Search Results (211)

Surveying and register administration are core to land administration, and accordingly, land surveying and registration are essential to socio-economic development due to their potential accuracy and efficiency. Until now, customary land surveying and registration have relied on human input, which is a situation that undermines efficiency and is prone to errors in data handling. During the last decade, the exponential growth in artificial intelligence (AI), in particular, geospatial artificial intelligence (GeoAI), has provided new methodologies that can overcome these deficiencies. This review examines AI in cadastral management by analyzing technical solutions and trends across three areas including data collection, modeling, and common applications. This review aims to provide a comprehensive survey of the current use of AI in cadastral management to the extent of defining a future research avenue. Based on the comprehensive review of literature, this study has reached the following three conclusions. (1) Automated extraction of parcel boundaries has been achieved through deep learning in data collection and processing, removing the bottlenecks of manual interpretation. Models such as convolutional neural networks (CNNs) and Transformers have been used for pixel-level semantic segmentation of high-resolution remote sensing images, leading to significant improvements in efficiency and accuracy. (2) Non-spatial data have been processed with natural language processing techniques to automatically extract information and construct relationships, thus overcoming the limitations of paper-based archives and traditional relational databases. (3) Deep learning models have been applied to automatically detect parcel changes and to enable integrated analysis of spatial and non-spatial data, which has supported the transition of cadastral management from two-dimensional to three-dimensional. However, several challenges remain, including differences in multi-temporal data processing, spatial semantic ambiguity, and the lack of large-scale, high-quality annotated data. Future research can focus on improving model generalization, advancing cross-modal data fusion, and providing recommendations for the development of a reliable and practical intelligent cadastral system. Full article

Training the public health workforce is a critical component of health system strengthening. In Mexico, postgraduate education operates under a national accreditation framework intended to ensure academic quality and social relevance, yet comprehensive information about the scope and distribution of training programs is limited. This study characterizes public health and related academic programs available in 2024, examining the institutional sector, delivery modality, geographic distribution, and accreditation status. A systematic institutional mapping was conducted through structured searches of the official websites of public and private higher education institutions. Eligible programs included bachelor’s degrees, specializations, master’s degrees, and PhDs that were active between March and November 2024. Searches used predefined keyword combinations, repeated at multiple timepoints, and were restricted to official institutional domains. Data were extracted on academic level, institutional sector, delivery format, duration, geographic region, and inclusion in the National Postgraduate System. Descriptive statistics and logistic regression were used to analyze accreditation patterns; geospatial analysis assessed regional distribution. A total of 175 programs were identified across 30 of Mexico’s 32 states. Professional master’s degrees represented the largest category, followed by research-oriented master’s and PhD programs. Public institutions offered nearly two-thirds of all programs. Among postgraduate programs, fewer than half were accredited, with accreditation concentrated in master’s degrees in science (84.6%) and PhDs (55.6%). Only 23.0% of professional master’s degree were accredited. Most programs were delivered fully in person; online offerings were limited and more common in private institutions. Research-oriented programs were geographically concentrated in a small number of states, whereas professional programs exhibited broader but uneven national distribution. Public health education in Mexico shows growth in professionally oriented training but also reveals persistent gaps in accreditation, geographic equity, and flexible delivery modalities. The disproportionate expansion of professional programs without corresponding integration into accreditation frameworks raises concerns for workforce planning and educational equity. Strengthening national information systems, improving institutional reporting standards, and aligning accreditation criteria with workforce needs are essential to ensure that public health training supports progress towards universal health coverage and the Sustainable Development Goals. Full article

Digital elevation models (DEMs) play a critical role in geospatial analysis and surface modeling. However, due to differences in data collection payload, data processing methodology, and data reference baseline, DEMs acquired from various sources often exhibit systematic spatial offsets. This limitation substantially constrains their accuracy and reliability in multi-source joint analysis and fusion applications. Traditional registration methods such as the Least-Z Difference (LZD) method are sensitive to gross errors, while multimodal registration approaches overlook the importance of elevation information. To address these challenges, this paper proposes a DEM registration method based on physically consistent rendering and multimodal image matching. The approach converts DEMs into image data through irradiance-based models and parallax geometric models. Feature point pairs are extracted using template-based matching techniques and further refined through elevation consistency analysis. Reliable correspondences are selected by jointly considering elevation error distributions and geometric consistency constraints, enabling robust affine transformation estimation and elevation bias correction. The experimental results demonstrate that in typical terrains such as urban areas, glaciers, and plains, the proposed method outperforms classical DEM registration algorithms and state-of-the-art remote sensing image registration algorithms. The results indicate clear advantages in registration accuracy, robustness, and adaptability to diverse terrain conditions, highlighting the potential of the proposed framework as a universal DEM collaborative registration solution. Full article

