Remote sensing feature knowledge graph construction method based on cooperation of geographical object geometric features and graph enhanced retrieval

Abstract

The application discloses a remote sensing ground object knowledge graph construction method based on cooperation of geographical object geometric features and graph enhancement retrieval, and comprises the following steps: data set preparation; basic triple extraction and feature analysis; LLM-assisted semantic and relationship enhancement; flexible quantization and output of ground object knowledge graph knowledge; knowledge conflict correction and final version graph construction. The application utilizes a tensor decomposition model to train a ground object knowledge graph to obtain a knowledge graph embedding vector, and through an unsupervised / semi-supervised mode, utilizes a low-dimensional vector representation of a ground object category learned from a knowledge graph triple to embed the semantic relationship among categories in the knowledge graph embedding vector, so as to provide global semantic prior knowledge for a downstream model, and effectively solve the semantic confusion problem in a remote sensing image.

Description

A Method for Constructing Remote Sensing Ground Feature Knowledge Graphs Based on the Collaboration of Geographic Object Geometric Features and Graph Augmentation Retrieval Technical Field

[0001] This invention relates to the field of knowledge graph technology, and in particular to a method for constructing remote sensing ground feature knowledge graphs based on the synergy of geographic object geometric features and graph-enhanced retrieval. Background Technology

[0002] Knowledge graphs (KGs) have become a core technology for managing and utilizing large-scale, complex data. However, in the fields of remote sensing and geographic information, which have significant geospatial distribution characteristics, existing technologies face the following prominent problems:

[0003] 1. High reliance on manual annotation and high cost: Traditional knowledge graph construction methods, especially those based on supervised learning, require large-scale, high-quality labeled datasets. In the field of geographic information, acquiring such data is not only highly challenging and extremely costly, but also requires the deep involvement of scarce domain experts, making annotation a bottleneck in knowledge graph construction.

[0004] 2. Weak generalization ability in low-resource scenarios: In scenarios with sparse labeled data and few or zero samples, existing models have difficulty effectively identifying and extracting new entities or relationships that have not been seen before, resulting in insufficient robustness.

[0005] 3. Rigid Spatial Relationships: Traditional topological relationships (such as "adjacent" and "containment") are binary rigid logic (yes / no), which is difficult to express the fuzzy and gradual geographical relationships in the real world. This rigidity makes it difficult for downstream remote sensing analysis tasks to use accurate spatial prior knowledge for correction, easily leads to discontinuous classification boundaries, misjudgments of complex scenes, and seriously limits the topological consistency and spatial quality of segmentation results. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval, which is simple in algorithm, low in cost, and robust.

[0007] The technical solution of this invention to solve the above-mentioned technical problems is: a method for constructing a remote sensing ground feature knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, comprising the following steps:

[0008] Step S1, Dataset Preparation: Prepare remote sensing image data, metadata information for the project dataset, and ground truth label dataset for remote sensing images. External corpora and primary knowledge sources;

[0009] Step S2, Basic Triple Extraction and Feature Parsing: Using the metadata of the project dataset and the coarse segmentation results of remote sensing images, identify geographic objects (Geo-Objects) and their geometric and spectral features, and construct a basic geo-KG (Geographic Knowledge Graph).

[0010] Step S3, LLM-assisted semantic and relational enhancement: Introducing a large-scale language model (LLM), based on an external corpus, and utilizing... The mechanism retrieves structured evidence chains through topic community recall and multi-hop link retrieval, and performs knowledge expansion, relationship completion, and semantic verification on the basic geo-KG knowledge graph.

[0011] Step S4, Flexible Quantization and Output of Geographic Knowledge Graph Knowledge: Using Tensor Decomposition Model Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. Based on statistics from a large-scale remote sensing ground truth dataset, a probabilistic topological intensity matrix was obtained through quantification. ;

[0012] Step S5, Knowledge Conflict Correction and Final Graph Construction: Combining primary knowledge sources, through... Evidence chains are retrieved for conflicting knowledge, and LLM combines knowledge aggregation and conflict resolution strategies to adjudicate conflicts, ensuring that the final output is a complete knowledge graph of remote sensing features in the vertical domain.

