Search Results (18)

We present a novel deep learning framework for high-resolution semantic segmentation, designed to interpret complex visual environments such as cities, rural areas, and natural landscapes. Our method integrates conic geometric embeddings, which is a mathematical approach for capturing spatial relationships, with belief-aware learning, a strategy that adapts model predictions when input data are uncertain or change over time. A multi-scale refinement process further improves boundary accuracy and detail preservation. The proposed model, built on a hybrid Vision Transformer (ViT) backbone and trained end-to-end using adaptive optimization, is evaluated on four benchmark datasets including EDEN, OpenEarthMap, Cityscapes, and iSAID. It achieves 88.94% Accuracy and $R^2$ of 0.859 on EDEN, while surpassing 85.3% Accuracy on Cityscapes. Ablation studies demonstrate that removing Conic Output Embeddings causes drops in Accuracy of up to 2.77% and increases in RMSE, emphasizing their contribution to frequency-aware generalization across diverse conditions. On OpenEarthMap, our model achieves a mean IoU of 73.21%, outperforming previous baselines by 2.9%, and on iSAID, it reaches 80.75% mIoU with improved boundary adherence. Beyond technical performance, the framework enables practical applications such as automated landscape analysis, urban growth monitoring, and sustainable environmental planning. Its consistent results across three independent runs demonstrate both robustness and reproducibility, offering a reliable tool for large-scale geospatial and environmental modeling. Full article

Historic earthen architecture—particularly rammed earth—is underrepresented in digital heritage initiatives despite its widespread historical use and vulnerability to degradation. This paper presents a novel methodology for integrating semantic, geometric, and geospatial information from earthen heritage into a unified digital environment, bridging Heritage Building Information Modeling (HBIM) and Geographic Information Systems (GIS) through an object-relational database. The proposed workflow enables automated and bidirectional data exchange between Revit (via Dynamo scripts) and open-source GIS tools (QGIS and PostgreSQL/PostGIS), supporting semantic alignment and spatial coherence. The method was tested on seven fortified rammed-earth sites in the southwestern Iberian Peninsula, chosen for their typological and territorial diversity. Results demonstrate the feasibility of multiscale documentation and analysis, supported by a structured database populated with geometric, semantic, diagnostic, and environmental information, enabling enriched interpretations of construction techniques, material variability, and conservation status. The approach also facilitates the integration of HBIM datasets into broader territorial management frameworks. This work contributes to the development of scalable, open-source digital tools tailored to vernacular heritage, offering a replicable strategy for bridging the gap between building-scale and landscape-scale documentation in cultural heritage management. Full article

To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces. Full article

The demand for interoperable, lifecycle-oriented data exchange in the port and waterway sector is intensifying amid global digital transformation and infrastructure modernisation. Traditional Building Information Modelling (BIM) practices often fail to capture the domain-specific complexity and multidisciplinary collaboration required in maritime infrastructure. This paper critically evaluates the IFC 4.3 schema as a foundational standard for openBIM-based integration in this sector, offering a semantic alignment framework designed for the planning, design, and operational phases of port projects. Rather than proposing schema extensions, the framework interprets existing IFC constructs to model port-specific assets while supporting environmental and geospatial integration. Two case studies, a master planning project for a shipyard and a design coordination project for a ship lock complex, demonstrate the schema’s capability to facilitate federated modelling, reduce semantic discrepancies, and enable seamless data exchange across disciplines and software platforms. The research delivers actionable implementation strategies for practitioners, identifies technical limitations in current toolchains, and outlines pathways for advancing standardisation efforts. It further contributes to the evolving discourse on digital twins, GIS-BIM convergence, and semantic enrichment in infrastructure modelling. This work provides a scalable, standards-based roadmap to improve interoperability and enhance the digital maturity of port and waterway infrastructure. Full article

This study presents a practical methodology for integrating the multiscale spatial information of archaeological sites by combining Geographic Information Systems (GISs) with Historic Building Information Modelling (HBIM). The methodology categorises and integrates data based on its type and geometric scale, leveraging advanced 3D surveying techniques alongside semantic and parametric modelling tools. A multiscale system is proposed to manage heterogeneous geospatial data efficiently, enabling the development of enriched geometric models with detailed semantic and parametric attributes. The effectiveness of this approach is demonstrated through a case study of the Archaeological Area of Ancient “Abellinum”, showcasing seamless integration between HGISs and HBIM across multiple levels of detail. This work highlights the potential for enhanced management and the interpretation of archaeological heritage using innovative digital methodologies, highlighting the importance of representation in documenting historical transformations. Full article

