421 results about "Street scene" patented technology

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention provides a multi-modal data urban land utilization scene classification method based on street scene image matching remote sensing image, designs a cross-view image matching method based on geometric transformation, and realizes spatial position matching of a multi-view street scene image and a remote sensing image; the method comprises the following steps: constructing an urban land utilization scene classification model based on a high-resolution remote sensing image and a streetscape image by utilizing a pre-trained large model with relatively strong generalization ability, and extracting depth features of different scales and different visual angles; designing a global-local feature fusion module, and aggregating features of different levels through self-attention; a gate control cycle fusion module is designed to realize interaction of streetscape images at different shooting time and shooting view angles; through a channel attention mechanism, remote sensing image features and streetscape image features are distributed according to different weights, contribution proportions of the features in different classification tasks are optimized, a dynamic gradient modulation strategy is designed, and different modal information is adaptively fused, so that accurate classification of urban land utilization remote sensing images is realized.

The invention relates to a deep learning-based historical urban style integrity evaluation method and system. The method comprises the following steps of: obtaining urban street view image data of a plurality of point locations; extracting a sky ratio and a green vision rate of an urban street scene image according to the urban street scene image data, respectively taking the sky ratio and the green vision rate as a street profile scale spatial feature and a natural element spatial feature, and calculating a signage color number and a color harmony degree of the urban street scene image as building color spatial features; calculating a traditional material proportion and material consistency of the urban street scene image as material mechanism spatial features; for the urban street view image data, obtaining an expert score obtained based on an ELO scoring algorithm; and constructing and training an evaluation model based on an artificial neural network based on the various spatial features and the small-scale expert scoring data set, and realizing historical urban style and appearance integrity evaluation by using the evaluation model.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention discloses a time sequence streetscape image-based building update type identification method and device, and relates to the technical field of civil engineering and computer vision. The method comprises the following steps: acquiring a plurality of streetscape images; outputting a target detection result of the building through a YOLO target detection model; according to the metadata information of the streetscape image and the target detection result, determining geographic coordinates of the building; constructing an initial building group geographic information database by combining a target re-recognition algorithm according to the geographic coordinates; constructing a building update type identification data set according to the initial building group geographic information database; building a building update type identification model; using the building update type identification data set to train a building update type identification model; and outputting an update type identification result of the building through the trained building update type identification model. And storing the update type identification result to the initial building group geographic information database to obtain a target building group geographic information database.

The invention provides an element feature measurement method and device for block style and appearance shaping, which can be applied to the field of image processing and the field of urban and rural planning and design. The method comprises the steps that a trained semantic segmentation model is used for processing a streetscape image, an element type proportion and a segmented image are obtained, and the element type proportion represents the area proportion of elements with element types in the streetscape image; processing the streetscape image by using a large language model to obtain a streetscape description text; performing feature extraction on the streetscape description text to obtain streetscape semantic features; streetscape visual features and streetscape semantic features obtained by performing feature extraction on the element type proportions and the segmented images are fused, and multi-modal fusion features are obtained; and inputting the multi-modal fusion features into a trained evaluation model, and outputting an evaluation result, the evaluation result comprising a plurality of evaluation values used for measuring features of each element in the streetscape image for different evaluation dimensions, and the evaluation result being used for shaping block styles and features with endemic features.

The invention belongs to the technical field of city planning, and discloses a dynamic adjustment method for a city update planning scheme and related equipment, and the method comprises the steps: obtaining city street view video data through the normalized cruise shooting of an unmanned plane; preprocessing the urban street scene video data, identifying building facade damage information and traffic information from the preprocessed urban street scene video data, and evaluating a building facade damage level and a traffic influence level; based on a preset building facade damage grade threshold value, a traffic influence grade threshold value, a building facade damage risk weight and a traffic influence risk weight, constructing a weighted risk assessment model, and according to the building facade damage grade and the traffic influence grade, calculating a comprehensive risk score; determining an adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban update planning scheme; therefore, the adjustment efficiency of the urban update planning scheme can be improved.