[0013] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, specifically step S2, involves the following process:

[0014] Step S21, coarse segmentation: Apply the SLIC algorithm to the remote sensing image to generate Geo-Object candidate regions with preliminary spatial boundaries;

[0015] Step S22, Metadata Fusion: Associate the Geo-Object candidate region with the metadata information of the project dataset to ensure that each superpixel block obtains a preliminary semantic label and spatial context, and complete the preliminary semantic recognition of the Geo-Object.

[0016] Step S23, Geometric feature calculation: Calculate the geometric features of each obtained Geo-Object to quantify the shape and size attributes of the land cover;

[0017] Step S24, Spectral feature calculation: Statistical analysis is performed on all pixels within the Geo-Object candidate region to calculate the spectral features of the Geo-Object.

[0018] Step S25: Construct the basic triplet.

[0019] The aforementioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, specifically step S25, involves: using the preliminary classification labels of geographic objects (Geo-Objects) as entities to determine the node types of the knowledge graph; and then using a dimensional extension nine-intersection model... Spatial operations are performed to extract initial topological relationships; initial functional relationships are extracted based on weakly supervised semantic initial rules; all calculated geometric and spectral features are used as attributes of Geo-Object entities, and finally, the initial set of basic triples for the geographic feature knowledge graph is output. , ,in Representative attribute triples, This represents the union operation. Indicates the head entity. Indicates the tail entity. Indicates a relationship.

[0020] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, specifically step S3, involves the following process:

[0021] Step S31, Uncertainty Analysis and Identification: For The confidence level of the triples is evaluated, and triples with confidence levels below a preset threshold or lacking semantic evidence are selected for enhancement.

[0022] Step S32: Set up the GraphRAG deep retrieval mechanism: Based on the head entity, tail entity, and relation type of the triple to be enhanced, construct a refined retrieval query; utilize a semantic graph pre-built from an external corpus; identify semantic clusters using the Leiden algorithm; recall the entire semantic cluster related to the target topic; perform multi-hop path analysis to mine implicit causal and semantic chains across paragraphs and documents; rank the recalled text blocks by relevance; select the most informative text context set to form a chain of evidence. ;

[0023] Step S33, Structured Knowledge Extraction and Completion Based on LLM: LLM for Evidence Chain To conduct analysis, The semantic rationality of the relationships in the dataset is verified. The LLM process performs entity extraction, relation extraction, attribute extraction, and event extraction tasks, producing formalized knowledge triples and event quadruples. The LLM identifies synonymous entities, assigns them unified labels, and generates new functional relation triples that conform to the common sense of the vertical domain. Finally, the enhanced Geo-KG set is output. .

[0024] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, in step S4, utilizes a tensor decomposition model. Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. The process is as follows:

[0025] First, the enhanced Geo-KG collection All semantic triples in the training set , ,in It is a head entity. , It is a collection of entities. It's a relationship. , It is a set of semantic relations. It is a tail entity. ;

[0026] Then, model selection and initialization were performed, using a tensor decomposition model. As a knowledge graph embedding algorithm, initialize all entities and relationships Complex vector embeddings;

[0027] Then minimize the scoring function During training, the scoring function is defined as follows:

[0028]

[0029] in, yes The conjugate of the set is used, and the training objective is to maximize the score of correct triples and minimize the score of incorrect triples. The real part extraction operator is , and <> is the trilinear dot product over the complex field;

[0030] After training, the knowledge graph embedding vectors of all categories of Geo-Object are obtained. , , For discrete entity indexing, For the first The latent feature vectors of each entity represent global semantic association information. For entity collection The base number.

