159 results about "Geographical maps" patented technology

The invention relates to the field of city updating, and discloses a city updating decision-making method based on public participation, which comprises the following steps: acquiring city space basic data including a vector map, remote sensing image data and historical, cultural and geographic information data of a city; on the basis of the acquired spatial data, a public participation base map is constructed, and the base map comprises information of a road network, land use types and historical and cultural areas of the city; collecting opinion data of the public on facility site selection through an interactive platform on the public participation base map; and according to the collected public opinion data, constructing a multi-subject scoring model. According to the method, a decision-making method based on public participation is adopted, space basic data and public opinions of cities are combined, facility site selection is evaluated through a multi-subject scoring model, and the opinions of different subjects can be integrated by the system by setting weight values of the public, experts and the government, so that the diversity and fairness of site selection schemes are ensured.

Various embodiments discussed herein relate to using one or more language models and / or mapping platforms to generate a response to a natural language question or command regarding geographical information associated with a mapping platform. In response to receiving such natural language question or command, some embodiments first extract contextual data. Based at least in part on the extracting of the contextual data, various embodiments then provide the contextual data and the natural language command or question as input into one or more language models such that the one or more language models and / or mapping platforms generate a response. Some embodiments then cause presentation of an indication associated with the response.

The invention provides an online map construction method and device fusing time sequence prior and historical global map prior, and relates to the technical field of online map construction.The method comprises the steps that time sequence BEV features and online generated BEV features are efficiently fused through multi-channel cross attention based on masks; adaptively fusing the time sequence enhanced BEV features and local BEV features provided by a historical map through a spatial prior gating network; a mixed prior ROI mask is generated through prior information provided by a time sequence and a historical global map, and initialization of map query in map decoding at the current moment is assisted, so that the reliability of online map construction is improved.

The invention provides a cultural field-oriented knowledge graph space-time map display method, which comprises the following steps of: extracting culture entities and relationships thereof from multi-source heterogeneous data through a knowledge graph construction module, and constructing a structured knowledge graph; analyzing and normalizing the time and space information of the culture entity through a space-time processing module; through a map display module, displaying the processed culture entity and the time-space relationship thereof on a user terminal in a map form; through the interaction module, interaction operation between the user and the knowledge graph displayed in the map is achieved, and the user can more conveniently explore culture information; the method supports the display of multi-level cultural relationships, can effectively extract and display target information in a complex cultural network, has good applicability, can be widely applied to the fields of cultural heritage, historical research, archaeological research and the like, and enables cultural entities and relationships thereof to be more visually and dynamically presented in front of users.

The invention relates to an intelligent map making system and method based on a large language model, and the method comprises the steps: enabling a client to input data analysis information and a map making demand to a task planning Agent of a server, and carrying out the reasoning and disassembly of the input information into preliminary planning information through a double-LLM mechanism, the method comprises the following steps that Agent is used as a tool library, a tool library document, a knowledge library and a memory library are further inquired, a tool is selected through RAG and sent to a client side, if an existing tool cannot meet requirements, the Agent will generate a special code to complete a task, the client side calls the tool to carry out specific drawing according to received information, and a user can re-plan and adjust the drawing process through dialogue. The memory bank records session information and charting interaction operation information of this time and history. The problem of system fragmentation is solved through a unified architecture, stability and flexibility are balanced by combining large language model driving, tool agent and code generation, domain knowledge is introduced to enhance decision and improve specialty, and a user preference memory mechanism is constructed to realize personalized services.

Systems and methods are provided to select a set of candidate listings having geographic locations within a geographical area, fit a bound for a maps viewport to the set of candidate listings, generate a maximum score for the set of candidate listings based on a booking probability for each listing, a distance of the listing location from a center of the bound for the maps viewport, and visibility based on other listings. The systems and methods further optimize the listings shown in the maps viewport by removing and replacing farthest listings up to a predefined number of listings to be removed and computing a cumulative booking score for each set of listings to compare to the maximum score. The systems and methods cause display in a maps viewport in a user interface of a computing device of a set of listings that has a maximum score.

