231 results about "Geographical maps" patented technology

Approaches presented herein provide for the ability to process, store, index, and search geospatial information such as maps with flexible granularity. A set of observations, such as may include sensor data captured for a region of an environment, can be fed as input to a language model. The language model can generate a tokenized description of the region, as may include a text string of tokens encapsulating semantics, topology, geometry, and / or other aspects of the region. A feature vector or embeddings for the region can be generated based on the tokenized description, and a similarity search performed against a vector database, for example, to identify similar feature vectors corresponding to similar regions or domains. Labels or other information associated with these similar feature vectors can be automatically applied to the example region. Clustering of feature vectors or other embeddings can also be performed based in part on the similarity.

Approaches presented herein provide for the performance of quality assurance-related tasks with respect to a representation of an environment, such as a set of generated map data. In particular, a language model can be used to generate a tokenized representation of a map using information such as the semantics, topology, and / or geometry determinable from the map data. A language model-generated representation can comply with real-world rules and constructs, and can account for omissions or errors in the input data based upon known relationships and semantics for various objects in the environment. One or more language models can be used to not only identify potential issues in the map data, but also to make recommendations for modifications and / or to generate plaintext descriptions of the issues, modifications, or recommendations to assist a human reviewer in addressing the issues.

The present disclosure relates to systems and / or methods for predicting the location of subsurface reservoirs of natural hydrogen. One or more embodiments described herein include a system that can comprise a memory to store computer executable instructions. The system can also comprise one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement a hydrogen exploration controller configured to generate a plurality of maps characterizing a geographical region. The respective maps from the plurality of maps can delineate a location of respective geological features within the geographical region that are associated with a presence of a subsurface reservoir of natural hydrogen. Additionally the hydrogen exploration controller can be further configured to identify an area in the geographical region that comprises a convergence of two or more of the respective geological features based on the plurality of maps.

The invention relates to the technical field of synchronous positioning and map construction, in particular to a geometric-semantic collaborative fusion mobile robot three-dimensional semantic map construction method, which is used for performing deep fusion on a geometric map and multi-view semantic information so as to solve the problem of difference of the two types of information in resolution, scale and dimension. The geometric map mainly provides structured information of the environment, and the semantic information describes object categories and features thereof in the environment. Direct fusion of the two types of information often causes spatial positioning errors and semantic information loss or redundancy problems. According to the method, a geometric map containing environmental structured information is constructed through an SLAM technology, and meanwhile, object classification, attribute labeling and semantic relationship analysis are performed on multi-view image data by utilizing the semantic information extraction capability of a large model. And finally, precise fusion of the geometric map and multi-view semantic information is realized, and meanwhile, the object recognition and semantic understanding capabilities of the robot in a complex scene are improved.

The invention discloses enhancement of perceptual data using a large language model in an environment reconstruction system and application, and specifically discloses a method of generating a tokenized description of a physical environment using a language model. In at least one embodiment, sensor and / or observation data of an environment may be obtained and used to generate a perceptual data set. The perceptual data and approximate location data within the environment may be analyzed to identify an aligned map data set. The aligned map data and perception data may be provided as input to a trained language model, which may be trained as associated and / or fused information to generate a single consistent representation of the environment. The language model may output a tokenized description of the environment, which may be a compact but robust textual description of the environment in a language of the particular field.

Approaches presented herein provide for the automated, end-to-end generation of map data based at least in part on sensor data captured for an environment. At least one language model can be used to generate a text-based, tokenized description of the environment that includes semantics, topology, geometry, and / or other information for the environment. A generation pipeline can use one or more language models in one or more stages, and the data passed between stages can be in a determined tokenized representation format, as may correspond to a tokenized text string in a specific structured language.

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.

The invention discloses a map information updating method and system, and relates to the technical field of map updating, and the method comprises the steps: according to each road sign object, using an editing distance and a cosine similarity to measure a first similarity between symbol elements in an element set of the road sign object and symbol elements in a map element set; measuring a second similarity between the color elements in the element set of the road sign object and the color elements in the map element set by using the Euclidean distance; measuring a third similarity between a central point position element in the element set of the road sign object and a central point position element in a map element set by using a Hausdorff distance; taking the intersection-union ratio of the contour frame elements in the element set of the road sign object and the contour frame elements in the map element set as fourth similarity; determining whether the four similarities are matched or not through the four similarities; if yes, updating the map. The map can be updated in time, and the real-time performance is good.