[0031] In the aforementioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, step S4 involves quantizing the probabilistic topological intensity matrix based on statistics from a large-scale remote sensing ground truth dataset. The process is as follows:

[0032] First, use the ground truth label dataset of remote sensing images. The set of land cover categories is , , For the first Each category of land cover It represents the total number of land cover categories;

[0033] Then, spatial co-occurrence frequency statistics are performed, which includes pixel pair statistics, total pixel pair statistics, and calculation of the initial co-occurrence frequency. Pixel pair statistics are... Each pair of land cover categories , For the first Each category of land cover For the first Each land cover category, statistics and The number of spatially adjacent or functionally symbiotic pixel pairs Total pixel pair statistics are statistics Number of all pixel pairs appearing ; Calculate the first co-occurrence frequency in the initial co-occurrence frequency matrix Line number Column elements :

[0034]

[0035] Next, the probabilistic topological strength value is calculated, and LLM and Geo-Object geometric factors are introduced for correction, resulting in the final strength value. The mixture is weighted and fused from three factors and then processed by the Sigmoid function. Normalization yields:

[0036]

[0037] in, It is a weighted fusion score, defined as:

[0038]

[0039] in, For geometric feature factors based on Geo-Object, Based on Statistical factors of the knowledge graph structure. This serves as a verification factor for the semantic rationality of topological relationships based on LLM. All are weighted coefficients;

[0040] Finally, output the probabilistic matrix, obtaining a dimension of Probabilized topological strength matrix , As a soft constraint for topological consistency in downstream models.

[0041] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, in step S5, involves... The process of evidence chain retrieval for conflicting knowledge is as follows:

[0042] First, , And an inviolable first-level knowledge source as input, the first-level knowledge source being a hard rule base;

[0043] Then, semantic embedding verification, topology strength verification, and domain logic verification are performed respectively to identify conflicts;

[0044] Finally, all identified conflicts are marked as a set of triples to be corrected. ,use right For each conflicting triple, a chain of evidence is retrieved. The goal is to obtain a structured, traceable chain of evidence. Chain of evidence Points to the most authoritative text blocks or logical reasoning chains in an external corpus, used to support or refute conflicting knowledge.

[0045] The aforementioned method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, in step S5, aims to ensure that the semantic relationships between geographic objects conform to domain common sense. The semantic embedding verification process involves: evaluating... and Vector distance between ,like Less than the preset threshold However, the definition of a first-level knowledge source and If they must be independent, then they are marked as semantic conflicts;

[0046] The goal of topology strength verification is to ensure that spatial symbiotic flexibility conforms to hard rules. The process of topology strength verification is as follows: Check The intensity value, if defined by the first-level knowledge source and They should never coexist, but the verification process found... Greater than the set threshold If so, it is marked as a topological conflict;

[0047] The goal of domain logic verification is to ensure that the graph structure conforms to the domain axioms. The process of domain logic verification is as follows: Using triples in the equation for rule-based logical reasoning, if... There exists The definition of a first-level knowledge source It is impossible to be If it is included, it is marked as a logical conflict.

[0048] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, in step S5, the process of LLM combining knowledge aggregation and conflict resolution strategies to adjudicate conflicts is as follows:

[0049] First, LLM receives First-level knowledge source rules and chain of evidence ;

[0050] Then, the LLM executes the following knowledge aggregation and conflict resolution strategies:

[0051] Credibility assessment: Based on the authority of the evidence source Conduct credibility scoring;

[0052] Duplicate entity disambiguation: Re-verify whether there are synonymous unmerged entities in the conflicting triples;

[0053] Relation consistency check: Check whether the relation logic of conflicting triples conforms to domain axioms and authoritative evidence;

[0054] Finally, LLM correction Incorrect semantic embedding values ​​or topological strength values ​​are used to generate the final map set. .

[0055] The above-mentioned method for constructing a remote sensing knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, in step S5, ultimately, will... Write the data into the Neo4j graph database and output a vertical domain remote sensing feature knowledge graph that is compatible with the corresponding field or project, including... , , ,Will As external knowledge, reconstruct semantic clusters to improve the next round. This improves the accuracy of recall and forms a closed-loop knowledge mining system.

[0056] The beneficial effects of this invention are as follows:

[0057] 1. This invention utilizes a tensor decomposition model. Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. Through unsupervised / semi-supervised methods, utilizing Learn low-dimensional vector representations of land cover categories from knowledge graph triples. It embeds semantic relationships between categories, providing global semantic prior knowledge for downstream models and effectively solving the semantic confusion problem in remote sensing images.