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article

In the digital transformation era, video media libraries’ untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study’s findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications. Full article

Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility prediction with multi-modal remote sensing data involving digital elevation models, optical remote sensing, and an SAR dataset. Moreover, based on the results generated by multi-modal remote sensing data, we further conducted landslide and mudslide susceptibility prediction with semantic knowledge. Through the comparisons with the ground truth datasets created by field investigation, experimental results have proved that remote sensing data can only enhance deep learning techniques to detect the landslide and mudslide, rather than the landslide and mudslide susceptibility. Knowledge regarding the potential clues about landslide and mudslide, which would be critical for estimating landslide and mudslide susceptibility, have not been comprehensively investigated yet. Full article

The objects presented in synthetic-aperture radar (SAR) images are the products of the joint actions of ground objects and SAR sensors in specific geospatial contexts. With the accumulation of massive time-domain SAR data, scholars have the opportunity to better understand ground-object targets and sensor systems, providing some useful feedback for SAR-data processing. Aiming at normalized and low-cost SAR radiometric monitoring, this paper proposes a new hyper-pixel concept for handling multi-pixel ensembles of semantic ground targets. The special hyper-pixel in this study refers to low-rise single-family residential areas, and its radiation reference is highly stable in the time domain when the other dimensions are fixed. The stability of its radiometric data can reach the level of 0.3 dB (1$\sigma$), as verified by the multi-temporal data from Sentinel-1. A comparison with tropical-rainforest data verified its availability for SAR radiometric monitoring, and possible radiation variations and radiation-intensity shifts in the Sentinel-1B SAR products ere experimentally monitored. In this paper, the effects of seasonal climate and of the relative geometrical states observed on the intensity of the hyper-pixel’s radiation are investigated. This paper proposes a novel hyper-pixel concept for processing and interpreting SAR-image data. The proposed residential hyper-pixel is shown to be useful in multi-temporal-data observations for normalized radiometric monitoring and has the potential to be used for cross-calibration, in addition to other applications. Full article

Over the life cycle of a civil infrastructure (a bridge as an example), 0.4–2% of the construction cost is spent annually on its maintenance. Utilising new technologies including the internet of things (IoT) and digital twin (DT) can significantly reduce the infrastructure maintenance costs. An infrastructure DT involves its digital replica and must include data on geometric, geospatial reference, performance, attributes (material, environment etc.) and management. Then, the acquired data need to be analysed and visualised to inform maintenance decision making. To develop this DT, the first step is the study of the infrastructure life cycle to design DT architecture. Using data semantics, this paper presents a novel DT architecture design for an intelligent infrastructure maintenance system. Semantic modelling is used as a powerful tool to structure and organize data. This approach provides an industry context through capturing knowledge about infrastructures in the structure of semantic model graph. Using new technologies, DT approach derives and presents meaningful data on infrastructure real-time performance and maintenance requirements, and in a more expressible and interpretable manner. The data semantic model will guide when and what data to collect for feeding into the infrastructure DT. The proposed DT concept was applied on one of the conveyors of Dalrymple Bay Coal Terminal in Queensland Australia to monitor the structural performance in real-time, which enables predictive maintenance to avoid breakdowns and disruptions in operation and consequential financial impacts. Full article

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc. Full article

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling. Full article

One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent. Full article

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation. Despite convolutional neural networks (CNNs) having facilitated the development in this domain, the computation efficiency under real-time application and the accurate positioning on relatively small objects in HSR images are two noticeable obstacles which have largely restricted the performance of detection methods. To tackle the above issues, we first introduce semantic segmentation-aware CNN features to activate the detection feature maps from the lowest level layer. In conjunction with this segmentation branch, another module which consists of several global activation blocks is proposed to enrich the semantic information of feature maps from higher level layers. Then, these two parts are integrated and deployed into the original single shot detection framework. Finally, we use the modified multi-scale feature maps with enriched semantics and multi-task training strategy to achieve end-to-end detection with high efficiency. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset have demonstrated the superiority of the presented method. Full article

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by the ever-increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper, we explore the developmental framework of a pure JavaScript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend JavaScript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting JavaScript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform. Full article