This study proposes a systematic framework for establishing ethical guidelines for GeoAI (Geospatial Artificial Intelligence), which integrates AI with spatial data science, GIS, and remote sensing. While general AI ethics have advanced through the OECD, UNESCO, and the EU AI Act, ethical standards tailored to GeoAI remain underdeveloped. Geospatial information exhibits unique characteristics, spatiality, contextuality, and spatial autocorrelation—and consequently entails distinct risks such as geo-privacy, spatial fairness and bias, data provenance and quality, and misuse prevention related to mapping and surveillance. Following PRISMA 2020, a systematic review of 32 recent international policy documents and peer-reviewed articles was conducted; through content analysis with intercoder reliability verification (Krippendorff’s α ≥ 0.76), GeoAI ethical principles were extracted and normalized. The analysis identified twelve ethical axes—Geo-privacy, Data Provenance and Quality, Spatial Fairness and Bias, Transparency, Accountability and Auditability, Safety (Security and Robustness), Human Oversight and Human-in-the-Loop, Public Benefit and Sustainability, Participation and Stakeholder Engagement, Lifecycle Governance, Misuse Prevention, and Inclusion and Accessibility—each accompanied by an operational guideline. These axes together form a practical framework that integrates universal AI ethics principles with spatially specific risks inherent in GeoAI and specifies actionable assessment points across the GeoAI lifecycle. The framework is intended for direct use as checklists and governance artifacts (e.g., model/data cards) and as procurement and audit criteria in academic, policy, and administrative settings. Full article

With rapid advancements in sub-meter satellite and aerial imaging technologies, high-resolution remote sensing imagery has become a pivotal source for geospatial information acquisition. However, current semantic segmentation models encounter two primary challenges: (1) the inherent trade-off between capturing long-range global context and preserving precise local structural details—where excessive reliance on downsampled deep semantics often results in blurred boundaries and the loss of small objects and (2) the difficulty in modeling complex scenes with extreme scale variations, where objects of the same category exhibit drastically different morphological features. To address these issues, this paper introduces MAFMamba, a multi-scale adaptive fusion visual Mamba network tailored for high-resolution remote sensing images. To mitigate scale variation, we design a lightweight hybrid encoder incorporating an Adaptive Multi-scale Mamba Block (AMMB) in each stage. Driven by a Multi-scale Adaptive Fusion (MSAF) mechanism, the AMMB dynamically generates pixel-level weights to recalibrate cross-level features, establishing a robust multi-scale representation. Simultaneously, to strictly balance local details and global semantics, we introduce a Global–Local Feature Enhancement Mamba (GLMamba) in the decoder. This module synergistically integrates local fine-grained features extracted by convolutions with global long-range dependencies modeled by the Visual State Space (VSS) layer. Furthermore, we propose a Multi-Scale Cross-Attention Fusion (MSCAF) module to bridge the semantic gap between the encoder’s shallow details and the decoder’s high-level semantics via an efficient cross-attention mechanism. Extensive experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate that MAFMamba surpasses state-of-the-art Convolutional Neural Network (CNN), Transformer, and Mamba-based methods in terms of mIoU and mF1 scores. Notably, it achieves superior accuracy while maintaining linear computational complexity and low memory usage, underscoring its efficiency in complex remote sensing scenarios. Full article

Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typically possess high-dimensional spatiotemporal attributes. Effectively extracting underlying patterns and meaningful information from such complex data is crucial for informed decision-making. This study addresses the challenge of visually representing temporal sequences within geospatial contexts, a process often hindered by the separate visualization of spatial and temporal dimensions. We propose a method that embeds a geographic map within a parallel coordinate plot: time is represented on the parallel axes, and high-dimensional attributes are encoded using color channels. This integrated view, combined with a suite of interactive techniques, enables detailed, multi-perspective, and holistic visual exploration and enhances the understanding of high-dimensional spatiotemporal meteorological data. Full article

Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance. Full article

Soil salinity is an essential constraint on sustainable crop production, particularly in arid and semi-arid regions, due to its effects on soil fertility. This study presents a data-driven approach for mapping soil salinity by integrating field-based electrical conductivity (EC) measurements with remote sensing and geospatial analysis in the district of Mandi Baha Uddin, Pakistan. Eleven georeferenced soil samples were collected and analyzed for EC (range: 0.59–1.06 dS/m), serving as training data for model calibration. Using Landsat 8 Surface Reflectance imagery within Google Earth Engine, spectral indices Normalized Difference Salinity Index (NDSI), Salinity Index (SI), and Brightness Index (BI) were extracted. Among various modeling approaches, a linear regression model was applied to these indices, revealing NDSI as the most significant predictor (coefficient = 12.938), while SI and BI show negligible contribution. The model achieved moderate accuracy (R² = 0.566, RMSE = 0.085 dS/m). A Random Forest approach yielded higher training accuracy (R² = 0.841) but suffered from overfitting during cross-validation, indicating limited sample size constraints. The regression equation (EC = 12.938 × NDSI + 5.864) was applied in GEE to generate the EC prediction map. The resulting 30 m resolution EC map was classified into salinity categories and validated through independent field observations. This framework highlights the effectiveness of using freely available satellite data and cloud-based platforms like GEE for cost-effective soil salinity monitoring. The study provides a transferable methodology for precision agriculture, enabling informed land management and crop planning in salinity-affected regions. Full article

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice. Full article

Urban informal settlements (UISs), characterized by self-organized housing, a high population density, inadequate infrastructure, and insecure land tenure, constitute a critical, yet underexplored, aspect of contemporary urbanization. They necessitate scholarly scrutiny to tackle pressing challenges pertaining to equity, sustainability, and urban governance. The automated, accurate, and rapid extraction of UISs is of paramount importance for sustainable urban development. Despite its significance, this process encounters substantial obstacles. Firstly, from a remote sensing standpoint, informal settlements are typically characterized by a low elevation and a high density, giving rise to intricate spatial relationships. Secondly, the remote sensing observational features of these areas are often indistinct due to variations in shooting angles and imaging environments. Prior studies in remote sensing and geospatial data analysis have often overlooked the cross-modal interactions of features, as well as the progressive information encoded in the intrinsic hierarchies of each modality. We introduced a spatial network to solve this problem by combining panoramic and coarse-to-fine asymptotic perspectives, using remote sensing images and urban street view images to support a hierarchical analysis through fusion. Specifically, we utilized a multi-linear pooling technique and then established coarse-to-fine-grained and panoramic viewpoint details within an integrated structure, known as the panoramic fusion network (PanFusion-Net). Comprehensive testing was performed on a self-constructed WuhanUIS dataset as well as two open-source datasets, ChinaUIS and $S^{2}UV$. The experimental results confirmed that the performance of the introduced PanFusion-Net exceeded all comparative models across all of the above datasets. Full article

As cyber threats continue to grow in complexity and sophistication, the need for advanced network and sensor security solutions has never been more urgent. Traditional intrusion detection methods struggle to keep pace with the sheer volume of network traffic and the evolving nature of attacks. In this paper, we propose a novel machine learning-driven Intrusion Detection System (IDS) that improves intrusion detection through a comprehensive analysis of multidimensional data. Transcending traditional feature extraction methods, the system introduces geospatial context features and self-supervised semantic features that provide rich contextual information for enhanced threat identification. The system’s performance is validated on a carefully curated dataset from China Mobile, containing over 100 K records, achieving an impressive 98.5% accuracy rate in detecting intrusions. The results highlight the effectiveness of ensemble learning methods and underscore the system’s potential for real-world deployment, offering a significant advancement in the development of intelligent cybersecurity tools that can adapt to the ever-changing landscape of cyber threats. Furthermore, the proposed framework is extensible to IoT and wireless sensor networks (WSNs), where resource constraints and new attack surfaces demand lightweight yet semantically enriched IDS solutions. Full article