[0058] 2. This invention is based on the statistical analysis of a large-scale remote sensing ground truth dataset, and quantifies to obtain a probabilistic topological intensity matrix. Transforming traditional binary topological relationships into Probability intensity values ​​within the interval, probabilistic topological intensity matrix The intensity of flexible symbiosis between land cover categories in spatial coexistence or repulsion was quantified, serving as a soft constraint for topological consistency in downstream deep learning models. Attached Figure Description

[0059] Figure 1 is an overall flowchart of the present invention.

[0060] Figure 2 is a flowchart of the basic triplet extraction and feature parsing in this invention.

[0061] Figure 3 is a flowchart of the LLM-assisted semantic and relational enhancement in this invention.

[0062] Figure 4 is a diagram of the calculation model of the probabilistic topological strength matrix in this invention. Detailed Implementation

[0063] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0064] As shown in Figure 1, a method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval includes the following steps:

[0065] Step S1, Dataset Preparation: Prepare remote sensing image data, metadata information for the project dataset, and ground truth label dataset for remote sensing images. External corpora and primary knowledge sources.

[0066] Step S2, Basic Triple Extraction and Feature Parsing: Using the metadata of the project dataset and the coarse segmentation results of remote sensing images, identify geographic objects (Geo-Objects) and their geometric and spectral features, and construct a basic geo-KG knowledge graph.

[0067] As shown in Figure 2, the specific process of step S2 is as follows:

[0068] Step S21, coarse segmentation: Apply the SLIC algorithm to the remote sensing image to generate Geo-Object candidate regions with preliminary spatial boundaries; the SLIC algorithm effectively generates compact and uniform superpixel blocks by clustering in five-dimensional space, which serve as the basis for subsequent Geo-Objects.

[0069] Step S22, Metadata Fusion: Associate the Geo-Object candidate region with the metadata information of the project dataset (including but not limited to geographic coordinates and preliminary classification labels) to ensure that each superpixel block obtains a preliminary semantic label and spatial context, and completes the preliminary semantic recognition of the Geo-Object.

[0070] Step S23, Geometric feature calculation: Calculate the geometric features of each obtained Geo-Object. Geometric features include, but are not limited to, area, perimeter, shape index (length-to-width ratio, etc.), and compactness (perimeter squared / area ratio), which are used to quantify the shape and size attributes of the land feature.

[0071] Step S24, Spectral feature calculation: Statistical analysis is performed on all pixels within the Geo-Object candidate region to calculate the spectral features of the Geo-Object. The spectral features include the mean, standard deviation (measures the spectral uniformity within the region), and specific band combination indices (Normalized Difference Vegetation Index NDVI, texture, and others).

[0072] Step S25: Construct the basic triplet.

[0073] The initial classification labels of geo-objects (e.g., buildings, roads) are used as entities to determine the node types of the geo-feature knowledge graph; a nine-intersection model based on dimensional expansion is then used. Spatial operations are performed to extract initial topological relationships; initial functional relationships are extracted based on weakly supervised semantic initial rules; all calculated geometric and spectral features are used as attributes of Geo-Object entities, and finally, the initial set of basic triples for the geographic feature knowledge graph is output. , ,in Representative attribute triples, This represents the union operation. Indicates the head entity. Indicates the tail entity. Indicates a relationship.

[0074] Step S3, LLM-assisted semantic and relational enhancement: Introducing a large-scale language model (LLM), based on an external corpus, and utilizing... The mechanism retrieves structured evidence chains through topic community recall and multi-hop link retrieval, and performs knowledge expansion, relationship completion, and semantic verification on the basic geo-KG knowledge graph.

[0075] As shown in Figure 3, the specific process of step S3 is as follows:

[0076] Step S31, Uncertainty Analysis and Identification: For The confidence level of the triples is evaluated, and triples with confidence levels below a preset threshold or lacking semantic evidence are selected for enhancement.

