Search

Abstract The practical development of compact modern nanophotonic devices relies on the availability of fast and low-cost fabrication techniques applicable to a wide variety of materials and designs. We have engraved a split grating geometry on stainless steel using femtosecond laser processing. This structure serves as a template to fabricate efficient plasmonic sensors, where a thick gold layer is grown conformally on it. The scanning electron microscope (SEM) images confirm the generation of the split laser-induced periodic spatial structures. The optical reflectance of our sensors shows two dips corresponding to the excitation of surface plasmon resonances (SPRs) at two different wavelengths. Furthermore, the asymmetric shape of these spectral responses reveals a strong and narrow Fano resonance. Our computational electromagnetism models accurately reproduce the reflectivity of the fabricated structure. The spectral responses of both the simulated and fabricated structures are fitted to the Fano model that coherently combines the narrow SPRs with the broad continuum background caused by diffraction. The parameters extracted from the fitting, such as the resonance wavelengths and line widths, are used to evaluate the performance of our device as a refractometric sensor for liquids. The maximum sensitivity and figure of merit are 880 nm/RIU and 80 RIU−1, respectively. Besides the compact design of our sensing device, its performance exceeds the theoretical maximum sensitivity of a classical Kretschmann setup.

Abstract Optical fiber sensors (OFSs) have attracted more and more attention in the field of in-situ ocean observation because of their advantages of the small size, light weight, low cost, and intrinsic immunity to electromagnetic interference. Here, we comprehensively reviewed the development of various advanced physical and chemical OFSs for in-situ measurement of ocean key parameters, including temperature, salinity, pressure, acoustics, flow velocity and turbulence, potential of hydrogen (pH), heavy metal ions, nitrates, and gases. Moreover, the performances of different kinds of OFSs and their practical sea trial reports for in-situ measurement in the marine environment are provided. As we discussed, the field of OFS is rapidly evolving and is poised to make essential breakthroughs in marine monitoring, leading to better utility of marine resources.

Abstract Hydrogen, a high-density and clean energy, has been widely used in various critical applications. However, the safety risk caused by hydrogen leakage during storage and transportation is still a non-negligible issue. Therefore, it is necessary to offer hydrogen sensors with fast response and high repeatability, and it will be perfect for achieving in situ monitoring over the lifecycle of hydrogen production and utilization. Here, we propose a compact optical fiber sensor with a short section of the tilted Bragg fiber grating (TFBG) inscribed in the fiber core and a palladium and tungsten trioxide (Pd/WO3) combined film of 40 nm thickness over the fiber surface. The TFBG excites tens of narrow cladding resonances, part of which possess refractive indexes matching that of the Pd/WO3 coating and providing the high sensitivity to the surrounding hydrogen concentration change. The sensor offers improved sensing characteristics, including the fast response time (less than 10 s), high repeatability (over tens of measurement), and excellent linear response (higher than 99.6%) over the 0% to 3% concentration range.

Abstract We study twisted bilayer van der Waals (vdW) materials as a platform to generate versatile bremsstrahlung X-rays, and show that the twist angle in bilayer vdW materials provides an unprecedented degree of controllability over various properties of bremsstrahlung radiation from these materials. Specifically, we combine the waveshaping of the free electron’s quantum wavepacket with the unique crystalline atomic positioning of twisted bilayers to realize shaped bremsstrahlung X-rays, which feature enhancements in directionality and intensity. In the process, we present a theoretical model for bremsstrahlung radiation that is applicable to twisted multilayer vdW materials in general. We also investigate the dependence of our X-ray emission mechanism on physical parameters, including the interlayer spacing and number of layers. Our findings pave the way for the use of twisted multilayer van der Waals materials in the generation of tailored X-ray spectra for applications like X-ray imaging, X-ray fluorescence, and X-ray treatment.