The embodiment of the invention discloses an address search method and device based on deep learning, a storage medium and a server, and relates to the field of logistics information processing. The method comprises the following steps: firstly, acquiring data such as text addresses, space coordinates, peripheral POIs (Point of Interest) and streetscape pictures from a multi-modal address database; text addresses are decomposed into different levels and converted into corresponding vectors, and then the vectors are spliced into text address vectors. The space coordinates, the POI and the streetscape pictures are also converted into vectors respectively, and the vectors are subjected to weighted summation to generate multi-modal vectors; meanwhile, the text address entity inquires the knowledge base to obtain semantic enhancement information, encodes the semantic enhancement information into a vector, splices the vector with the multi-modal vector to form a final address vector, and stores the final address vector into an address vector library. After the user inputs the query text address, the query vector is generated, the candidate address vector with high similarity is retrieved in the library, and detailed information of the candidate address is displayed on the interactive interface, so that the accuracy and richness of address description are improved, and an efficient address query function is realized.

A method and an apparatus for predicting a road traffic carbon emission based on panoramic images, a device and a medium are provided, relating to the technical field of data prediction. In the method, a historical street view image of an observation area is acquired from the Internet, feature analysis is performed on the historical street view image to obtain a historical feature vector, and a road traffic carbon emission predicted value is obtained based on the historical feature vector and a carbon emission prediction model. With the method or the apparatus, auxiliary explanations for carbon emission sources in cities can be provided based on features of street views.

The invention discloses a shielding complementing method based on a continuous streetscape panoramic image, and aims to solve the problem of information loss caused by shielding of dynamic objects (such as vehicles and pedestrians) in the existing streetscape image, and the method comprises the following steps: S1, recognizing and positioning a dynamic shielding object area in a continuous streetscape panoramic image sequence; s2, providing a continuous panoramic image-oriented feature extraction and matching algorithm (FDMPano), realizing robust feature retrieval and matching in a cross-view angle, and accurately positioning a reference image region which can be complemented; and S3, carrying out feature alignment and content mapping on the sheltered area based on a matching result, and generating an unsheltered panoramic image with consistent vision. According to the method, the internal relevance of continuous streetscape data is fully utilized, the occlusion area can be automatically completed with high quality, the integrity and availability of streetscape images are remarkably improved, and the method has wide application value in the fields of automatic driving, digital cities, virtual reality and the like.

According to the cold region city emotion perception evaluation and measurement method, a cold region city is selected as a research object, and physiological signals and subjective scores are comprehensively considered; street view images are scored by training the street view perception model, perception of the street view images is realized, physiological signals and subjective scores are combined to realize comprehensive evaluation of corresponding areas of the cold region city, and a comprehensive evaluation result is less subject to subjective influence and is stable; the method can analyze the influence of the winter street space of the cold region city on the residential emotion perception, accurately measure the quality of the cold region city street space influencing the residential emotion, has the advantages of high efficiency and easy popularization and application, can be widely applied to the field of urban planning living street space quality investigation and measurement, and has a wide application prospect. And a method and a basis are provided for cold region city planning street space optimization practice.

The invention discloses an environmental health risk assessment system, electronic equipment and a nonvolatile storage medium. The system comprises an environment image acquisition module which is used for acquiring an environment image of a target area; the image feature extraction module is used for performing feature extraction on the environment image to obtain a first feature corresponding to the streetscape image and a second feature corresponding to the remote sensing image; the environment variable prediction module is used for fusing the first feature and the second feature, and determining a parameter value of an environment variable corresponding to the target area according to a fused feature obtained after fusion; and the comprehensive index determination module is used for determining a comprehensive environment index corresponding to the target crowd object in the environment of the target area according to the parameter value of the environment variable. According to the method and the device, the technical problem that the environmental health risk cannot be effectively assessed due to the fact that a method for measuring and analyzing the environmental variables mainly depends on field monitoring, the efficiency is low and the data coverage is narrow in the prior art is solved.

