252 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 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.

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.

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 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 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 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 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.

The invention discloses a multi-fractal-based urban green vision rate dynamic evolution mode identification method. The method comprises the following steps: S100, acquiring urban street green vision rate data; s200, acquiring urban multi-scale walking isochronous circle data, and acquiring walking reachable areas within 5 minutes, 10 minutes, 15 minutes and 20 minutes through an API (Application Program Interface) of an OpenRouteService platform on the basis of an urban road network; s300, identifying a dynamic evolution mode of the green vision rate; and S400, identification result demonstration, including spatial distribution of a green vision rate dynamic evolution mode, key threshold identification and spatial structure differentiation, is used for assisting urban greening structure optimization and scientific intervention strategy formulation. According to the method, the problems of staticization, single scale and insufficient structure expression in the existing urban street view green visual rate analysis are solved.

The invention relates to the field of city planning, and discloses a city spatial evolution prediction system fusing multi-modal data, and the system comprises a data preprocessing and multi-modal knowledge graph construction module which is used for configuring a knowledge graph ontology architecture and preprocessing multi-source city data; establishing a multi-modal knowledge graph based on the preprocessed multi-source city data and the knowledge graph ontology architecture; the model setting module is used for configuring a multi-modal representation learning model; the data integration module is used for a multi-modal representation learning model and performing deep integration on multi-modal data in a vector space to obtain vector representation with remote sensing and streetscape semantic information; and the knowledge reasoning and predicting module is used for performing knowledge reasoning on the missing part in the multi-modal knowledge graph based on the vector representation so as to predict the urban spatial evolution event. The urban spatial evolution event prediction can be realized, and the problem that the remote sensing image and the streetscape image are difficult to be deeply fused is effectively solved.

The invention discloses a portable urban thermal environment intelligent sensing method and equipment based on AR, and the method comprises the steps: collecting a panoramic RGB street scene image of a user visual angle through AR glasses, and synchronously obtaining the time, geographic position and meteorological parameters; mapping the RGB image and the environmental parameters into a panoramic infrared thermal image by using a generative adversarial network, and obtaining the radiation temperature of each pixel; projecting the panoramic heat map to a unit spherical surface taking a user as a center, calculating a long-wave radiation flux through spherical surface angle integration, and calculating a short-wave radiation flux based on a solar radiation model by combining a solar elevation angle and the like; and fusing the total radiation amount to calculate the average radiation temperature, and finally superposing the heat map and / or the average radiation temperature to a real or virtual environment through AR glasses or an intelligent terminal to realize spatialization thermal load visualization.

Embodiments of the present application relate to the technical field of artificial intelligence. Disclosed is a training method for a street scene understanding model assisted by a large vision model, comprising: extracting a street scene instantaneous feature of the current street scene image frame, and extracting street scene robust invariant features of all historical street scene image frames; respectively acquiring the difference between the current street scene image frame and each historical street scene image frame, and extracting street scene dynamic features of all street scene difference images; randomly mixing the street scene instantaneous feature, the street scene robust invariant features, and the street scene dynamic features to obtain a mixed street scene feature; performing street scene semantic segmentation on the mixed street scene feature by means of an initial street scene understanding model to obtain a predicted street scene mask of the current street scene image frame; generating a sample street scene mask of the mixed street scene feature by means of a large vision model; and updating model parameters of the initial street scene understanding model on the basis of the predicted street scene mask and the sample street scene mask to obtain a target street scene understanding model. Thus, the accuracy of street scene understanding is improved.

The invention discloses a street safety perception evaluation method and system based on electroencephalogram characteristic waves, and relates to the technical field of construction of environment safety sensing degree, and the method comprises the steps: obtaining an electroencephalogram signal induced by a street scene, extracting a power spectrum value of the electroencephalogram signal, and calculating the power spectrum value of the electroencephalogram signal; based on the power spectrum value of the electroencephalogram signal, calculating the asymmetry of an electroencephalogram prefrontal lobe alpha frequency band and a cognitive load and pressure balance index, and converting the asymmetry and the cognitive load and pressure balance index into electroencephalogram safety perceptibility of a street scene; acquiring safety perception element feature data of the street scene, and inputting the safety perception element feature data of the street scene and the electroencephalogram safety perceptibility of the street scene into a pre-established street safety perceptibility evaluation model for iterative training to obtain a trained street safety perceptibility evaluation model; and based on the trained street safety perceptibility evaluation model, quantifying the feature contribution of the street environment elements to the safety feeling score predicted value by adopting an SHAP method, and obtaining a street safety perception evaluation result.

