invention provides a deep learning-based glacier information extraction method. This deep learning model continuously learns and optimizes to adapt to the seasonal changes in glacier morphology and coverage. By incorporating strategies such as transfer learning and online learning, the model can continuously learn new glacier characteristics and rapidly adapt to dynamic changes in environmental conditions. Furthermore, the use of multi-source data fusion technology, combined with multi-dimensional information such as satellite imagery and ground observations, further enhances the model's adaptability to complex environments, ensuring accurate glacier information extraction in different seasons and weather conditions.

[0053] 3. The present invention provides a glacier information extraction method based on deep learning. The efficient computing power of the deep learning model significantly improves monitoring efficiency and real-time performance. It can quickly process large-scale remote sensing data and realize real-time extraction and updating of glacier information. This not only reduces labor costs, but also shortens the data processing cycle, enabling decision makers to quickly obtain the latest status of glaciers and providing the possibility for timely response measures.

[0054] 4. The present invention provides a glacier information extraction method based on deep learning. The efficient computing power of the deep learning model enables real-time data processing to be faster, and provides timely intelligence through real-time monitoring capabilities, enabling them to take quick action to reduce potential disaster risks and protect human life and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 This is a flow chart for obtaining the environmental anomaly index and seasonal anomaly index of the present invention;

[0058] Figure 3 The figure is a flow chart for obtaining the early warning evaluation sequence table of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Examples, such as Figure 1-3 As shown, the present invention provides a glacier information extraction method based on deep learning, comprising the following steps:

[0061] Step 1: Collect the required remote sensing image data according to the study area and build a sample library. The remote sensing image data includes real-time data and historical data. The process of building the sample library is as follows: clarify the geographical location of the study area, such as the latitude and longitude range, administrative boundaries or specific geographical features, mark the specific location of the study area on the map for subsequent data query and download, and the time range required for real-time data and historical data. Obtain the latest remote sensing image data through satellite sensors, aerial photography, and ground observation stations. Remote sensing image data includes remote sensing images, terrain data, and climate data. Filter real-time data based on the study area and the time range of remote sensing image data. Use the data publicly available on the remote sensing data platform to obtain historical data of remote sensing image data. Integrate the filtered real-time data with the acquired historical data to build a sample library.

[0062] Step 2: pre-process the collected remote sensing image data, extract the glacier features of the pre-processed remote sensing image data, and analyze the anomalies in the extraction process to obtain an abnormal feature analysis table; the process of obtaining the abnormal feature analysis table is: correcting the collected real-time data and historical data, enhancing the data, and processing clouds and shadows. The correction includes radiation correction to eliminate the influence of sensor characteristics, solar altitude angle, atmospheric conditions and other factors on the data; atmospheric correction to remove the influence of the atmosphere on the image, such as aerosol scattering, water vapor absorption, etc., so that the image data is closer to the actual reflectivity or emissivity of the surface; geometric correction to correct the geometric distortion of the image, such as pixel position offset caused by satellite attitude changes, earth curvature, terrain undulations, etc., and use GIS processing technology to extract the real-time data and historical data. Perform feature extraction, specifically for glacier area, glacier texture (such as roughness and directionality), glacier shape (such as area, perimeter, aspect ratio), and spatial distribution. Perform anomaly analysis based on the extracted features, and record information such as anomaly type, location, and impact range to obtain anomaly interference factors. Anomaly interference factors include cloud data, shadow data, avalanche data, and missing value data. For cloud data and shadow data, record their detection methods, mask processing results, and impact on glacier feature extraction. For avalanche data and missing value data, record their causes, impact range, and possible remedial measures. Prepare an anomaly feature analysis table based on the anomaly interference factors, and record detailed information for each anomaly, including anomaly type, occurrence time, location, range, impact, and treatment measures.