The invention discloses a greenbelt cooling effect evaluation method and system based on a remote sensing image and a streetscape image, and the method comprises the steps: obtaining the data of the streetscape image, carrying out the denoising and cutting, recognizing, extracting and calculating the pixel coverage rate of arbors, grass trees and shrubs, and constructing the greening feature indexes of the streetscape image; acquiring an urban satellite remote sensing image, performing radiometric calibration, correction and identification, and constructing a greening characteristic index of the remote sensing image; acquiring a multispectral remote sensing image of the city, performing cloud detection, and obtaining the surface temperature of the city through surface temperature inversion; and constructing a random forest model and performing iterative training based on greening characteristic indexes of the surface temperature, the streetscape image and the remote sensing image of the city, and evaluating the cooling effect by using the fitted random forest model. The method gives full play to the advantages of wide coverage range of the remote sensing image and rich details of the street scene image, makes up for the limitation of a single data source, and achieves the comprehensive and accurate evaluation of the cooling effect of the green land.

The invention provides a city streetscape image real-time semantic segmentation method based on attention boundary enhancement and an aggregation pyramid. The method comprises the following implementation steps: obtaining a training sample set and a test sample set; constructing an urban street scene real-time semantic segmentation network model based on attention boundary enhancement and an aggregation pyramid, and carrying out iterative training on the urban street scene real-time semantic segmentation network model; and obtaining a real-time semantic segmentation result of the city street scene image. According to the method, the sensitivity to boundaries is enhanced through the attention boundary enhancement sub-network, context information is integrated by using feature perception from coarse to fine, the robustness of semantic information is improved, meanwhile, the pyramid pooling network is aggregated to carry out feature splicing on pooling feature maps of different scales, the network generates correct semantic tags, and the accuracy of the semantic tags is improved. And finally, the global feature information graph, the detail feature information graph and the boundary feature information graph of each sample output by the three networks are fused, so that the recognition precision of the semantic segmentation image is improved.

The invention discloses an urban traffic three-dimensional model vegetation element automatic generation method and system based on streetscape recognition, a terminal and a storage medium. The method comprises the steps of constructing a vegetation element knowledge graph, defining a vegetation type and typical attributes, directionally collecting a three-dimensional model and a multi-view image, unifying metadata and storing the metadata. Pixel-level semantic segmentation is carried out on the streetscape image, vegetation categories are identified based on transfer learning and an attention mechanism, and ROI and feature vectors are output; screening the candidate set based on ontology mapping, selecting a best matched vegetation model for the local feature vector of the recognized ROI and a pre-stored feature vector in a material library, and recording mapping; based on reference object correction, knowledge graph fusion and automatic discrimination or optimization strategies, reliable scale correction, number or position deduction and batch three-dimensional model import placement meeting ecological constraints are carried out on the vegetation ROI. According to the invention, efficient, intelligent and standardized technical support is provided for traffic simulation and urban visualization.

The invention discloses a street scene reconstruction method, device and system and a storage medium. The reconstruction method comprises the following steps: acquiring a target image set shot for a target street scene; reconstructing a target street scene based on the target street scene model according to the obtained target image set; wherein the target street scene model is trained based on a first data set and a second data set, and the first data set is constructed based on a training image set shot for the target street scene and is used for training a three-dimensional model to obtain an initial street scene model; the second data set is constructed based on an unseen view angle image set and a shot training image set, and the unseen view angle image set is obtained by using the obtained initial street scene model. As the used target street scene model is obtained through iterative optimization of the initial street scene model, geometric reconstruction precision is improved when the target street scene model is used for street scene reconstruction.