[0077] Step S32: Set up the GraphRAG deep retrieval mechanism: Based on the head entity, tail entity, and relation type of the triple to be enhanced, construct a refined retrieval query; utilize a semantic graph pre-built from an external corpus; identify semantic clusters using the Leiden algorithm; recall the entire semantic cluster related to the target topic; perform multi-hop path analysis to mine implicit causal and semantic chains across paragraphs and documents; rank the recalled text blocks by relevance; select the most informative text context set to form a chain of evidence. This ensures that the external knowledge input into the LLM is of high quality and has broad coverage;

[0078] Step S33, Structured Knowledge Extraction and Completion Based on LLM: LLM for Evidence Chain To conduct analysis, The semantic rationality of the relationships in the dataset is verified. The LLM process performs entity extraction, relation extraction, attribute extraction, and event extraction tasks, producing formalized knowledge triples and event quadruples. The LLM identifies synonymous entities, assigns them unified labels, and generates new functional relation triples that conform to the common sense of the vertical domain. Finally, the enhanced Geo-KG set is output. .

[0079] Step S4, Flexible Quantization and Output of Geographic Knowledge Graph Knowledge: Using Tensor Decomposition Model Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. Based on statistics from a large-scale remote sensing ground truth dataset, a probabilistic topological intensity matrix was obtained through quantification. .

[0080] Using tensor decomposition model Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. The process is as follows:

[0081] First, the enhanced Geo-KG collection All semantic triples in the training set , ,in It is a head entity. , It is a collection of entities. It's a relationship. , It is a set of semantic relations. It is a tail entity. ;

[0082] Then, model selection and initialization were performed, using a tensor decomposition model. As a knowledge graph embedding algorithm, initialize all entities and relationships Complex vector embeddings;

[0083] Then minimize the scoring function During training, the scoring function is defined as follows:

[0084]

[0085] in, yes The conjugate of the set is used, and the training objective is to maximize the score of correct triples and minimize the score of incorrect triples. The real part extraction operator transforms complex vector interactions into a scalar score, used to quantify the authenticity or confidence of triples in the latent semantic space; 〈〉 represents the complex-domain trilinear dot product, which, through the introduction of conjugate transformation, enables the model to effectively capture asymmetric features in geospatial relationships (i.e., distinguish between...). and The directional difference is the core operator for achieving high-precision semantic embedding;

[0086] After training, the knowledge graph embedding vectors of all categories of Geo-Object are obtained. , , For discrete entity indexes, it involves traversal. In the algorithm implementation, the discrete index variables... As a unique identifier, it is used to locate a specific first in the map. A geographic object or semantic concept node; For the first Potential feature vectors of each entity; For entity collection The cardinality represents the total number of unique nodes contained in the constructed geographic feature knowledge graph. The total number of unique nodes defines the... The scale, that is, the total capacity of knowledge.

[0087] It incorporates global semantic prior knowledge between categories.

[0088] Based on statistics from a large-scale remote sensing ground truth dataset, a probabilistic topological intensity matrix is ​​obtained through quantization. The process is as follows:

[0089] First, a large-scale, high-reliability remote sensing image ground truth label dataset is used. The set of land cover categories is , , For the first Each category of land cover It represents the total number of land cover categories;

[0090] Then, spatial co-occurrence frequency statistics are performed, which includes pixel pair statistics, total pixel pair statistics, and calculation of the initial co-occurrence frequency. Pixel pair statistics are... Each pair of land cover categories , For the first Each category of land cover For the first Each land cover category, statistics and The number of spatially adjacent or functionally symbiotic pixel pairs Total pixel pair statistics are statistics Number of all pixel pairs appearing ; Calculate the first co-occurrence frequency in the initial co-occurrence frequency matrix Line number Column elements :

[0091]

[0092] Next, the probabilistic topological strength value is calculated, as shown in Figure 4. LLM and Geo-Object geometric factors are introduced for correction, resulting in the final strength value. The mixture is weighted and fused from three factors and then processed by the Sigmoid function. Normalization yields:

[0093]

[0094] in, It is a weighted fusion score, defined as:

[0095]

[0096] in, For geometric feature factors based on Geo-Object, Based on Statistical factors of the knowledge graph structure. This serves as a verification factor for the semantic rationality of topological relationships based on LLM. All are weighted coefficients;

[0097] Finally, output the probabilistic matrix, obtaining a dimension of Probabilized topological strength matrix , As a soft constraint for topological consistency in downstream models.

