431 results about "Land cover" patented technology

A method and device for evaluating ecological cumulative effects of surface mining areas are provided. The method includes: constructing a surface mining areas eco-environmental evaluation index (SMAEEI) suitable for semi-arid grasslands; constructing a first eco-environmental quality condition adjustment coefficient R(Si,j,th) based on the actual land cover classification result and the SMAEEI; obtaining the actual ecosystem service value ESV per unit area of the area to be evaluated in several years through the unit area ecosystem service value coefficient VCif of the area to be evaluated and the first eco-environmental quality condition adjustment coefficient R(Si,j,th); based on the time-series trajectory integral value of the actual ecosystem service value ESV per unit area and the ideal ecosystem service value ESV′ per unit area undisturbed by human beings, performing residual analysis to obtain the ecosystem service value accumulation induced by anthropogenic factors (ESVA-AF).

The invention discloses a remote sensing image multi-scale semantic segmentation method based on a coding and decoding network, and relates to the technical field of computer vision and remote sensing image processing, and the method comprises the following steps: S1, obtaining image data, carrying out the preprocessing of an image, carrying out the normalization of the image to a specified size, and carrying out the data enhancement operation, the data enhancement operation comprises random rotation, overturning and zooming. According to the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network, ResNeXt5032x4d is introduced to serve as a backbone network, grouping convolution is combined, multi-scale features are effectively extracted, the accurate recognition capacity of the model for the land cover type is improved, and through the fusion strategy of the self-adaptive feature cooperation module AFCM, the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network is obtained. According to the method, local and global context information is fused, the segmentation capability of the model on a large target is enhanced through a parallel multi-scale convolution layer and cavity convolution, the accuracy and integrity of a segmentation result are ensured, and a segmented region is smoother and more complete.

The invention relates to the technical field of remote sensing recognition, in particular to a remote sensing recognition method and system applied to ecological system investigation. The method comprises the following steps: acquiring multi-temporal remote sensing image data of a target area; extracting a land cover type of the multi-temporal remote sensing image data, and performing dominant human activity area identification on the multi-temporal remote sensing image data according to the land cover type to generate dominant human activity area data; acquiring night light data of the target area; performing space-time registration on the night light data of the target area and the multi-temporal remote sensing image data to generate fused night light remote sensing data; performing boundary region extraction on the multi-temporal remote sensing image data through the land cover type to obtain edge region data; and calculating a vegetation index and a noctilucence index of the marginal region data based on the fused noctilucence remote sensing data. According to the method, through multi-temporal and multi-source data fusion and multi-index time sequence analysis, the accuracy and comprehensiveness of ecological system investigation remote sensing recognition are improved.

The invention discloses a high-precision topographic mapping system and method based on multi-source remote sensing data, and relates to the technical field of topographic mapping, and the method comprises the steps: obtaining an optical remote sensing image, a radar remote sensing image, LiDAR point cloud data and meteorological index data of a target region, and carrying out the data preprocessing of the optical remote sensing image, the radar remote sensing image and the LiDAR point cloud data; obtaining the surface feature types of the target area and the coverage area of each surface feature type, and carrying out the weighted fusion of the optical remote sensing image and the radar remote sensing image of the target area according to the surface feature types of the target area and the coverage area of each surface feature type, and obtaining a self-adaptive fusion remote sensing image; according to the method, points in LiDAR point cloud data are divided into distinguished ground points and non-ground points, a digital elevation model is constructed according to the ground points in the LiDAR point cloud data, three-dimensional modeling of the terrain is performed on the basis of the digital elevation model in combination with texture information of a self-adaptive fusion remote sensing image, a three-dimensional terrain model is generated, and the terrain surveying and mapping precision is remarkably improved.