The invention provides a visual language navigation method based on a topological graph, and belongs to the technical field of navigation. On the basis of the road point predictor, the topological map of the current environment is constructed through the road point predictor, and the intelligent agent plans the path through the nodes in the topological map, so that the capacity of capturing the global environment layout is improved, meanwhile, the previous position can be well backtracked, and the long-term planning capacity of the intelligent agent is improved.

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 territorial space planning scheme automatic generation system based on artificial intelligence, relates to the technical field of territorial space planning, and aims to solve the technical problems that a current system is difficult to construct a reasonable regional framework, land evaluation is not comprehensive enough, and reliable data support is difficult to provide for territorial space planning. Comprising a scheme generation model construction module. According to the invention, the target region is segmented into a plurality of regional regions through the segmentation and division unit, and a clear regional architecture is constructed; the feature extraction unit deeply excavates the land features of each region and comprehensively evaluates the land; element values of a land utilization intensity matrix are calculated through the matrix, so that the precise land utilization intensity matrix is generated, and data support is provided for territorial space planning. According to the method, a reasonable regional framework is established through map segmentation, the land value is comprehensively evaluated through feature extraction, meanwhile, the land utilization intensity matrix is determined, and reliable data support is provided for planning through the matrix.

An SLAM map global alignment system based on an OSM map comprises a priori OSM map processing module, an SLAM map data processing module, a global map coarse alignment module and a map optimization-based fine alignment module, a topological map is constructed as a transfer structure of map alignment by using a common topological structure in a priori map as a key point, and the overall alignment accuracy of the map alignment is improved. The effects of aligning the perception map with the road network map and endowing the SLAM map with global coordinates are achieved, global coordinate calibration, namely geographic longitude and latitude coordinates, of the SLAM map can be completed with relatively high precision, semantic information association is achieved after the SLAM map is aligned with the OSM, and the method has relatively good application extensibility for future map application.

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 belongs to the technical field of geographic data registration, and discloses an urban map batch geographic registration method and system based on map feature classification. The method comprises the following steps: accurately classifying collected target maps based on map features, wherein the classification comprises dividing drawings with high repetition rate of drawing contents into the same class; unified batch processing of similar modern city maps is completed for the classified same-group maps by means of picture editor drawing software, and external reference grid cross points are loaded; and completing batch geographical registration of the same group of maps by utilizing the established external reference grid intersection points. The method is different from a thought starting from internal elements of a map, the thought of adding an external reference control point to the map is changed to carry out geographic registration, a batch high-efficiency geographic registration mode can be considered at the same time, the learning threshold is low, and the method has wide popularization and promotion value in the field of modern city maps.

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 relates to a vector geographic spatial data relation description method, and belongs to the technical field of spatial data management. According to the method, a relation description data structure comprising relation coding values, relation coordinate values, at least two members and role attributes of each member is constructed, relation data is described by utilizing the structure, and the relation coordinate values are used for describing coordinates displayed in a map or positions where text letters appear. The role attributes are used for describing spatial semantics or other business attribute relations of members in the relations, and the members are point elements, line elements, surface elements or relation data in the vector geographic space data. It can be seen that by means of the constructed relation description data structure, the relation between the geographic entities can be described, the vector geographic space data are made to carry semantic information, the relevance between the vector geographic space data can be described more clearly, and relevance analysis of the geographic space data is facilitated.

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.

Various embodiments of the present disclosure provide machine learning using map representations of categorical data to provide classification predictions. In one example, an embodiment provides for generating a first map representation of a first categorical input feature set for categorical data based on a first coding standard. A second map representation of a second categorical input feature set for the categorical data may also be generated based on a second coding standard. Additionally, at least one machine learning model may be applied to the first map representation and the second map representation to generate the prediction output. Based on the prediction output one or more prediction-based actions may also be performed.