The invention relates to a historical image-text-based three-dimensional ancient city model construction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining historical image-text data of a target city, the historical image-text data comprises related historical literatures of the target city in a specified period, an electronic ancient map corresponding to an ancient map drawn in the specified period, an electronic old map corresponding to an old map surveyed and mapped in the latest period from the specified period, and a shot satellite image; based on the historical image-text data, generating a city plane restored map of the target city in a specified period; extracting semantic information of the target city in a specified period from at least one kind of data in the historical image-text data, wherein the semantic information comprises city information of the target city in the specified period and attribute information of constituent elements; and generating a three-dimensional ancient city model of the target city in a specified period based on the semantic information city plane restoration map. Therefore, a high-precision and high-integrity three-dimensional ancient city model of the target city in the specified period can be constructed.

The invention relates to the technical field of historic site restoration, and discloses a historic site surrounding landscape atmosphere perception simulation and historical authenticity reconstruction method, which comprises the following steps: (S1) carrying out site region multi-dimensional data acquisition and integration; (S2) analyzing historical authenticity factors and extracting value features; (S3) constructing a landscape atmosphere perception index system and quantifying factors; (S4) performing multi-period historical scene simulation and atmosphere perception modeling; and (S5) carrying out landscape atmosphere perception simulation analysis and authenticity verification. According to the historic site surrounding landscape atmosphere perception simulation and historical authenticity reconstruction method, multi-dimensional data acquisition and integration and historical element analysis are performed, the relevance between a site and surrounding natural, artificial and humanistic environments is systematically reconstructed, and the defect that only a single building is focused in the prior art is overcome; for example, oblique photography and ancient book map fusion are utilized to restore the spatial topological relation among the ruins, historical water systems and ceremony paths, and a complete environment system supporting historical functions is formed.

The invention discloses a travel map searching system and method based on deep learning and a Beidou database, and the method comprises the following steps: obtaining the position and track data of a user, loading a geographic object, and constructing a heterogeneous map; receiving a natural language request, extracting a semantic vector and writing the semantic vector into the graph; constructing a graph comparison learning network, generating a view and executing embedded learning; optimizing structural parameters by using a pigeon inspired algorithm, and outputting an optimal model; constructing a current position node, extracting a local sub-graph, and generating a sorting list; executing path reasoning, generating a recommendation result, and feeding back and updating. According to the method, deep semantic understanding and geographical trajectory modeling are fused, so that high-precision, interpretable and self-learning travel map intelligent search recommendation under the driving of the current position of the user is realized.

The invention discloses a map construction method and system based on VLM and SLAM positioning, and relates to the technical field of map construction, and the method comprises the steps: carrying out the preprocessing of current environment data collected by a multi-modal sensor, and obtaining multi-modal target data; performing environment positioning and map construction on the multi-modal target data through an SLAM technology to obtain an initial three-dimensional environment map; when a blank area exists in the initial three-dimensional environment map, reasoning environment semantic information in the current environment data through a visual language model to obtain a semantic completion map; and fusing the semantic complemented map with the initial three-dimensional environment map to obtain a dense map. Due to the fact that the visual language model is combined with the SLAM technology, the blank area in the initial three-dimensional environment map generated by the SLAM technology can be automatically complemented through understanding and reasoning of the visual language model on the environment semantic information, and therefore the precision and robustness of map building are improved.

The invention belongs to the technical field of building identification, and particularly discloses a historical building intelligent identification method and system based on multi-source spatio-temporal data, and the method comprises the steps: building a unified spatio-temporal grid reference through collecting and fusing a historical map, a multi-temporal remote sensing image and laser radar point cloud data; three-dimensional shape and structure features and space-time semantic attribute features of the building are extracted from the fusion data set, and cross-modal feature deep fusion is performed by using a double-flow space-time neural network; and finally, completing intelligent identification and multiple verification of the historical building based on the fusion features, and outputting a structured result containing a distribution map and an attribute database. According to the method, the problems of single data dimension, dependence on artificial experience and insufficient result practicability in a traditional method are effectively solved, the full-process automation and intelligence of historical building recognition from data perception, feature analysis to result output are realized, and the recognition accuracy and efficiency are remarkably improved.