[0098] Step S5, Knowledge Conflict Correction and Final Graph Construction: Combining primary knowledge sources, through... Evidence chains are retrieved for conflicting knowledge, and LLM combines knowledge aggregation and conflict resolution strategies to adjudicate conflicts, ensuring that the final output is a complete knowledge graph of remote sensing features in the vertical domain.

[0099] pass The process of evidence chain retrieval for conflicting knowledge is as follows:

[0100] First, , And an inviolable first-level knowledge source as input, the first-level knowledge source being a hard rule base;

[0101] Then, semantic embedding verification, topology strength verification, and domain logic verification are performed respectively to identify conflicts;

[0102] The goal of semantic embedding validation is to ensure that the semantic relationships between geo-objects conform to domain common sense. The process of semantic embedding validation is as follows: evaluation and Vector distance between ,like Less than the preset threshold However, the definition of a first-level knowledge source and If they must be independent, then they are marked as semantic conflicts;

[0103] The goal of topology strength verification is to ensure that spatial symbiotic flexibility conforms to hard rules. The process of topology strength verification is as follows: Check The intensity value, if defined by the first-level knowledge source and Never coexist (e.g., (Should be 0), but the verification program found... Greater than the set threshold If so, it is marked as a topological conflict;

[0104] The goal of domain logic verification is to ensure that the graph structure conforms to the domain axioms. The process of domain logic verification is as follows: Using triples in the equation for rule-based logical reasoning, if... There exists The definition of a first-level knowledge source It is impossible to be If it is included, it is marked as a logical conflict.

[0105] Finally, all identified conflicts are marked as a set of triples to be corrected. ,use right For each conflicting triple, a chain of evidence is retrieved. The goal is to obtain a structured, traceable chain of evidence. Chain of evidence Points to the most authoritative text blocks or logical reasoning chains in an external corpus, used to support or refute conflicting knowledge.

[0106] The process by which LLM combines knowledge aggregation and conflict resolution strategies to adjudicate conflicts is as follows:

[0107] First, LLM receives First-level knowledge source rules and chain of evidence ;

[0108] Then, the LLM executes the following knowledge aggregation and conflict resolution strategies:

[0109] Credibility assessment: Based on the authority of the evidence source Conduct credibility scoring;

[0110] Duplicate entity disambiguation: Re-verify whether there are synonymous unmerged entities in the conflicting triples;

[0111] Relation consistency check: Check whether the relation logic of conflicting triples conforms to domain axioms and authoritative evidence;

[0112] Finally, LLM correction Incorrect semantic embedding values ​​or topological strength values ​​are used to generate the final map set. .

[0113] Ultimately, Write the data into the Neo4j graph database and output a vertical domain remote sensing feature knowledge graph that is compatible with the corresponding field or project, including... , , ,Will As external knowledge, reconstruct semantic clusters to improve the next round. This improves the accuracy of recall and forms a closed-loop knowledge mining system.