[0063] Step 3: Annotate the remote sensing image data in the sample library, determine the glacier boundaries and classification labels, and build a deep learning model based on the abnormal feature analysis table to obtain the environmental anomaly index and seasonal anomaly index. The process of obtaining the environmental anomaly index and seasonal anomaly index is as follows: combine the glacier area, glacier texture, glacier shape and spatial distribution characteristics, crop the real-time data and historical data in the sample library to obtain image features, such as glaciers, water bodies, bare land, vegetation, etc., and automatically annotate the extracted image features to clarify the glacier area, abnormal area and specific abnormal type, integrate the abnormal interference factors and the associated data of the annotated image features to form a unified data set, use the abnormal feature analysis table to identify the abnormal conditions in the data set, encode the abnormal conditions, and build a deep learning model at the same time. The integrated data set is divided into training set, validation set and test set, and the training set data is used to verify the deep learning model. The deep learning model is trained and the trained deep learning model is verified using the data of the validation set and the test set to evaluate the performance of the deep learning model. The trained model is used in combination with the preprocessed real-time data to obtain the environmental anomaly index and the seasonal anomaly index, and the overall anomaly status of the glacier area is analyzed. The seasonal anomaly index of different seasons is compared, the patterns and characteristics of seasonal changes are identified, the degree of anomaly of the glacier area in different seasons is evaluated, and relevant analysis reports on the environmental anomaly index and the seasonal anomaly index are output. Through the optimization and customization of the deep learning model, the interference caused by natural factors such as cloud obstruction, snow cover and shadow changes is reduced. The model can learn the unique texture, shape and contextual information of the glacier, and can accurately distinguish between glacier areas and non-glacier areas even in complex and changing environments, thereby significantly improving the accuracy of glacier information extraction and providing reliable data support for glacier monitoring and protection.

[0064] The calculation formula of the environmental anomaly index is:

[0065]

[0066] Among them, EAI is the environmental anomaly index, A i is the reflectivity abnormal value of the i-th pixel, μ A is the average value of all pixel reflectance anomalies, N is the total number of pixel reflectance anomalies, C i is the cloud coverage of the j-th pixel, μ c is the average value of cloud coverage of all pixels, and M is the total number of pixels with cloud coverage;

[0067] The calculation formula of seasonal anomaly index is:

[0068]

[0069] Where SAI is the seasonal anomaly index, G tis the measured value of glacier coverage at time point t, G a is the long-term average glacier coverage, G s is the standard deviation of glacier coverage, ω t is the weight coefficient at the tth time point, r t is the seasonal variation coefficient at the t-th time point, and t is the total number of time points considered;

[0070] Step 4: Obtain a glacier early warning assessment coefficient based on the environmental anomaly index and the seasonal anomaly index, determine different early warning levels based on preprocessed historical data, set early warning assessment thresholds for different early warning levels, and obtain an early warning assessment sequence table. The early warning assessment sequence table is obtained by combining the environmental anomaly index and the seasonal anomaly index based on the preprocessed historical data, analyzing the degree of correlation between the environmental anomaly index and the seasonal anomaly index, assigning different weights to the environmental anomaly index and the seasonal anomaly index, performing weighted calculation on the two indices to obtain a glacier early warning assessment coefficient, assigning different weights to the anomaly index and the seasonal anomaly index based on their importance to glacier changes, dividing the glacier early warning assessment coefficient into different early warning levels, such as low risk, medium risk, and high risk, based on the distribution of the environmental anomaly index and the seasonal anomaly index in the historical data, matching the result of the glacier early warning assessment coefficient with each early warning level, and setting corresponding assessment thresholds. Arrange all glacier early warning assessment coefficients in descending order, assign a corresponding early warning level to each value, and create an early warning assessment sequence table that lists the corresponding relationship between the environmental anomaly index and the seasonal anomaly index.