The cultural heritage of the Liao dynasty in Chifeng encompasses significant historical and cultural information that requires systematic digital preservation and management. However, heterogeneous data sources across museums, archives, and research institutions lack semantic interoperability, creating barriers for cross-system integration and knowledge discovery. This study proposes a standardized knowledge graph construction method by integrating the CIDOC Conceptual Reference Model version 7.2 with large language models. A unified ontology framework enables semantic consistency across diverse heritage data, while Generative Pre-trained Transformer-based models automatically extract structured triples from unstructured texts through prompt engineering and entity disambiguation, with the resulting knowledge graph implemented in Neo4j graph database. The constructed knowledge graph integrates 106 immovable cultural heritage records from Chifeng City with approximately 20 types of semantic relationships, forming a comprehensive semantic network covering people, places, events, time, and materials. K-means clustering reveals five cultural value themes, including “Nomadic Imperial Power System” and “Multi-Capital Governance Network”, while geospatial mapping identifies a “dual-core and ring-belt” distribution pattern for heritage protection zoning. This research demonstrates how international semantic standards can be integrated with artificial intelligence technologies to enable interoperable cultural heritage knowledge systems, providing practical implications for cross-institutional heritage management and archaeological survey planning. Full article

Natural resource-endowed landscapes in many parts of the Global South play a crucial role in the livelihoods of communities. Such resource-endowed areas attract current and prospective resource-use actors, making them veritable hollow frontiers. Hollow frontiers, as crucial resource attractions in many parts of sub-Saharan Africa (SSA), have attracted significant interest in scientific and policy circles. While studies have explored the patterns of migration and population change around hollow frontiers, there is limited evidence on the resource-use dynamics and trajectories in hollow frontiers. This study uses the case of the Mungo Corridor of Cameroon, a hollow frontier par excellence, to (1) determine the variations in forestland resource-use practices, and (2) analyze changes in forestland resource space in the corridor. Data for this study was collected through key informant interviews (n = 37), focus group discussions (n = 15), household surveys using a structured questionnaire (n = 250), and Landsat images. Geospatial analysis, descriptive statistics, and the chi-square statistical technique were employed in the analysis. The study revealed that forestland resource-use practices (NTFPs harvesting) witnessed a significant decline due to the intensification of extraction rates. Furthermore, forestland witnessed a significant decline in Njombe-Penja and Loum (35.216% and 48.176%, respectively) between 1984 and 2024. The results provide novel insights on the pattern of resource use around hollow frontiers and further informs land management policy in the context of the regulation of land-based resources in the hollow frontiers of Cameroon and similar sub-Saharan African contexts. Future studies should explore forestland resource regeneration strategies in the Mungo Corridor. Full article

High-precision semantic segmentation of remote sensing imagery is crucial in geospatial analysis. It plays an immeasurable role in fields such as urban governance, environmental monitoring, and natural resource management. However, when confronted with complex objects (such as winding roads and dispersed buildings), existing semantic segmentation methods still suffer from inadequate target recognition capabilities and multi-scale representation issues. This paper proposes a neural network model, LMVMamba (LoRA Multi-scale Vision Mamba), for semantic segmentation of remote sensing images. This model integrates the advantages of convolutional neural networks (CNNs), Transformers, and state-space models (Mamba) with a multi-scale feature fusion strategy. It simultaneously captures global contextual information and fine-grained local features. Specifically, in the encoder stage, the ResT Transformer serves as the backbone network, employing a LoRA fine-tuning strategy to effectively enhance model accuracy by training only the introduced low-rank matrix pairs. The extracted features are then passed to the decoder, where a U-shaped Mamba decoder is designed. In this stage, a Multi-Scale Post-processing Block (MPB) is introduced, consisting of depthwise separable convolutions and residual concatenation. This block effectively extracts multi-scale features and enhances local detail extraction after the VSS block. Additionally, a Local Enhancement and Fusion Attention Module (LAS) is added at the end of each decoder block. LAS integrates the SimAM attention mechanism, further enhancing the model’s multi-scale feature fusion capability and local detail segmentation capability. Through extensive comparative experiments, it was found that LMVMamba achieves superior performance on the OpenEarthMap dataset (mIoU 52.3%, OA 69.8%, mF1: 68.0%) and LoveDA (mIoU 67.9%, OA 80.3%, mF1: 80.5%) datasets. Ablation experiments validated the effectiveness of each module. The final results indicate that this model is highly suitable for high-precision land-cover classification tasks in remote sensing imagery. LMVMamba provides an effective solution for precise semantic segmentation of high-resolution remote sensing imagery. Full article