The invention discloses a semantic vision SLAM (Simultaneous Localization and Mapping) method and a semantic vision SLAM system for an indoor low-texture and dynamic environment, which are characterized in that a visual odometer, a dynamic point elimination algorithm, a loopback detection algorithm and the like are integrated into a visual SLAM system, so that data in an indoor low-texture and dynamic scene are processed in real time, and high-precision positioning and high-quality map construction are provided. A semantic information acquisition thread and a dynamic point elimination module are added to the whole system, and a loopback detection module is improved. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-texture environment and a dynamic environment in the prior art are solved, and the feature point extraction and matching precision in the low-texture environment is improved; the interference of a dynamic object on the SLAM system is eliminated, and the positioning precision and the loopback detection performance are improved; the map construction quality is improved, and the semantic map with high readability is constructed by fusing semantic information.

According to the semantic map construction method based on deep learning and laser SLAM, a semantic segmentation method based on deep learning and a laser SLAM method are fused, a three-dimensional map with semantic tags is constructed in real time, and richer information support is provided for applications such as robot navigation and automatic driving. The invention provides a set of complete solution for challenges such as complexity and dynamics of an outdoor environment, large-scale and high-precision requirements, illumination and weather changes, semantic information requirements and the like.

The invention belongs to the field of tourism data processing, and provides a GIS-based culture and tourism spatio-temporal feature fusion system, which comprises a data acquisition module used for acquiring culture and tourism spatio-temporal data; the data fusion module is used for establishing semantic association between the cultural tourism data based on the knowledge graph; the GIS topological structure construction module is used for constructing a topological graph of the culture and tourism data and constructing a topological relation between the culture and tourism data based on the similarity of the topological graph and the semantic association relation; the culture and tourism time-space feature fusion module is used for constructing a culture and tourism fusion map; matching the tourist trajectory data with the cultural tourism resources; determining an influence range of the cultural activity; the prediction module is used for predicting a tourist diffusion thermodynamic diagram of a preset destination in a preset time; and the visual display module is used for displaying the cultural tourism fusion map and dynamically displaying the tourist diffusion thermodynamic diagram on the cultural tourism fusion map.

The invention belongs to the technical field of laser SLAM (Simultaneous Localization and Mapping), and particularly relates to a lightweight spherical map representation method, device and equipment for point cloud data. The method comprises the following steps: dividing point cloud data into at least one local point cloud according to a preset standard; projecting the local point cloud to a unit spherical surface, sampling in a preset mode based on scene attributes of the local point cloud to generate a local spherical map, establishing a globally unique index for sampling points based on a K-dimensional tree, segmenting the local point cloud into a plurality of point cloud blocks based on the sampling points, storing the structural features of the fitting plane of the point cloud block in the local spherical map; and forming lightweight spherical map representation of the point cloud data by all the local spherical maps and the corresponding poses. The invention provides a map representation method for lightweight point cloud data.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention relates to the technical field of robot positioning and map construction, and particularly discloses a self-adaptive LiDAR-IMU SLAM method fusing intensity features and Riemannian manifold ground constraints, and the LiDAR-IMU SLAM method comprises the following steps: a, original measurement; b, data preprocessing; c, feature extraction; d, carrying out ground manifold constraint; e, optimizing the factor graph; and f, outputting data. The method can solve the problems that in the prior art, matching fails in a low-texture environment due to dependence on geometric features, positioning drifting is caused by point cloud distortion and inertial accumulative errors, and Z-axis errors are continuously expanded due to lack of dynamic adaptation to complex terrains. Experimental results show that according to the self-adaptive LiDAR-IMU SLAM method, the Z-axis drift error is remarkably reduced by 67.78%, the minimum absolute pose error is 0.254 m, the environment sensing and mapping capacity of the mobile robot in the complex underground space is remarkably improved, and reliable technical support is provided for intelligent inspection and infrastructure monitoring of the underground space.

The invention belongs to the technical field of maps, discloses a method and a system for improving forest environment SLAM precision by using TLS high-precision map constraint, and provides a method and a system for improving forest environment SLAM precision by using a millimeter-level point cloud map scanned by ground-based laser scanning (TLS) under a static condition as a space reference. And calculating the relative pose transformation between the PLShh scanning point cloud and the TLS point cloud as an external absolute constraint, introducing the external absolute constraint into the SLAM system by constructing a factor graph, and performing global optimization to improve the trajectory precision and map consistency. In addition, the scheme can be seamlessly fused with the existing SLAM framework, and has important significance for promoting the application of the PLShh in forestry investigation and ecological monitoring.