The invention relates to the technical field of environment management, in particular to a land coverage change monitoring method based on fusion of a remote sensing image and a geographic information system, which comprises the following steps: S1, data acquisition: acquiring multi-temporal remote sensing and auxiliary data; s2, data preprocessing: preprocessing the collected remote sensing and auxiliary data; s3, land feature classification and land cover change identification: classifying land cover types, and performing land cover change identification in combination with the spatial texture features of the remote sensing image data; and S4, result output and analysis: performing fusion and analysis on the terrain classification and land cover change identification result and the existing land cover data in the geographic information system, and evaluating the change trend of land utilization. According to the method, different types of land coverage can be identified carefully, and particularly, when complex remote sensing data is processed, the classification precision and the accuracy of change identification are remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention discloses a depth space-time super-resolution mapping method fusing historical high-resolution land coverage data, and the method comprises the steps: constructing a depth space-time super-resolution mapping model structure which is formed by connecting a super-resolution adversarial network SRGAN structure and a MUNet network structure in series; mapping from low-resolution images to high-resolution land coverage category probabilities in combination with super-resolution reconstruction and semantic segmentation is carried out, a historical high-resolution land coverage category map of a target area is used as auxiliary information, and changed categories and unchanged categories are distinguished by comparing category information of the low-resolution images in a historical period and a target period; and a change detection-based sub-pixel category allocation optimization model fusing a historical high-resolution land cover classification map and a predicted high-resolution category probability map is constructed, so that the depth space-time super-resolution mapping of the low-resolution remote sensing image is completed. The method has the advantages of high simulation precision, stability and the like, and is suitable for earth surface information extraction work such as classification, land cover / utilization, change detection and the like when remote sensing images are insufficient.

The embodiment of the invention provides an AI analysis method and system applied to land resource investigation, and the method comprises the steps: obtaining the multi-source remote sensing image data of a target region, analyzing the spectrum and texture features of the multi-source remote sensing image data, generating a comprehensive land feature map, obtaining an initial land classification map through a land classification model, and obtaining the initial land classification map. And performing time sequence comparative analysis in combination with historical data to determine a dynamic area of land coverage change and generate a land resource change report. The method further comprises the functions of soil quality evaluation, change area visualization, abnormal area real-time monitoring, land classification model training, classification model adjustment according to user feedback and the like, and the efficiency and accuracy of land resource investigation are improved.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention discloses an SAR image classification method based on multi-modal knowledge distillation transmission, and relates to the technical field of land cover classification. According to the SAR image classification method based on multi-modal knowledge distillation transmission, a multi-modal knowledge distillation model including a teacher model and a student model is constructed, and the trained student model is utilized to perform classification identification on the SAR image to be detected. In the training process of the model, knowledge migration between different modal images and between image text modals is realized based on an optical-SAR knowledge migration strategy of knowledge distillation transmission, and multi-level semantic features can be extracted from a teacher model of a remote sensing optical image to assist learning of a student model of an SAR image. And the image semantics and the multi-label text semantics are aligned by an optimal transmission method, so that the defect of insufficient recognition capability of optical and SAR images under different environment conditions is overcome, and the accuracy of multi-label ground feature type judgment of the SAR image is effectively improved.

A processor-implemented method for calculating emissions and measuring carbon sequestration levels is provided. The method includes (i) registering, by a user device, a first entity associated with an amount of carbon emission and a second entity associated with an amount of carbon absorption to create an account in a blockchain-based system using user details, (ii) obtaining satellite images and the drone images of the land cover of the second entity from a satellite source database, (iii) monitoring the plurality of satellite images and the plurality of drone images of the land cover of the second entity by creating land coordinates to calculate a carbon sequestration level associated with the second entity using an AI model, and (iv) training the AI model using an algorithm by categorizing the carbon sequestration level with known species of the second entity and the known tree species includes a drone image and the date when the image is captured.

The invention relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention provides a large-scale land cover remote sensing intelligent information extraction method and system based on sequence mask prediction, and aims to improve the precision and efficiency of land cover information extraction. The method comprises the steps of preprocessing and region division, sample set generation and labeling, U-ConvHDNet model training, sequence mask prediction, model precision evaluation and the like. Through preprocessing of geometric correction, radiation correction and the like, fine region division is constructed in combination with multiple data sources. A sample set is independently manufactured and labeled, a U-ConvHDNet model is used for binary classification training, ConvNeXt, Haar wavelet down-sampling and up-sampling modules DySample are fused in the model, and deep features and multi-scale information are effectively captured. In the sequence mask prediction stage, a double receptive field seamless sliding window is adopted, and step-by-step prediction is carried out in combination with the ground feature extraction difficulty. The method and the system have the characteristics of high automation degree, high extraction precision, strong adaptability and the like, and can be widely applied to the field of large-scale land utilization mapping and monitoring.