Abstract Cancer-associated adipocytes (CAAs) have emerged as pivotal players in various cancers, particularly in such as breast cancer, significantly influencing their progression and therapy resistance. Understanding the adipocytes/cancer cells crosstalk is crucial for effective treatment strategies. Raman spectroscopy, a label-free optical technique, offers potential for characterizing biological samples by providing chemical-specific information. In this study, we used Raman spectroscopy and Trajectory Inference methods, specifically the Partition-based graph abstraction algorithm, to investigate the interactions between 3T3-L1 differentiated adipocytes and MDA-MB-231 breast cancer cells in a 2D co-culture model. We demonstrate the existence of subpopulations of adipocytes and the molecular changes associated with CAAs phenotype. This work contributes to understanding the role of CAAs in breast cancer progression and may guide the development of targeted therapies disrupting this interaction.

Abstract Computer holography is a prominent technique for reconstructing customized three-dimensional (3D) diffraction fields. However, the quality of optical reconstruction remains a fundamental challenge in 3D computer holography, especially for the 3D diffraction fields with physically continuous and extensive depth range. Here, we propose a 3D computer-generated hologram (CGH) optimization framework with phase space tailoring. Based on phase space analysis of the space and frequency properties in both lateral and axial directions, the intensity of the 3D diffraction field is adequately sampled across a large depth range. This sampling ensures the reconstructed intensity distribution to be comprehensively constrained with physical consistency. A physics-informed loss function is constructed based on the phase space tailoring to optimize the CGH with suppression of vortex stagnation. Numerical and optical experiments demonstrate the proposed method significantly enhances the 3D optical reconstructions with suppressed speckle noise across a continuous and extensive depth range.

Abstract Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications, sensing and metrology, as well as for the exploration of fundamental properties of light-matter interaction and optical nonlinearity. Real-time feedback for active control over light can provide a significant advantage in these endeavors, compensating for ever-changing experimental conditions and inherent or accumulated device flaws. Scanning nearfield microscopy, being slow in essence, cannot provide such a real-time feedback that was thus far possible only by scattering-based microscopy. Here, we present active control over nanophotonic near-fields with direct feedback facilitated by real-time near-field imaging. We use far-field wavefront shaping to control nanophotonic patterns in surface waves, demonstrating translation and splitting of near-field focal spots at nanometer-scale precision, active toggling of different near-field angular momenta and correction of patterns damaged by structural defects using feedback enabled by the real-time operation. The ability to simultaneously shape and observe nanophotonic fields can significantly impact various applications such as nanoscale optical manipulation, optical addressing of integrated quantum emitters and near-field adaptive optics.

Abstract Quantum optics has advanced our understanding of the nature of light and enabled applications far beyond what is possible with classical light. The unique capabilities of quantum light have inspired the migration of some conceptual ideas to the realm of classical optics, focusing on replicating and exploiting non-trivial quantum states of discrete-variable systems. Here, we further develop this paradigm by building the analogy of quantum squeezed states using classical structured light. We have found that the mechanism of squeezing, responsible for beating the standard quantum limit in quantum optics, allows for overcoming the “standard spatial limit” in classical optics: the light beam can be “squeezed” along one of the transverse directions in real space (at the expense of its enlargement along the orthogonal direction), where its width becomes smaller than that of the corresponding fundamental Gaussian mode. We show that classical squeezing enables nearly sub-diffraction and superoscillatory light focusing, which is also accompanied by the nanoscale phase gradient of the size in the order of λ/100 (λ/1000), demonstrated in the experiment (simulations). Crucially, the squeezing mechanism allows for continuous tuning of both features by varying the squeezing parameter, thus providing distinctive flexibility for optical microscopy and metrology beyond the diffraction limit and suggesting further exploration of classical analogies of quantum effects.

An Ms6.2 earthquake occurred in Jishishan County, Linxia Hui Autonomous Prefecture, Gansu Province on December 18, 2023, which is another strong earthquake event that occurred in the eastern and northeast margin of the Qinghai-Tibet Plateau after the Ms6.8 Luding earthquake in 2022. To determine the spatial characteristics and human casualties of the earthquake, this paper obtains the seismic spatial information based on remote sensing data. The results show that the earthquake has thrust characteristics and is a thrust-type earthquake, in which the line-of-sight deformation of the lifting and falling rail coseismic deformation fields are mainly uplifted, 7.51cm, and 7.91cm respectively. At the same time, the PSO-BiLSTM-Attention population casualty model is proposed, and 149 people are predicted to die, which is 2 people less than the actual 151 people, indicating that the model can well estimate the post-disaster death toll and can be used for the post-disaster death population assessment.

Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

3D city models are an important cornerstone in the development of digital twin cities, allowing for various analyses and simulations. CityGML, as an open standard for 3D city models, emphasizes five main aspects: scale or level of detail (LoD), semantics, geometry, topology, and appearance. One of the important elements in CityGML 3D city models is representation of vegetation. Vegetation in CityGML, especially individual trees, is still limited in terms of detail and resolution, reducing its effectiveness for specific applications. This paper proposes a graph-based conceptual model for individual trees that conforms to the CityGML v2.0 specification. The model refers to the morphological structure of a tree consisting of roots, trunk, and crown (including branches, twigs, and leaves), and considers the five main aspects of CityGML. By enhancing semantic information and geometric details, the model aims to provide an information-rich and realistic representation of individual trees in a 3D city environment. The 3D tree model created based on this conceptual model may be applicable in the development of digital twin urban forests and virtual forest simulations that utilize immersive technologies such as the metaverse. This research opens up new opportunities in the development of solitary vegetation objects through CityGML ADE by including more detailed semantic and topological information.

The Deep Earth System Data Lab (DeepESDL) provides an AI-ready, collaborative environment for researchers aiming to study the Earth's complex dynamics using various datasets and empirical approaches. Recently opened to Early Adopters, it builds on projects like CAB-LAB and ESDL, utilizing well-established Python and Julia technology stacks. DeepESDL offers programmatic access to extensive analysis-ready data cubes and computational resources, enabling researchers to focus on analysis without extensive preparations. Scientists can use persistently available data cubes or generate user-tailored cubes from own data or publicly available datasets. The goal is to streamline data processing through empirical or AI methods within high-dimensional Earth Observation workflows. DeepESDL addresses the complete research cycle, from discovery of earth data to powerful analyses, collaborative scientific research, advanced data visualisation and publication of results, promoting FAIR and Open Science. Apart from serving as a research environment, DeepESDL showcases scientific use cases and supports educational purposes through capacity building, academic programs, and Open Science initiatives. This paper presents an overview of DeepESDL.

The establishment of a comprehensive survey, monitoring and evaluation system for natural resources is important for determining the background of China's natural resources, fulfilling the "two unification" responsibilities of the Ministry of Natural Resources, and implementing the construction of an ecological civilization. According to the task requirements of the unified survey and monitoring of natural resources, we summarize the progress of natural resource management systems, monitoring systems, technical development and implementation of major projects, propose upgrading the "3rd National Land Resource Survey" to "the 1st Comprehensive Natural Resource Survey", and explore the construction of natural resource survey systems. Combined with the pilot project of comprehensive investigation of natural resources with multiple elements, the next major work for the comprehensive investigation of natural resources is to accelerate the formulation of standards and specifications, carry out application demonstrations, and establish and improve the working mechanism and investigation team.

The high number of vertices and faces present in a digital twin of a large scene poses a challenge when executing large-scale simulations that require the data to be rendered multiple times with slight changes such as the light conditions in a day-night cycle during a long period of time (weeks and even months). Adding urban morphology such as tree planting introduces a new layer of complexity due to the requirement for evaluating different tree configurations and their effect on the local environment in the context of urban heat islands. Using advanced rendering techniques such as splitting a single buffer into sub-regions with inplace updates as well as buffer orphaning multiple methodologies are evaluated.