The invention provides an urban visual navigation method, device and equipment based on a large language model and a storage medium, and relates to the technical field of visual navigation, the method comprises the following steps: constructing a fine tuning data set based on a plurality of first streetscape images of a target city, and performing fine tuning on a multi-modal large language model by using the fine tuning data set to obtain a multi-modal large language model; the annotation information of each first streetscape image comprises landmark position and distance information corresponding to each first streetscape image; determining an intelligent agent system for urban visual navigation based on the multi-modal large language model after fine tuning; based on the natural language description of the target position, the intelligent agent system repeatedly executes the processes of sensing, reflecting, planning and acting until the target navigation task is completed, the target position description comprises the position relation between the target and the landmark, and the position relation comprises the relative orientation and distance; the target navigation task is used for representing a navigation task from the current position of the intelligent agent system to the target position. According to the invention, autonomous navigation in a city scene is realized through the intelligent agent system.

The invention provides an urban multi-modal large model training method, device and equipment and a medium, and relates to the technical field of artificial intelligence. Constructing an image-text interlaced data set according to the satellite image description text corresponding to each satellite image and the streetscape image description text corresponding to each streetscape image; and training the city multi-modal large model based on the image-text interlaced data set, and adjusting perceptron parameters corresponding to the perceptron in the city multi-modal large model and large language model parameters corresponding to the large language model to obtain a trained target city multi-modal large model. According to the technical scheme, the problems that in the prior art, a training data source is single, and images and texts are independently processed are solved, the trained target city multi-mode large model has good understanding on the city environment, and the space structure and function characteristics of a city can be comprehensively captured.

The invention discloses a streetscape image semantic segmentation method and system based on feature perception, and relates to the technical field of computer vision, and the method comprises the steps: employing a panoramic segmentation large model to carry out the preliminary segmentation of a streetscape image, outputting a coarse segmentation mask image, and dividing the streetscape image into an identified region set and an unidentified region set; for the recognized region set, combining similar fracture regions based on the intersection-to-union ratio and feature similarity of adjacent recognized regions, matching a small segmentation model according to the label of each combined region, and performing secondary segmentation by using the matched small segmentation model to generate semantic elements of the recognized region set; for the unidentified region set, performing secondary segmentation on the unidentified region by using all the small segmentation models to generate semantic elements of the unidentified region set; and finally, outputting a panoramic semantic segmentation result of the streetscape image, and through progressive processing from coarse segmentation to fine segmentation, realizing accurate distinguishing of similar confusion elements and completeness of semantic segmentation in a complex scene.

The invention relates to a traffic facility attribute mining method and system based on multi-modal network open source data. The method comprises the following steps: extracting traffic facility information from a webpage to construct a knowledge graph; extracting positions and appearances of traffic facilities from the images, and analyzing streetscape images to obtain attributes such as road traffic; collecting map images, tiles and vector data to construct a road network topology and attribute database; associating webpage texts, pictures, streetscape images and network map multi-source data according to the spatial position of the traffic facility; through comprehensive and deep attribute mining, different modal data are integrated to improve the accuracy and reliability of attribute mining, the real-time and dynamic updating capability, the convenient visualization and interaction operation, the data sharing and integration convenience, and the traffic facility management efficiency and collaboration are improved. According to the method, comprehensive, accurate and real-time traffic facility attribute data support can be provided for urban traffic planning, traffic management and intelligent traffic system construction, so that the efficiency and the intelligent level of traffic facility management are remarkably improved.

The invention discloses an urban village identification method based on a graph neural network and multi-source geographic big data, and relates to the technical field of geographic images, and the method comprises the steps: obtaining remote sensing data, social perception data and building footprint data of an urban village; based on road network trajectory data, cutting built-up area boundary data through a hierarchical buffer area strategy to obtain a basic evaluation unit; the streetscape image data, the satellite image data, the interest point data, the building attribute data and the building form data are preprocessed respectively, and visual features, social economic features and building form features are constructed; constructing a graph network structure of the graph neural network model; and according to the instance sample group, in combination with a graph network structure of the graph neural network model, constructing and training a GraphSAGE model so as to improve the recognition probability of the villages in the city. According to the method, the spatial interaction and regional relation between the network spatial structure and the multi-dimensional attributes are effectively understood through the GNN.

The invention discloses a semantic change detection method and system supporting a block scene, and belongs to the technical field of artificial intelligence image understanding. Comprising the following steps: analyzing a geographic element relationship of a block scene, and constructing a time sequence street scene change detection data set; the method comprises the following steps: training a Transform single-stage scene graph generation model based on a Visual Genome data set, and extracting visual, semantic and spatial features; in combination with a self-built streetscape image data set, a scene graph generation model is finely adjusted to adapt to a block scene; streetscape images of different time phases are input, target detection and relation modeling are carried out, coordinates of an entity anchor frame are inferred, and a JSON file containing an inference result is generated; constructing an image space knowledge graph based on the extracted principal-called-guest triad and the space anchor frame; streetscape semantic change detection is realized through node alignment, graph difference, similarity calculation and graph visualization. The method has the advantages that the street view change is visually presented in a graph mode, and the real-time, accurate and comprehensive monitoring and analysis capability of the semantic change of the street environment is effectively improved.