[0071] The calculation formula for the glacier early warning assessment coefficient is:

[0072]

[0073] Among them, ω EAI and ω SAI are the weight coefficients of the environmental anomaly index and seasonal anomaly index, and ω SAI +ω EAI =1, EAI is the environmental anomaly index, SAI is the seasonal anomaly index, β and γ are the adjustment parameters that control the speed of index decrease and increase, and μSAI is the average value of seasonal anomaly index;

[0074] The warning levels and their assessment threshold ranges are as follows:

[0075] Low risk warning level: GAAC <Q1;

[0076] Medium risk warning level: Q1≤GAAC <Q2;

[0077] High-risk warning level: GAAC>Q2;

[0078] Step 5: Analyze the status of the glacier warning assessment coefficient at different warning levels based on the warning assessment sequence table, obtain an assessment report, and conduct analysis and warning based on the extracted glacier information. The process of analyzing and warning based on the extracted glacier information is as follows: classify the glacier warning assessment coefficient according to the warning level, analyze the distribution range at different warning levels under each level standard, determine the frequency and characteristics of each warning level, and obtain analysis results; examine the correlation between the GAC value and other environmental parameters, such as temperature, precipitation, and glacier thickness change rate, to further understand the physical mechanism behind each level; output an assessment report based on the analysis results, and prepare a risk assessment report, marking low risk, medium risk, and high risk in the report, and analyze the influencing factors and possible consequences at different warning levels; formulate response measures based on the assessment report and risk analysis results, and continuously monitor the glacier warning assessment coefficient, and regularly update the warning assessment sequence table and assessment report; formulate response measures based on the assessment report and risk analysis results. This may include: enhanced monitoring: increasing the frequency of monitoring in high-risk areas; disaster prevention: formulating and implementing disaster prevention and mitigation plans; public warning: issuing warning information to the public and relevant departments; policy adjustment: adjusting relevant policies and plans based on risk assessment. The efficient computing power of deep learning models has significantly improved monitoring efficiency and real-time performance. It can quickly process large-scale remote sensing data and realize real-time extraction and updating of glacier information. This not only reduces labor costs, but also shortens the data processing cycle, enabling decision makers to quickly obtain the latest status of glaciers and providing the possibility for timely response measures.

[0079] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A glacier information extraction method based on deep learning, characterized by: The following steps are involved: Step 1: Collect the required remote sensing image data according to the study area and build a sample library, where the remote sensing image data includes real-time data and historical data; Step 2: preprocess the collected remote sensing image data, extract glacier features from the preprocessed remote sensing image data, and analyze abnormalities in the extraction process to obtain an abnormal feature analysis table; Step 3: Label the remote sensing image data in the sample library, determine the glacier boundaries and classification labels, and build a deep learning model based on the abnormal feature analysis table to obtain the environmental anomaly index and seasonal anomaly index. The process of obtaining the environmental anomaly index and seasonal anomaly index is as follows: Step 301: combining glacier area, glacier texture, glacier shape and spatial distribution characteristics, cropping the real-time data and historical data in the sample library to obtain image features, and automatically annotating the extracted image features; Step 302: Integrate the associated data of abnormal interference factors and annotated image features to form a unified data set; Step 303: using the abnormal feature analysis table to identify abnormalities in the data set, encoding the abnormalities, and building a deep learning model; Step 304: Divide the integrated data set into a training set, a validation set, and a test set. Use the training set data to train the deep learning model, and use the validation set and test set data to verify the trained deep learning model and evaluate the performance of the deep learning model. Step 305: Using the trained model and preprocessed real-time data, the environmental anomaly index and seasonal anomaly index are obtained. The overall anomaly status of the glacier region is analyzed, and the seasonal anomaly indexes of different seasons are compared to identify the patterns and characteristics of seasonal changes. The anomaly degree of the glacier region in different seasons is evaluated, and a correlation analysis report of the environmental anomaly index and seasonal anomaly index is output. Step 4: Obtain a glacier warning assessment coefficient based on the environmental anomaly index and the seasonal anomaly index, determine different warning levels based on preprocessed historical data, set warning assessment thresholds for different warning levels, and obtain a warning assessment sequence table; Step 5: Analyze the status of the glacier warning assessment coefficient at different warning levels based on the warning assessment sequence table, obtain an assessment report, and perform analysis and warning based on the extracted glacier information.