A decision system (100) for planning urban green spaces in cities, wherein the system comprises: a remote sensing unit (102) configured to acquire satellite and aerial imagery for the assessment of land surface temperature (LST), land use, land cover and urban green areas; a unit (104) of a geographic information system (GIS) integrated with a remote sensing unit (102) and configured to collect and process spatial data, including land surface temperature (LST) data, land use data and green space distribution data; a computing unit (106) connected to the remote sensing unit (102) and configured to estimate the green space requirement based on LST reduction, wherein the computing unit (106) uses a curve fitting technique using historical and observed data sets to reduce the LST; a prediction unit (108) connected to a computing unit (106) and configured to predict the optimal grassland area to achieve a desired LST reduction, wherein the prediction unit (108) uses a third-order polynomial equation that correlates the grassland area with the land surface temperature; a data validation unit (110) used to validate generated outputs using statistical methods, such as the coefficient of determination (R²). 2 ), of the mean square error (RMSE) and the mean absolute error (MAE), is configured to ensure accuracy and reliability; a site suitability analysis unit (112) integrated into the GIS unit (104), configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; A processing unit (114) is configured so that urban planners and political decision-makers can enter parameters such as the desired temperature reduction and generate reports with detailed information on the need for green spaces and suitable locations for implementation.

The embodiment of the invention provides a rice identification method fusing time sequence spectrum difference and phenological information similarity matching, and belongs to the technical field of image identification, and the method specifically comprises the steps: combining phenology and land cover products to generate an automatic generation identification sample; training the first sample in each month of the target year in parallel to obtain 12 time sequence extraction classifiers, applying the 12 time sequence extraction classifiers to the second sample to obtain a prediction probability, and dividing the prediction probability into a third sample and a fourth sample; calculating the separable index of each time sequence based on the prediction probability of the third sample, and determining the phenological weight of each phenological period; taking the phenological weight as an adjustment parameter, transferring the prediction probability of the third sample to a time domain, calculating the similarity by using a time-weighted dynamic warping algorithm, cooperatively supervising, learning and training a prediction classifier, and verifying by a fourth sample; and forming an identification model and identifying the target area. Through the scheme disclosed by the invention, the recognition accuracy and adaptability are improved.

The invention discloses a Chinese region soil humidity remote sensing inversion method based on CYGNSS satellite data. The method comprises the following steps: S1, preprocessing the collected actually measured soil humidity, delay-Doppler map, normalized vegetation index and land cover type; modeling samples matched with all the data are obtained through space-time matching; s2, taking the soil humidity as a target variable, selecting a CYGNSS trailing edge slope, an incident angle, a ground elevation, a surface roughness and a vegetation optical thickness as characteristic variables, and constructing a soil humidity remote sensing model; the modeling samples are divided into a training set and a test set, the soil humidity remote sensing model is trained, and the optimal soil humidity remote sensing model is stored; and S3, according to the optimal soil humidity remote sensing model, inverting daily soil humidity data from the CYGNSS trailing edge slope and related auxiliary data. According to the invention, the precision of the satellite-borne GNSS-R technology for remotely sensing and monitoring the soil humidity is improved.

The invention discloses a remote sensing image cloud removal method and system based on geographic coordinate embedding, and the system employs a geographic coordinate system embedding module to encode pixel longitude and latitude information into high-dimensional features, and achieves the joint modeling of an absolute position and a relative space relation through spherical coordinate mapping and a rotation matrix. And in combination with a multi-head self-attention mechanism and residual connection, the position sensitivity of the feature space is enhanced. Meanwhile, by using a multi-level feature extraction network and a cloud distortion compensation module, through a residual block and convolution operation, cloud interference features are extracted, a position sensing correction vector is generated, nonlinear offset of a cloud coverage area is corrected, and earth surface textures are recovered. According to the method, the adaptability of a cross-regional scene is remarkably improved, texture distortion and information loss of a large-scale cloud region are effectively reduced, the method has the advantages of high precision and high robustness in the fields of remote sensing image preprocessing, agricultural monitoring, land cover change analysis and the like, and a new scheme is provided for high-quality application of optical remote sensing data.