The invention discloses an urban street annual green vision rate calculation method based on multi-temporal streetscape images, and the method comprises the steps: generating streetscape sampling points according to road network data, collecting a plurality of temporal streetscape images of each streetscape sampling point covering all seasons of the whole year, carrying out the semantic segmentation of the streetscape images through a deep learning model, and obtaining the annual green vision rate of a city street. And extracting a vegetation region in the streetscape image, and performing mathematical expectation calculation on the green vision rates under different time phases to obtain the annual green vision rate of the urban street. According to the method, annual street view data are accurately collected and processed, and an effective means is provided for scientific evaluation of urban street greening by combining a deep learning technology and an image segmentation technology. According to the method, the urban planning department can be helped to know the greening condition more accurately, important decision support can be provided for subsequent urban greening improvement and street landscape optimization, and meanwhile, the method is high in automation degree and suitable for practical application of large-scale urban greening evaluation.

The invention provides a signal system and method for urban street view visual emotion prediction and application. The signal system comprises a monitoring module, a processing module, a first construction module, a second construction module and an output module. The monitoring module is used for collecting electroencephalogram signals and emotion evaluation reaction indexes when the testee watches the streetscape image; the processing module obtains relative power of electroencephalogram signals of a single-image presentation section and a multi-image sequence section; the first construction module is used for constructing an electroencephalogram signal emotion prediction model by using multi-image sequence segment electroencephalogram signal relative power and emotion evaluation reaction indexes, and is applied to single-image presentation segment electroencephalogram signals to obtain an emotion reaction of any streetscape image; the second construction module is used for constructing a machine learning model, obtaining a streetscape image emotion prediction model based on electroencephalogram signal recognition, and applying the streetscape image emotion prediction model to the output module to obtain a city streetscape visual emotion map; the method has the advantages of being sensitive in expression and accurate in emotion capture.

The invention relates to the field of smart cities, in particular to an urban landscape semantic segmentation method and system based on a large model, and the method comprises the steps: obtaining street view image data of a target geographic region; inputting the streetscape image data into a preset image semantic segmentation model, analyzing the streetscape image data through the image semantic segmentation model, and identifying various predefined environmental element categories contained in each image; calculating an urban landscape quantitative index based on the recognition result of the environmental element category; associating the calculated value of the urban landscape quantitative index to a corresponding road section or node in the road network spatial data corresponding to the target geographic area; generating a road network space visualization layer representing the value of the index value in a color grading manner based on the result after association; and according to a preset index threshold value, identifying the spatial road sections or nodes meeting the early warning conditions in the visual layer and generating prompt information. And the scientificity and comparability of an analysis result are ensured.

The invention discloses a land utilization classification method based on multi-source data intelligent fusion, and the method comprises the steps: obtaining multi-source geographic space data, including remote sensing image data, streetscape image data, building vector data, road network vector data, interest point data, mobile phone signaling data and microblog sign-in data, of a research region, and forming a land utilization classification experiment data set; remote sensing image features and streetscape image features of each research unit are constructed, environment features, interest point features and population distribution features are established, and a feature set of land utilization classification tasks is formed; combining the constructed features in pairs to generate feature pairs formed by features of different modalities, performing feature alignment on the features of different modalities through a comparative learning method, and adding the aligned features into a feature set of a land utilization classification task; and land utilization classification is carried out based on an XGBoost integrated learning model, and model validity inspection is carried out by adopting five-fold cross validation. According to the invention, the accuracy of land utilization classification is improved.

The invention discloses a dynamic street scene reconstruction method and device based on a four-dimensional Gaussian scene graph, which are used for synthesizing a new visual angle image of a dynamic street scene by optimizing four-dimensional Gaussian and realizing high-fidelity reconstruction of the street scene. The method comprises the following steps: giving an image of a dynamic street scene, a camera pose and image annotation information, respectively optimizing a static street background and a dynamic object through four-dimensional Gaussian, and combining into a four-dimensional Gaussian scene graph to represent the dynamic street scene; each node in the four-dimensional Gaussian scene graph stores a group of four-dimensional Gaussian parameters; and high-fidelity reconstruction of a dynamic street scene is realized based on the four-dimensional Gaussian of the graph. And giving any view angle of the dynamic street scene, selecting a corresponding four-dimensional Gaussian node from the four-dimensional Gaussian scene graph, and combining four-dimensional Gaussian according to the image annotation information to render the image of the dynamic street. According to the method, high-fidelity modeling of the dynamic street scene is realized through the four-dimensional Gaussian scene graph, and the rendering quality of the dynamic street scene is improved.