Claims

1. A method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph-enhanced retrieval, characterized in that, Includes the following steps: Step S1, Dataset Preparation: Prepare remote sensing image data, metadata information for the project dataset, and ground truth label dataset for remote sensing images. Step S1: External corpus and primary knowledge source; Step S2: Basic triplet extraction and feature parsing: Using metadata from the project dataset and coarse segmentation results of remote sensing images, identify geographic objects (Geo-Objects) and their geometric and spectral features, and construct a basic geospatial knowledge graph (Geo-KG); Step S3: LLM-assisted semantic and relation enhancement: Introduce a large-scale language model (LLM), based on an external corpus, and utilize... The mechanism retrieves structured evidence chains through topic community recall and multi-hop link retrieval, and performs knowledge expansion, relation completion, and semantic verification on the basic Geo-KG knowledge graph; Step S4, flexible quantification and output of Geo-KG knowledge: using a tensor decomposition model. Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. Based on statistics from a large-scale remote sensing ground truth dataset, a probabilistic topological intensity matrix was obtained through quantification. In step S4, a probabilistic topological intensity matrix is ​​obtained by statistical analysis based on a large-scale remote sensing ground truth dataset. The process is as follows: First, use the remote sensing image ground truth label dataset. Define the set of land cover categories as ， ， For the first Each category of land cover It is the total number of land cover categories; then, spatial co-occurrence frequency statistics are performed, which includes pixel pair statistics, total pixel pair statistics, and calculation of the initial co-occurrence frequency. Pixel pair statistics are... Each pair of land cover categories ， For the first Each category of land cover For the first Each land cover category, statistics and The number of spatially adjacent or functionally symbiotic pixel pairs Total pixel pair statistics are statistics Number of all pixel pairs appearing ； Calculate the first co-occurrence frequency in the initial co-occurrence frequency matrix. Line number Column elements : Next, the probabilistic topological strength value is calculated, and LLM and Geo-Object geometric factors are introduced for correction, resulting in the final strength value. The mixture is weighted and fused from three factors and then processed by the Sigmoid function. Normalization yields: ;in, It is a weighted fusion score, defined as: ;in, For geometric feature factors based on Geo-Object, For based on Statistical factors of the knowledge graph structure. This serves as a verification factor for the semantic rationality of topological relationships based on LLM. All are weighted coefficients; finally, the probabilistic matrix is ​​output, yielding a matrix with dimensions of [missing information]. Probabilized topological strength matrix ， As a soft constraint for topological consistency in the downstream model; Step S5, knowledge conflict correction and final graph construction: combining primary knowledge sources, through Evidence chain retrieval is performed on conflicting knowledge. The LLM (Local Level Management) system, combining knowledge aggregation and conflict resolution strategies, adjudicates the conflicts to ensure a complete vertical domain remote sensing feature knowledge graph is output. In step S5, the process of the LLM system adjudicating conflicts using knowledge aggregation and conflict resolution strategies is as follows: First, the LLM receives… First-level knowledge source rules and evidence chains Then, the LLM performs the following knowledge aggregation and conflict resolution strategies: Credibility assessment: based on the authority of the evidence source... Credibility scoring is performed; duplicate entity disambiguation: the entities in conflicting triples are re-verified for unmerged synonyms; relation consistency verification: the relation logic of conflicting triples is checked to ensure it conforms to domain axioms and authoritative evidence; finally, LLM correction is performed. Incorrect semantic embedding values ​​or topological strength values ​​are used to generate the final map set. 。 2. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 1, characterized in that, The specific process of step S2 is as follows: Step S21, coarse segmentation: Apply the SLIC algorithm to the remote sensing image to generate Geo-Object candidate regions with preliminary spatial boundaries; Step S22, metadata fusion: Associate the Geo-Object candidate regions with the metadata information of the project dataset to ensure that each superpixel block obtains a preliminary semantic label and spatial context, completing the preliminary semantic recognition of the Geo-Object; Step S23, geometric feature calculation: Calculate the geometric features of each obtained Geo-Object. Used to quantify the shape and size attributes of ground features; Step S24, Spectral feature calculation: Statistical analysis is performed on all pixels within the Geo-Object candidate region to calculate the spectral features of the Geo-Object. Step S25: Construct the basic triplet.

3. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 2, is characterized in that, The specific process of step S25 is as follows: taking the preliminary classification labels of the Geo-Object as entities, determining the node types of the geographic feature knowledge graph; and expanding the nine-intersection model based on dimensionality. Spatial operations are performed to extract initial topological relationships; initial functional relationships are extracted based on weakly supervised semantic initial rules; all calculated geometric and spectral features are used as attributes of Geo-Object entities, and finally, the initial set of basic triples for the geographic feature knowledge graph is output. ， ,in Representative attribute triples, This represents the union operation. Indicates the head entity. Indicates the tail entity. Indicates a relationship.

4. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 3, characterized in that, The specific process of step S3 is as follows: Step S31, Uncertainty analysis and identification: for The confidence level of the triples is evaluated, and triples with confidence levels below a preset threshold or lacking semantic evidence are selected for enhancement. In step S32, a GraphRAG deep retrieval mechanism is set up: based on the head entity, tail entity, and relation type of the triples to be enhanced, a refined retrieval query is constructed. Using a pre-built semantic graph from an external corpus, semantic clusters are identified through the Leiden algorithm, and the entire semantic cluster related to the target query topic is recalled. Multi-hop path analysis is performed to mine implicit causal and semantic chains across paragraphs and documents. The recalled text blocks are ranked by relevance, and the most informative text context set is selected to form an evidence chain. Step S33, Structured Knowledge Extraction and Completion Based on LLM: LLM for Evidence Chain To conduct analysis, The semantic rationality of the relationships in the dataset is verified. The LLM process performs entity extraction, relation extraction, attribute extraction, and event extraction tasks, producing formalized knowledge triples and event quadruples. The LLM identifies synonymous entities, assigns them unified labels, and generates new functional relation triples that conform to the common sense of the vertical domain. Finally, the enhanced Geo-KG set is output. 。 5. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 4, characterized in that, In step S4, the tensor decomposition model is used. Training the Geo-KG knowledge graph yields the knowledge graph embedding vectors. The process is as follows: First, the enhanced Geo-KG dataset is... All semantic triples in the training set ， ,in It is a head entity. ， It is a collection of entities. It's a relationship. ， It is a set of semantic relations. It is a tail entity. Then, model selection and initialization are performed, using a tensor decomposition model. As a knowledge graph embedding algorithm, initialize all entities and relationships Complex vector embedding; then minimizing the scoring function During training, the scoring function is defined as follows: ;in, yes The conjugate of the set is used, and the training objective is to maximize the score of correct triples and minimize the score of incorrect triples. The operator for extracting the real part is , and is the trilinear dot product in the complex field. After training, the knowledge graph embedding vectors for all categories of Geo-Object are obtained. ， ， For discrete entity indexing, For the first The latent feature vectors of each entity represent global semantic association information. For entity collection The base number.

6. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 5, is characterized in that, In step S5, through The process of evidence chain retrieval for conflicting knowledge is as follows: First, ... 、 The system takes an inviolable first-level knowledge source as input, which is a hard rule base; then, it performs semantic embedding verification, topological strength verification, and domain logic verification to identify conflicts; finally, it marks all identified conflicts as a set of triples to be corrected. ,use right For each conflicting triple, a chain of evidence is retrieved. The goal is to obtain a structured, traceable chain of evidence. Chain of evidence Points to the most authoritative text blocks or logical reasoning chains in an external corpus, used to support or refute conflicting knowledge.

7. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 6, characterized in that, In step S5, the goal of semantic embedding verification is to ensure that the semantic relationships between geo-objects conform to domain common sense. The process of semantic embedding verification is as follows: evaluation and Vector distance between ,like Less than the preset threshold However, the definition of a first-level knowledge source and If they are independent, they are marked as semantic conflicts; the goal of topology strength verification is to ensure that spatial symbiotic flexibility conforms to hard rules, and the process of topology strength verification is as follows: check The intensity value, if defined by the first-level knowledge source and They should never coexist, but the verification process found... Greater than the set threshold If a graph structure is found to be in a topological conflict, it is marked as such. The goal of domain logic verification is to ensure that the graph structure conforms to the domain axioms. The process of domain logic verification is as follows: Using triples in the context of rule-based logical reasoning, if... There exists The definition of a first-level knowledge source It is impossible to be If it is included, it is marked as a logical conflict.

8. The method for constructing a remote sensing geographic feature knowledge graph based on the synergy of geographic object geometric features and graph augmentation retrieval as described in claim 7, characterized in that, In step S5, finally, Write the data into the Neo4j graph database and output a vertical domain remote sensing feature knowledge graph that is compatible with the corresponding field or project, including... 、 、 ,Will As external knowledge, reconstruct semantic clusters to improve the next round. This improves the accuracy of recall and forms a closed-loop knowledge mining system.

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