Population aging is a global trend and a world-wide challenge to many governments. The high-quality life of the elderly cannot be separated from the support of basic services and facilities. Therefore, it is necessary to conduct analysis and research on the accessibility of urban community services and facilities. It is worth noting that the research in this article is based on master thesis (Bai, 2013). Drawing on the data, methods, and conclusions from the master's thesis, in this paper, an approach is designed for measuring spatial accessibility to services/facilities by a spatially dispersed ageing population in urban areas. The local government area (LGA) of Monash in the Melbourne metropolitan area has been selected the case study area. This study combines GIS-based spatial analytical procedures and two-step floating catchment area (2SFCA) method, in conjunction with mesh block level 2011 population data, service/facility data and transportation network data, to measure and map spatial variations in potential accessibility to services and facilities deemed essential to the aged population. Service facilities considered in the study include bus stops, train stations, bank facilities, shopping centres, post offices, churches, parks, public libraries, community centres, pharmacies, GP clinics and hospitals in Monash LGA. The research results can be used to optimize the spatial planning and layout of public facilities for ageing population.

Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

The process of urbanisation has a significant impact on climate change, with projections indicating that over 70% of the global population will live in cities by 2050. The challenges associated with climate change impacts on urban areas include urban heat islands, unsustainable water management, floods and air pollution. This study explores the potential of Nature-Based Solutions (NBS) and renewable energy in Torino, Italy, with a particular focus on the exploitation of rooftops for solar panels and green roofs. The data from 159 buildings in San Salvario was collected using aerial photogrammetry. The solar potential was analysed with ArcGIS, taking into account both geographic and building-specific factors. Buildings that were unsuitable for solar panels were identified for green roofs. This research supports the United Nations Sustainable Development Goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 13 (Climate Action), and 15 (Life on Land). The annual solar potential was estimated at 100,300 kWh, with 2,450 m2 of suitable roof area for green roofs. The study demonstrates the feasibility of using rooftops to increase urban sustainability, thereby enhancing the quality of life for residents. The integration of NBS and solar energy can facilitate the creation of more sustainable and resilient urban environments. The study recommends the implementation of spatial-enabled urban planning as a means of overcoming barriers and promoting the development of green, energy-efficient communities.

Climate change has an immense impact on many fields, especially in the agricultural sector the reduction in crop yield, pre-maturation of crops, and water scarcity that may lead to drought, and scarcity of food in India. Agriculture plays a significant role in India where approximately 60% of the population works in the agricultural sector, contributing 20.2% to India’s Gross Domestic Product (GDP). India has one of the largest populations 1.2 billion in the whole world (Census 2011), Precision Agriculture (PA) techniques with the utilization of Unmanned Ariel Vehicles (UAV), equipped with multi-sensor along with satellite imagery providing more spectral and temporal resolution. The primary objective of this research is to create a model within the Google Earth Engine (GEE) framework, a WebGIS platform that enables its users to access analyses and visualize geospatial data for a wide range of applications. This model will enable researchers to calculate several indices for crop health and stress assessment mentioned in this research in their respective Areas of Interest (AOI) whether using either a UAV or satellite dataset. These indices help in evaluating water stress, disease detection, biomass and nitrogen estimation, and soil moisture estimation in crops, various tools and techniques will be employed for data processing, comparing, and analysis to acquire the optimal precision, additionally, ground truthing measures were also utilized to validate the accuracy of the outcomes. This research will help in supporting PA practices and promoting sustainable development in the agricultural sector.

This paper is a step forward towards a construction of 4D cell-complex models implemented using a topological data structure, dual half-edge. This allows for explicit representation of spatial relations among objects, such as adjacency relationship, coincidence or order of construction elements, e.g. vertices and edges. A method based on extrusion is proposed. An original 2D model is extruded to 3D, where adjacent cells sharing the same face are directly linked. Then extrusion to 4D is performed. In this case individual 4D cells sharing the same 3D cell are linked together. A model is kept valid at any stage of the construction process and links among cells are automatically provided by the construction operators. Implementation is based on the 4D data structure, which is used at each stage of construction, even in intermediate models of lower dimensionality.