The invention provides a projection transformation method for hexahedral panoramic data to spherical panoramic data, and the method achieves the seamless splicing of panoramic images of six surfaces of a cube and the mapping of the panoramic images to a spherical coordinate system through the determination of a direction vector, the normalization of the vector, the conversion of spherical coordinates, the projection to an equidistant histogram, and an illumination and color correction algorithm. The problems of splicing gaps, deformation, texture distortion and the like in a traditional method are solved. The method is not only widely applied to the fields of virtual reality, augmented reality, panoramic video production and the like, but also greatly expands the application scenes, including but not limited to panoramic roaming, streetscape roaming experience, integrated use of specific roaming plug-ins, seamless embedding on a map platform and the like, and has wide application prospects. Therefore, the display effect of the panoramic data and the immersive experience of the user are remarkably improved.

The invention relates to the technical field of image segmentation, and discloses a street view image semantic segmentation system for a complex city scene, and the system comprises a double-edge energy ratio calculation module which obtains a double-edge energy ratio through calculation; the priori mask generation module is used for generating a priori mask; the segmentation network construction module is used for outputting logarithmic probabilities and probabilities of various categories through a trunk segmentation network; the total loss construction module is used for constructing total loss; the mutual suppression optimization module is used for calculating to obtain a preliminary semantic segmentation result; and the semantic segmentation output module is used for calculating to obtain a final semantic segmentation result. According to the method, a unified process of double-edge energy ratio driving direction convolution, separation loss and reasoning period mutual suppression is adopted, repeated boundaries and false objects caused by reflection and transmission mixing in a complex city scene containing glass and a water surface are reduced, pixel-level boundary positions are stabilized, and road space, building facades and traffic element division are consistent.

The invention relates to the field of city analysis, and discloses a method for analyzing the nonlinear influence of a city environment on city vitality, and the method comprises the steps: carrying out the preprocessing of multi-source geographic big data and multi-source remote sensing data; according to a Deeplab V3 + deep learning semantic segmentation algorithm, predicting the pixel ratio of each category in each streetscape image, and constructing streetscape features based on the pixel ratio; constructing urban vitality evaluation indexes, and aggregating the urban vitality evaluation indexes by using a TOPSIS algorithm to obtain an evaluation result of the urban vitality; constructing an urban environment index according to the preprocessed multi-source remote sensing data; analyzing the spatial distribution difference of the non-linear influence of the urban environment on the urban vitality by using a geographically weighted random forest algorithm; and analyzing the threshold effect of the non-linear influence of the urban environment on the urban vitality by using a Gaussian fitting line algorithm. According to the method, street view features are extracted through a semantic segmentation technology, and comprehensive, scientific and refined quantitative evaluation of urban vitality is realized.

The invention discloses an urban non-fortification building reinforcement identification method and device based on historical streetscape pictures, and relates to the technical field of civil engineering technology and image processing, and the method comprises the steps: constructing a streetscape sequence data set containing time sequence features according to the historical streetscape pictures and streetscape metadata, the method comprises the following steps: firstly, constructing a streetscape feature extraction model, marking streetscape pictures of non-fortified buildings in different reinforcement states to obtain a streetscape training set, and training the streetscape spatial-temporal feature extraction model which is an improved FCOS network; inputting a streetscape sequence image to be recognized into the trained streetscape spatial-temporal feature extraction model, and determining a reinforcement state and a change time point of a non-fortified building in the streetscape sequence image; and in combination with geographic information data, geocoding is performed on the identified building, the spatial distribution and change trend of the reinforced building are obtained, and a building reinforcement thermodynamic diagram over the years in the region of interest is obtained. According to the invention, the efficiency of building safety management can be greatly improved, and the investment of human resources is reduced.

