To address the challenges in current research on spatial clustering algorithms for buildings in topographic maps—namely, their limited ability to effectively accommodate diverse application scenarios, including dense and regular urban environments, sparsely and irregularly distributed rural areas, and urban villages with complex structures—this paper introduces an innovative progressive clustering algorithm framework. The proposed framework operates in a hierarchical manner, progressing from macro to micro levels, thereby enhancing its adaptability and practical versatility. Specifically, it employs the minimum spanning tree (MST) technique for macro-level clustering analysis. Subsequently, a self-organizing map (SOM) neural network is utilized to perform micro-level clustering, enabling a more refined and detailed classification. Within this framework, the minimum spanning tree effectively captures the macroscopic distribution patterns of the building population. The macroscopic clustering results are then utilized as the initial weight configurations for the SOM neural network. This approach ensures that the overall spatial structural integrity is preserved during the subsequent micro-level clustering process. Moreover, the SOM neural network achieves refined optimization of micro-clustering details by incorporating building feature factors. To validate the effectiveness of the proposed algorithm, this study conducts an empirical analysis and comparative testing using building data from Futian District, Shenzhen City. The results indicate that the proposed algorithm exhibits superior recognition capabilities when applied to complex and variable spatial distribution patterns of buildings. Furthermore, the clustering outcomes align closely with the principles of Gestalt visual perception and outperform the comparison algorithms in overall performance.

Rapid and reliable building damage assessment (BDA) is crucial for post-disaster emergency response. However, existing methods face challenges such as complex background interference, the difficulty in jointly modeling local geometric details and global spatial dependencies, and adverse weather conditions. To address these issues, this paper proposes the Adaptive Difference State-Space Fusion Network (ADSFNet), capable of processing both optical and Synthetic Aperture Radar (SAR) data to alleviate weather-induced limitations. To achieve this, ADSFNet innovatively introduces the Adaptive Difference Attention Fusion (ADAF) module and the Hybrid Selective State-Space Convolution (HSSC) module. Specifically, ADAF integrates pre- and post-disaster features to guide the network to focus on building regions while suppressing background interference. Meanwhile, HSSC synergizes the local texture extraction of CNNs with the global modeling strength of Mamba, enabling the simultaneous capture of cross-building spatial relationships and fine-grained damage details. Experimental results on sub-meter high-resolution MultiModal (BRIGHT) and optical (xBD) datasets demonstrate that ADSFNet attains F1 scores of 71.36% and 73.98%, which are 1.29% and 0.6% higher than the state-of-the-art mainstream methods, respectively. Finally, we leverage the model outputs to construct a disaster-centric knowledge graph and integrate it with Large Language Models to develop an intelligent management system, providing a novel technical pathway for emergency decision-making.

Building extraction from high-resolution remote sensing imagery holds significant value for urban planning, disaster assessment, and geospatial analysis. However, current semantic segmentation models still face limitations when handling complex scenarios characterized by diverse building morphologies, significant scale variations, and blurred boundaries. To address the challenges of insufficient long-range dependency modeling, suboptimal multi-scale feature representation, and weak spatial adaptability, this paper proposes a building extraction network that integrates multi-scale sequence modeling with spatial adaptive enhancement. Adopting UPerNet (equipped with ConvNeXt-Tiny) as the baseline framework, the proposed method introduces a dedicated PyramidSSM-based neck (PyramidSSMNeck) as the primary design for multi-scale feature alignment and fusion, and further integrates three enhancement components (S6 (SSM-based), LSKNet, and SAFM) that provide additional improvements mainly reflected in boundary delineation. Specifically, PyramidSSMNeck performs structured cross-scale feature projection, alignment, and aggregation to strengthen multi-scale representation; S6 enhances long-range contextual modeling, LSKNet adaptively adjusts spatial receptive fields to accommodate scale variations, and SAFM modulates feature responses with spatial cues to refine boundaries and fine details—forming a unified framework in which PyramidSSMNeck primarily drives multi-scale alignment and fusion, while S6, LSKNet, and SAFM further enhance long-range context modeling and spatial adaptivity, mainly benefiting boundary preservation and fine-detail integrity. Experiments were conducted on the WHU Building, INRIA, and a self-constructed Ganzhou urban dataset, and the results indicate that the proposed method achieved IoU scores of 91.29%, 81.96%, and 88.18% across the three datasets, outperforming the baseline UPerNet (ConvNeXt-Tiny) by 2.37%, 0.88%, and 3.68%, respectively, with F1-scores consistently exceeding 90%. Importantly, ablation results indicate that the majority of region-level gains (IoU/F1) come from PyramidSSMNeck, whereas the additional modules contribute more prominently to boundary quality, yielding a Boundary IoU increase from 63.29% to 65.63% (+2.34) from the neck-only setting to the full model. Visualization results further support the method’s advantages in boundary preservation and detail integrity, and additional cross-domain transfer experiments (zero-shot and few-shot from WHU to Ganzhou) suggest improved robustness under domain shift.

Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to different classification systems, which brings great obstacles to the management and sharing of geographical information. This study analyzes the complexities of multiple classifications of geographical entities and proposes a multi-classification model for geographical entities based on directed hypergraph theory. This model integrates and transforms different classification systems for the same geographical entity, creating a unified method for expressing multiple classifications. We also designed a data structure to support this unified expression. By implementing this model, the study enables the effective management of geographical entity data, facilitating improved sharing and the exchange of geographical information across different industries and applications. In practical, the multi-classification model proposed in this paper allows geographical entities from different classification systems to be stored and managed within a single geographical database. Data views are then used to provide tailored services to various industry sectors and business applications. This approach effectively reduces data duplication and enhances the efficiency of managing and sharing geographical information. Using land use classification as an example, this study constructs a unified expression of three different land use classification systems based on the multi-classification model. An experiment managing land use data for a specific city was conducted using this model in PostgreSQL. The results indicate that the proposed method not only reduces data redundancy but also improves the query efficiency by over 10% on average compared to the mainstream relational database management mode. This confirms the effectiveness and practical value of the proposed method.

Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge.

Mine ecological restoration involves multiple tasks, including ecological problem identification, restoration measure selection, and restoration effect evaluation. Relevant knowledge is widely scattered across academic literature and policy and regulatory documents, making it difficult to directly support structured queries, knowledge association, and measure screening. To address the insufficient organization of knowledge in mine ecological restoration and the unstable quality of triples extracted by large language models, this study proposes a large language model-driven workflow for knowledge graph construction and restoration measure decision support in mine ecological restoration. First, a weak ontology schema containing eight entity types and eight relation types was constructed, and a manually annotated dataset was developed based on 30 papers on mine ecological restoration. Second, multiple large language models were compared on the triple extraction task, and Kimi, which achieved the best overall performance, was selected for large-scale knowledge extraction. On this basis, a weak-ontology-guided triple quality control method, named WOTQC, was proposed to check the structural consistency of candidate triples in terms of entity type, relation type, relation direction, and entity orientation. DeepSeek was then introduced to perform verification based on original textual evidence. Subsequently, entity normalization and relation aggregation were conducted on the verified triples to construct a knowledge graph for mine ecological restoration. Path mining, restoration measure decision support, and external case validation were further performed around the “ecological problem–restoration measure–restoration effect” path. The experimental results show that Kimi-based initial extraction achieved an F1 score of 0.7286. After WOTQC processing, the F1 score increased to 0.7561, and after DeepSeek-based evidence verification, it further improved to 0.7862. The final knowledge graph contains 18,783 entity nodes and 23,149 relations, and identifies 3048 “ecological problem–restoration measure–restoration effect” paths. The external case validation shows that the integrated ranking method achieved 1.00 under both strict Hit@3 and strict Hit@5, with a path explanation coverage@5 of 0.7500. The ranking results of the knowledge graph showed good correspondence with the main engineering measures in an actual mine ecological restoration design scheme. The results indicate that weak ontology constraints and verification based on original textual evidence can improve the reliability of triple extraction, while entity normalization and relation aggregation can enhance the structural consistency of the knowledge graph. The constructed knowledge graph can provide traceable and interpretable knowledge support for knowledge organization, candidate restoration measure screening, and restoration scheme formulation in mine ecological restoration, thereby supporting more informed and evidence-based restoration decision-making and contributing to the long-term ecological recovery and sustainable management of degraded mining areas.