2. The method for extracting glacier information based on deep learning according to claim 1, characterized in that: The process of building the sample library is as follows: Step 101, defining the geographical location of the study area and the time range required for real-time data and historical data; Step 102, acquiring the latest remote sensing image data through satellite sensors, aerial photography, and ground observation stations, and filtering the real-time data based on the study area and the time range of the remote sensing image data; Step 103: Use the data published by the remote sensing data platform to obtain historical data of remote sensing image data, and integrate the screened real-time data with the obtained historical data to build a sample library.

3. The method for extracting glacier information based on deep learning according to claim 2, characterized in that: The process of obtaining the abnormal feature analysis table is as follows: Step 201: Correction, data enhancement, and cloud and shadow processing are performed on the collected real-time data and historical data, wherein the correction includes radiation correction, atmospheric correction, and geometric correction; Step 202: Using GIS processing technology to extract features from real-time data and historical data, and extracting features for glacier area, glacier texture, glacier shape, and spatial distribution; Step 203: performing abnormal situation analysis based on the extracted features to obtain abnormal interference factors, wherein the abnormal interference factors include cloud data, shadow data, avalanche data, and missing value data; Step 204: compile an abnormal feature analysis table based on the abnormal interference factors, and record detailed information of each abnormal situation.

4. The method for extracting glacier information based on deep learning according to claim 3, characterized in that: The calculation formula of the environmental anomaly index is: ； in, is the environmental anomaly index, For the The reflectivity anomaly of each pixel point, is the average value of all pixel reflectance anomalies, is the total number of pixels with abnormal reflectivity values, For the The cloud coverage of each pixel, is the average value of cloud coverage of all pixels, is the total number of pixels of cloud cover; The calculation formula of the seasonal anomaly index is: ； in is the seasonal anomaly index, For the The glacier cover measurement at each time point, is the long-term average glacier cover, is the standard deviation of glacier coverage, For the The weight coefficient of each time point, For the The seasonal variation coefficient at each time point, is the total number of time points considered.

5. The method for extracting glacier information based on deep learning according to claim 4, characterized in that: The acquisition process of the early warning assessment sequence table is as follows: Step 401, combining the environmental anomaly index and the seasonal anomaly index with the pre-processed historical data, and analyzing the correlation between the environmental anomaly index and the seasonal anomaly index; Step 402: assign different weights to the environmental anomaly index and the seasonal anomaly index, perform weighted calculation on the two indices, and obtain a glacier early warning assessment coefficient; Step 403: classify the glacier warning assessment coefficient into different warning levels according to the distribution of the environmental anomaly index and the seasonal anomaly index in the historical data; Step 404: Match the result of the glacier early warning assessment coefficient with each warning level and set the corresponding assessment threshold; Step 405: Arrange all glacier warning assessment coefficients in descending order, assign a corresponding warning level to each value, and create a warning assessment sequence table, listing the corresponding relationship between the environmental anomaly index and the seasonal anomaly index.

6. The method for extracting glacier information based on deep learning according to claim 5, characterized in that: The calculation formula of the glacier early warning assessment coefficient is: ； in, and are the weight coefficients of the environmental anomaly index and seasonal anomaly index, respectively, and , is the environmental anomaly index, is the seasonal anomaly index, and is the adjustment parameter for controlling the speed of index decrease and increase, is the average value of the seasonal anomaly index.

7. The method for extracting glacier information based on deep learning according to claim 6, characterized in that: The warning levels and their assessment threshold ranges are as follows: Low risk warning level: ; Medium risk warning level: ; High risk warning level: .

8. The method for extracting glacier information based on deep learning according to claim 7, characterized in that: The process of analyzing and warning based on the extracted glacier information is as follows: Step 501: Classify the glacier warning assessment coefficients according to warning levels, analyze the distribution ranges of different warning levels under each level standard, determine the frequency and characteristics of each warning level, and obtain analysis results; Step 502: Output an assessment report based on the analysis results and compile a risk assessment report, marking low risk, medium risk, and high risk in the report, and analyzing the influencing factors and possible consequences of different warning levels; Step 503: formulate response measures based on the assessment report and risk analysis results, continuously monitor the glacier early warning assessment coefficient, and regularly update the early warning assessment sequence table and assessment report.

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