The invention discloses a geographic position identification method of a streetscape image and a model training method and device.The training method is used for training a multi-modal model based on comparative learning and comprises the steps that the model is used for obtaining image feature vectors of streetscape image samples in an image sample set of a geographic area; obtaining text feature vectors of streetscape image sample text tags of a plurality of geographic areas in the tag sample set by using the model; combining the text feature vectors and the streetscape fusion features by using a model to obtain multi-modal fusion features of a plurality of geographic areas; acquiring the similarity between the image feature vector and the multi-modal fusion features of the plurality of geographic areas by using the model; and performing loss calculation according to the similarity and the geographic region label of the streetscape image sample, and adjusting model parameters according to a loss calculation result. The technical scheme provided by the invention is beneficial to improving the training efficiency of the model, enriching the implementation modes of geographic position recognition and improving the accuracy of geographic position recognition.

The invention provides a panoramic streetscape image generation method and system, and belongs to the technical field of image data processing. According to the method, the complete panoramic image with real distortion is directly generated, a multi-view image splicing mode is not needed, and the high-fidelity panoramic streetscape image with global brightness consistency, seamless connection and real distortion can be generated on the basis of reserving accurate geometric control; moreover, the authenticity and consistency of the image are improved, the complexity of multi-view generation is reduced, and the applicability and real-time performance in an automatic driving scene are remarkably improved. Multi-scale geometric control and conditional coding are introduced, a pre-training diffusion model is combined, a panoramic streetscape image with real distortion is generated from multi-condition input of a road BEV map, a 3D target frame, a camera posture, text description and the like, geometric details such as road elevation and target object height can be accurately controlled in the generation process, and the image quality is improved. And the training effect of the 3D perception task is obviously improved.

The invention relates to an unsupervised field adaptive streetscape semantic segmentation method. The method comprises the following steps: establishing a PixEL unsupervised field adaptive network model; the method comprises the following steps: firstly, training a segmentation network Deeplabv2multi through a source domain data set to obtain a pre-trained segmentation model; perturbation is added to the features, obtained through a Deeplabv2multi encoder, of the image in a target domain, and a feature perturbation enhanced version of the image is obtained; performing consistency training on the strong enhancement version, the weak enhancement version and the feature disturbance version of the target domain image by using the segmentation model; in a target domain, energy scores of unmarked samples are calculated to evaluate the closeness degree of the unmarked samples and current training distribution, samples with higher energy scores are selected to generate pseudo labels, and the pseudo labels are utilized to further carry out optimization training on the model. Compared with an existing unsupervised field self-adaption method, the method has the advantages that a better semantic segmentation effect is achieved on a real reference data set through synthesis, the subjective vision and objective evaluation indexes are obviously improved, and the unsupervised field self-adaption problem in the street scene image semantic segmentation field is effectively solved.

The invention discloses a green vision index estimation model construction and green vision index monitoring method and device, and belongs to the technical field of geographic information, and the method comprises the steps: obtaining a plurality of remote sensing slice images and a plurality of streetscape images corresponding to the remote sensing slice images; wherein each remote sensing slice image corresponds to a road network; determining a sample label corresponding to the remote sensing slice image according to the plurality of streetscape images; calculating a corresponding auxiliary feature map according to the remote sensing slice image; wherein the auxiliary feature map comprises a normalized vegetation index map, a depth map and a visual field perception intensity map; and according to each remote sensing slice image, each auxiliary feature map and each sample label, training and verifying a preset deep learning model, and obtaining a green vision index estimation model. Therefore, by implementing the method and the device, the problem of how to perform large-range and periodic monitoring on the street green vision index under the pedestrian view angle can be solved.