The invention discloses a multi-temporal remote sensing image automatic change detection method based on deep learning, and the method comprises the following steps: collecting remote sensing image data obtained in different periods in a target region, carrying out the standardization processing of the remote sensing image data, and obtaining a standardized multi-temporal remote sensing image data set; based on the standardized multi-temporal remote sensing image data set, carrying out ground feature category segmentation by adopting a semantic segmentation network to obtain a ground feature semantic priori graph; inputting the standardized multi-temporal remote sensing image data set into the improved U-shaped twin network, and outputting a change detection probability graph and a binary change mask; based on the change detection probability graph and the binary change mask, attribute discrimination is carried out on the change area in combination with the ground feature semantic prior graph, and a change area result is output; and integrating the change area result to a geographic information service platform. The improved U-shaped twin network is adopted, and automatic change detection of the multi-temporal remote sensing image is achieved.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention discloses a land cover change and thermal environment influence research method based on multi-temporal data, and the method comprises the following implementation steps: 1), obtaining a multi-temporal remote sensing image of a to-be-monitored region, carrying out the land cover classification mapping research of the remote sensing data of different time phases of the to-be-monitored region, and carrying out the statistics of the area of each land cover type; 2) performing surface temperature inversion on the remote sensing data of different time phases of the to-be-monitored area, grading the temperature, and analyzing the temporal and spatial change conditions of different temperature grades; 3) in the aspect of exploring the influence of the land coverage and the change thereof on the surface temperature, firstly, the temperature response of the surface temperature of different land coverage types in different seasons is analyzed; secondly, semi-quantitatively researching the relationship between the surface temperature and the surface temperature by introducing surface characteristic parameters; then quantitatively analyzing the contribution ratio of the land cover type to the surface temperature after the land cover type is changed; and finally, by introducing a distribution function, quantitatively analyzing heat source area information of different land coverage types in the research area.

The invention relates to the technical field of land coverage remote sensing monitoring, in particular to a time sequence SAR crop classification method based on multi-scale cross attention. According to the method, the complex phenological relationship of crops is considered, the features of time and space dimensions of a time sequence SAR image are fully utilized, an input array is mapped to a high-dimensional feature space through an encoder, and multi-scale local space features are extracted; constructing a multi-scale cross attention module MCMSABlock to extract multi-scale time features of the time sequence SAR image block, taking a weighted result of an original scale as a query matrix of other scales, indexing and weighting, and extracting remote dependency information at different time scales; the decoder adopts a plurality of up-sampling modules to gradually recover the spatial scale, and multi-scale time features are fused while multi-scale local spatial features are reserved by utilizing jump connection. The method is suitable for crop diversity and complex time sequence SAR scenes, the requirement for a memory is reduced while an accurate crop classification result is obtained, and the classification efficiency is improved.

The invention provides a method for improving flood inundation simulation precision based on fusion of VGI data and optimization of DEM, and the method comprises the steps: selecting HEC-RAS as a flood inundation simulation model, and inputting data including DEM data, river flow data, precipitation data and land coverage type data; according to a Manning coefficient range table provided by the land coverage database, setting corresponding Manning coefficients for different coverage types, and preliminarily setting the Manning coefficients as the median of each coverage type; the Manning coefficient setting is iteratively adjusted by comparing the difference between the simulation result and the actual water depth data; establishing a relation chain between the extracted water depth information and the submerged simulated water depth; height measurement data of a land observation satellite is combined with the COPDEM to estimate a residual error between the COPDEM at a water depth point position and a real elevation, and a conversion relation is established between extracted water depth and simulated water depth of a relatively optimized DEM according to an elevation difference value between the DEM and the COPDEM. Based on the technical scheme of the invention, the simulation precision is obviously improved, the VGI data is successfully combined with the optimized DEM, and the accurate correction of the Manning coefficient is realized.

The invention discloses a large-scale forest land carbon reserve estimation method and system based on an active and passive remote sensing technology. The method comprises the following steps: acquiring satellite-borne laser radar data of different sensors in a research area, SAR data of an L wave band and a C wave band, multispectral remote sensing image data, airborne laser radar data, topographic data and land coverage category data containing forest land; discontinuous combined spaceborne laser radar canopy height products are obtained by using spaceborne laser radar data of different sensors; improving a geographic weighted regression model to estimate the canopy height of a continuous scale; extracting polarization parameters by using C-band SAR data, extracting vegetation indexes and texture indexes by using multispectral remote sensing image data, constructing characteristic variables together with topographic data, and screening the characteristic variables based on a variance reduction criterion; and constructing an overground carbon reserve inversion model, and carrying out large-scale forest land carbon reserve estimation. According to the method, the forest land carbon reserve estimation precision of the large-scale regional broken plot is improved.