The invention relates to the technical field of SLAM (Simultaneous Localization and Mapping), and provides a dynamic SLAM method and system based on static semantic anchor points, comprising the steps of static structure semantic anchor point generation, anchor point-based robust tracking and multi-sensor tight coupling fusion, back-end optimization and static semantic map construction. By actively selecting high-stability static structure features and combining lightweight semantic segmentation and space-time consistency verification, the problems of poor dynamic elimination effect and high calculation overhead in the prior art are solved. According to the method, the positioning robustness and precision in a dynamic scene are improved, the calculation overhead is reduced, the static map rich in semantics is generated, and the universality is high.

The invention discloses a method for generating an intangible cultural heritage map under the assistance of artificial intelligence, and the method comprises the steps: firstly collecting non-immortal item information from a multi-source heterogeneous data source, associating a space-time tag, and extracting non-immortal gene features; associating the non-perpetual development data with the environmental data by using a knowledge extraction technology; three types of dynamic relation layers are constructed in real time by adopting natural language processing, conflict information is merged through a knowledge fusion technology, and data accuracy is guaranteed; according to the three types of entity relation layers, combining time axis slicing and GIS spatial analysis, and utilizing a sequence modeling algorithm to reconstruct a non-genetic gene cross-spatiotemporal propagation path; and finally, mapping the propagation path to a non-abandoned map, and dynamically displaying related coordinates in a highlight manner according to non-abandoned keywords input by tourists. According to the method, intelligent reconstruction and visualization of the non-genetic genetic propagation path are realized, personalized services are provided for tourists, and the method has great significance in promoting inheritance and propagation of non-genetic culture.

The invention provides a semantic map construction method, device and system for robot navigation. The method provided by the embodiment of the invention can comprise the steps of geometry-topology SLAM increment updating, sparse imaging evidence generation, analysis availability field calculation, formalized semantic constraint compiling, increment satisfiability solving and minimum conflict processing, logic entropy driven active exploration, updating change arbitration and audit account book maintenance. Wherein the increment satisfiability solving and the minimum conflict processing comprise the following steps: executing increment satisfiability solving on a formalized semantic constraint set, if the formalized semantic constraint set cannot be satisfied, positioning a minimum unsatisfiability subset, approximately determining a minimum constraint set according to a rollback set, and executing minimum rollback processing to obtain a constraint solving output result, the constraint solution output result comprises a consistency solution space generated in the current period and a logic entropy value. According to the method, accumulated errors or logic contradictions of semantic information in long-term operation can be effectively avoided, and stable evolution of the semantic map is ensured.

A portable electronic system receives a set of one or more canonical maps and determines the sparse map based at least in part upon one or more anchors pertaining to the physical environment. The sparse map is localized to at least one canonical map in the set of one or more canonical maps, and a new canonical map is created at least by merging sparse map data of the sparse map into the at least one canonical map. The set of one or more canonical maps may be determined from a universe of canonical maps comprising a plurality of canonical maps by applying a hierarchical filtering scheme to the universe. The sparse map may be localized to the at least one canonical map at least by splitting the sparse map into a plurality of connected components and by one or more merger operations.

The present invention discloses a dynamic monitoring method for the spatial distribution of urban biodiversity, which relates to the technical field of biodiversity distribution surveys. The method includes using multi-source data to extract the boundary of the urban built-up area; and generating a spatial distribution map of urban biodiversity by using public big data and extrapolation method. By adopting the open big data of biological distribution records on the network platform and combining with the extrapolation method to generate a biodiversity map of urban areas with fine resolution, the present invention develops a general analysis framework, which can reduce the labor and time costs in the acquisition stage of basic data on urban biodiversity. On the other hand, by comparing remote sensing image data, it is possible to obtain data on animals and plants under the canopy cover, which is convenient for evaluating the species diversity under the canopy, and can capture complex information on species composition, which is beneficial to improving the adaptability to the complex artificial environment and highly fragmented habitat patches in urban areas.