The invention relates to the technical field of data processing, in particular to an urban green visual rate spatial distribution simulation method based on a green facility environment feature framework, which comprises the following steps of: acquiring a streetscape image, a satellite remote sensing image, road network distribution data and land use type grids of a whole city domain, marking a vegetation coverage area and generating an initial green visual rate grid map layer; and performing semantic segmentation on the streetscape image to calculate a vegetation pixel proportion so as to generate a green vision rate observation value of the road network region. And combining the road network weight parameter and the land use type coding parameter to form an environment parameter index when the multi-scale environment feature framework is constructed. And then constructing a spatial interpolator based on the frame, and completing non-road region green vision rate calculation in combination with an input observation value and an environmental parameter index to generate a global interpolation result. The spatial limitation of existing street scene sampling is broken through, global coverage is achieved through public geographic data, the implementation threshold of small and medium-sized cities is lowered, and a scientific decision basis is provided for heat island effect analysis and green land planning.

The invention discloses a lightweight traffic sign recognition method fusing time sequence characteristics in a shielding scene, and relates to the field of computer vision and intelligent traffic, in particular to a traffic sign recognition technology. Respectively constructing a single-frame data set with slight occlusion and a time sequence data set with severe occlusion; for a street scene image containing a complex background, training a lightweight target detector YOLOv8-N based on an existing bounding box label in a data set; secondly, a depth separable convolution structure with MobileNetV2 as a trunk network is adopted, and a high-dimensional feature map is extracted from the candidate mark image; then, a space attention mechanism is introduced, weighted correction is carried out on input features, an unshielded region is strengthened, and a shielded and noise region is suppressed; and finally, extracting a feature sequence of a historical frame of a heavily shielded sample, and inputting the feature sequence into an extended long-short-term memory network xLSTM for time sequence fusion.

The invention discloses an emotion perception quantification method considering scene semantic information based on a graph structure, which comprises the following steps of: firstly, performing label conversion preprocessing on an emotion perception data set of a streetscape image, and providing a data source for training an emotion perception model; and carrying out transfer learning by adopting a scene graph generation model based on an attention mechanism to obtain entity representation and entity relationship representation for emotion perception. Through the representations, node features and edge features are generated, and multi-dimensional image features are fused to construct richer graph structure features. Then, screening the features by applying a confidence screening rule, and carrying out feature fusion and training by using a graph convolutional network based on the screened features; the method has the advantages that the emotion-related features in the streetscape image are structured into highly explained entity and relation features, and finally quantitative model training of emotion perception is realized to reveal the relation between the city appearance and the emotion perception, deepen the understanding of the physical appearance of the city street and further assist in decision making of city planning.

The invention discloses a dynamic shelter restoration method and system based on continuous streetscape panoramic images, and the method comprises the steps: firstly obtaining a to-be-restored target panoramic image A and a to-be-restored reference panoramic image B, and generating an original pixel-level shelter mask; secondly, extracting matching points among the panoramic images, realizing cross-view geometric alignment of the images, and obtaining a target perspective view C and a reference perspective view D through perspective re-projection; and then inputting the target perspective view C and the reference perspective view D into a three-dimensional reconstruction framework, and performing three-dimensional point cloud reimaging under the camera pose of the target perspective view C by using a depth inspection mechanism. And finally, carrying out image restoration on a three-dimensional point cloud re-imaging result, restoring to a panoramic coordinate system, splicing with an original panoramic image, and outputting a shielding-free panoramic image. According to the method, it is ensured that the repairing result conforms to the authenticity and geometric consistency of the geographic space, and the problems of overlapping conflicts and visual tearing during multi-view projection fusion are effectively solved.

The invention provides a semantic segmentation method, system and equipment based on a traditional village multi-source image. The method comprises the following steps: acquiring a multi-source image and preprocessing the multi-source image; performing pixel-level labeling on the multi-source image to obtain a corresponding label graph; calculating the class imbalance weight of each class based on the tag graph, and obtaining a normalized class weight; inputting the label graph into a multi-scale context-aware coding-decoding network for feature extraction and fusion to obtain a prediction probability, constructing weighted multi-classification cross entropy loss based on a normalized category weight and the prediction probability, constructing a composite loss function in combination with boundary sensitive loss, and carrying out iterative updating on the network until convergence, so as to obtain a multi-scale context-aware coding-decoding network; through the setting, a synergistic effect is formed in four levels of data construction, a network structure, a training strategy and result output, so that objective indexes of traditional village street view scale semantic segmentation are improved, and the labor burden in an engineering use scene is remarkably reduced.