The invention discloses a city weather simulation prediction method based on an improved mesoscale WRF model, and the method comprises the steps: obtaining geographic space information data, carrying out the preprocessing, coupling local climate partitioning and population density grading, carrying out the detailed division of a built earth surface, carrying out the classification conversion of a natural earth surface according to a butt-joint mapping relation, and carrying out the prediction of the natural earth surface. The method comprises the following steps: obtaining a land cover classification data set and calculating to obtain corresponding urban canopy parameters, then improving default underlying surface data built in a mesoscale WRF model, and performing simulation prediction on urban meteorology by using the improved mesoscale WRF model. According to the method, through detailed division of the built-up surface and classification conversion of the natural surface, a surface coverage fine classification data set conforming to the real condition and considering the complex non-uniformity of the built-up area and corresponding city canopy parameters are constructed, then default underlying surface data built in the mesoscale WRF model are improved, and the city meteorological simulation prediction capability is remarkably improved.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses a regional thermal environment analysis implementation method and device and a storage medium, and relates to the technical field of thermal environment analysis. According to the regional thermal environment analysis implementation method, the change trends corresponding to all adjacent positions of different land cover types in remote sensing data can be mined by lengthening the time dimension, and the weight parameter corresponding to each land cover is determined by combining all the change trends; the weight parameter can measure the influence degree of the earth surface coverage on the thermal environment of the target area. And finally, analyzing the thermal environment corresponding to the target area according to all the land surface coverage and the weight parameter corresponding to each land surface coverage. Therefore, the effective content of the remote sensing data can be fully mined, so that the accuracy and reliability of regional thermal environment analysis are improved.

The invention provides a sunlight-induced chlorophyll fluorescence reconstruction method and system based on satellite observation data. Comprising the following steps: collecting and processing ground site observation data, air temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir correction reflectivity data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, and OCO-2 and TROPOMI satellite observation SIF data; performing data reconstruction by using the OCO-2 SIF and the TROPOMI SIF as original data respectively by using a machine learning method; then, an empirical formula between the two kinds of reconstructed SIF data and the site observation SIF is constructed, and estimation parameters on the site are obtained through optimization; then, calculating estimation parameters of the empirical formula on each land coverage type; and finally, substituting the two types of reconstructed SIF data into an empirical formula to obtain a reconstructed SIF with site SIF information.

The invention discloses a drought disaster risk quantitative assessment method fusing an irrigation disaster reduction effect, and belongs to the field of disaster risk assessment, and the method comprises the steps: obtaining different meteorological data of a target region in a three-dimensional space grid scale, and calculating a drought index; a drought event is identified by using an ST-KMeans clustering method; drought events are screened and combined, the space overlapping degree of the clustered drought region and the historical disaster region is calculated, and risk loss data docking is carried out; generating a drought event characteristic dynamic data set, and measuring the drought event; and in combination with the land coverage and utilization data of the target area and the farmland irrigation area data, extracting irrigation response information serving as a drought adaptability measure, and carrying out irrigation disaster reduction capability quantification and risk assessment. According to the method, irrigation information can be integrated on the basis of drought event identification, the drought slow release effect of irrigation in different time and space scales is quantitatively evaluated, and a scientific basis is provided for drought disaster risk management and agricultural adaptability policies.

The invention relates to a remote sensing image semantic change detection method based on difference feature guidance, and the method comprises the steps: obtaining difference features through employing a pixel-level subtraction method, employing a guidance segmentation branch to only focus on the segmentation of a change region, finally generating an accurate land coverage map, and guiding a change branch to generate a clear edge of the change region. According to the method, semantic segmentation is more accurate, and the edge of a change region is clearer.

The invention provides a method for improving urban green land biological source emission based on a high-resolution global mode, and provides a technical scheme for optimizing a biological source emission list by using high-resolution land coverage data aiming at the problem of urban green land emission deficiency in a current numerical model. The technologies comprise a weak supervision global land coverage mapping method, spectral information of images can be fused, and features extracted by a visual large model are used for drawing high-resolution land coverage images; the vegetation function type matching method is used for converting the high-resolution land coverage data into vegetation function types; and preparing a high-resolution numerical mode input file, converting the file into an input format suitable for a high-resolution earth system mode, carrying out numerical simulation, and estimating global biological source emission, especially urban green space emission.