To provide a device and method that can provide an environment in which participants in a "space" for thinking and discussing can consider things from a wide variety of perspectives. [Solution] According to this embodiment, the map classifies fields related to society in the first direction, classifies the level of each field in the second direction (social domain in the Arctic direction), and classifies words related to individuals in the third direction (personal domain in the Antarctic direction). Multiple nodes with semantic content are placed in the social domain and the personal domain according to the words (attribute information) at their placement location.

The invention provides a laser SLAM (Simultaneous Localization and Mapping) method based on an improved Cartograph algorithm, and aims to solve the problems of loopback detection and map construction in a dynamic environment. SURF feature extraction and generalized ICP fine registration are introduced, so that the precision and robustness of point cloud matching are improved; due to the design of the delay judgment module, false loopback misinformation is effectively reduced, and the consistency of a global map is ensured; and meanwhile, a multi-resolution search strategy is adopted, so that the loopback detection efficiency and precision are improved. Experimental results show that compared with Hector-SLAM (Simultaneous Localization and Mapping) and Cartographer, the algorithm disclosed by the invention shows higher map construction precision and consistency in a long straight corridor and a loopback environment, and successfully solves the problems of accumulative errors and wrong loopback. The algorithm is suitable for complex SLAM tasks, has wide engineering application value, and is especially suitable for the fields of robot navigation, autonomous driving and the like.

The application belongs to the technical field of map knowledge, and particularly relates to a map knowledge representation method based on a knowledge graph. The method first acquires map data, extracts map knowledge therefrom, including concept class knowledge and rule class knowledge, the concept class knowledge being common sense knowledge in the field of cartography and including map element knowledge; the rule class knowledge being various models, rules and principles formed in the process of map making; then generates a map knowledge network graph based on the knowledge graph according to the map element knowledge; and finally performs knowledge reasoning according to the rule class knowledge, completes the map knowledge network graph, and generates new entities and / or new entity relationships. The application fully mines geographical knowledge contained in a map, provides a basis for all-round knowledge mining and fusion of the map, and has certain reference value for realizing the transformation of 'data-information-knowledge'.

This invention discloses a local SLAM construction method, a global SLAM construction method, and a construction device. In this embodiment, a cloud map and a real-time map are compared, and an object classification model is used to obtain the object classification of the current scene. Based on the object classification, prediction updates are performed, and a sub-map is obtained based on the prediction updates. The updated sub-maps obtained by the local SLAM construction method are then fused to obtain the global map. By classifying objects into dynamic and static objects, and predicting the motion of dynamic objects, a sub-map is obtained based on the prediction results, enabling real-time identification of dynamic objects.

The invention discloses a map loading method and a related device, and relates to the field of map data processing. According to the application, global tile set element information is introduced, and the information comprises complete tile set element information, such as basic information of tile data, tile subdivision information, encryption related information and the like, so that the loading and rendering requirements of any coordinate system, any subdivision rule and any scale tile set can be met. According to the method, the tile data set is split into the plurality of tile archive files to be stored, and the number of tiles in each tile archive file is small, so that the offset of the tile data in the tile archive files is small, and the reading speed of the tile data is increased. The map loading speed is increased, and the user experience is improved.

The invention relates to a literature work geographic mark presentation system based on literature, history and geography fusion. According to the system, original literature works, historical geographic data, historical time period length and historical population density data are integrated; extracting geographic entities by using a natural language processing model and labeling space-time tags to generate a structured data set; and constructing a space-time dimension literature density thermodynamic value matrix to quantitatively analyze geographical distribution characteristics, dynamically screening negative space candidate areas with literature density lower than a threshold value and active historical data in combination with a historical event database, generating a causal hypothesis, and superposing a visual thermodynamic diagram and a time axis through a map interface to generate a dynamic visual map. Therefore, the distribution rule of the literature geography negative space and the deep association between the literature geography negative space and the historical background are efficiently detected and revealed, the automation degree and the empirical property of literature geography analysis are remarkably improved, and intelligent decision support is provided for culture research, education and heritage protection.