Modern drone platforms provide the opportunity to capture large quantities of high quality, geotagged aerial imagery quickly and over large distances, at a relatively low cost. As organisations increasingly turn to digital twin technology to manage their operations, rich, photorealistic three dimensional (3D) models of their assets become increasingly important. This paper describes how cloud computing, and container based serverless compute in particular, can be used together with open source software to generate a 3D model, converted to 3D tiles for optimised transmission, from images of large outdoor assets captured by drone. This approach offers a flexible and cost effective way to processing drone imagery into 3D models whilst allowing you to maintain ownership and control of your data.

Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

In recent years the digital visualisation techniques, including 3D digital models of indoor spaces, saw an exponential increase in their applications, both in the fields of Architecture, Engineering and Construction (AEC) and of Cultural Heritage (CH). This growth is mainly due to the increasing affordability of tools for the acquisition and elaboration of three-dimensional digital survey/data management and to their increasing ‘user friendliness’.The research proposes a further step on research started some years ago by our group about the exploration of indoor mapping for the creation of the 3D virtual museum of our Institution, starting with the realisation of a virtual model of the university’s spaces (departments and halls). At first, the virtual museum will host the Curioni Digital Collection, which consist of circa 140 wooden models of building details, bridges, tunnels, and vaults designed by prof. G. Curioni during the second half of the XIX century as teaching aids for the Construction and Structural Design classes. A comparison between two main tools to support management and diffusion of geometrical, alphanumerical, and topological data will be given (Matterport and Unity). The parallel approach considering two tools is to provide a digital twin of a real collection placed in a real space, and, on the other hand, to use a real historical university space, virtualized, to host a virtual collection.

This paper addresses the pervasive challenge of localization within indoor navigation systems. Many existing systems grapple with this issue, relying on marker-based methods, Bluetooth beacon localization, and image processing. However, these solutions often necessitate the placement of predetermined markers within the environment, presenting limitations such as marker deterioration over time, spatial constraints, and associated costs. In response, we propose a novel point cloud registration-based approach. This method involves the creation of a comprehensive global point cloud representing the entire indoor environment, effectively serving as a three-dimensional virtual map. Concurrently, a mixed-reality device is utilized to generate a local point cloud specific to the device’s current location within the building. By aligning and registering the local cloud with the global counterpart, the device’s precise location within the virtual map can be determined. This approach enables seamless navigation within indoor environments without the reliance on physical markers, offering a versatile and cost-effective solution to the challenge of localization in indoor navigation systems. Finally, an error analysis was also done to test localization accuracy for different illumination conditions, demonstrating the effectiveness of this method.

The integration of low-cost thermal sensors with Apple Smart Devices supports the generation of 3D point clouds that include temperature indicators. This generates a new perspective for the study of buildings, allowing for fast and reliable examination of physical building structures. To the best of our knowledge, this study is the first to demonstrate the use of affordable sensors for 3D thermal point cloud generation. The study involved capturing data from the LiDAR and thermal sensors, followed by an extrinsic calibration process to align the datasets. Subsequently, the point cloud was segmented based on different acquisition poses of the device and finally, the thermal data was projected onto the 3D model, integrating temperature information with spatial coordinates. Our results demonstrate the effectiveness of the approach for three-dimensional point cloud generation in indoor environments, highlighting significant thermal variations and enabling thermal mapping of building structures. Furthermore, our findings underscore the feasibility of employing low-cost sensors for generating detailed thermal models, opening possibilities for widespread adoption in various building analysis applications. This approach provides a comprehensive and cost-effective solution for building monitoring, democratizing access to advanced evaluation tools.

Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

Accurate information on cropland changes is critical for monitoring arable land minimum, ensuring national food security, and grasping the situation of agricultural production and supply. The change detection using remote sensing images is one of the main methods for quickly extracting cropland changes. However, existing methods were highly susceptible to seasonal differences due to the high heterogeneity of cultivated land. In this study, an integrated framework was proposed to perform change detection by incorporating phenological patterns and multi-source remote sensing images.There were two improvements in this proposed cropland change detection method: 1) the multi-source remote sensing images were utilized to fill the missing data within a time-series image stack by considering phenological patterns of cropland and 2) the Seq2Seq model considering phenological patterns was developed to extract changes in cropland directly. Compared to the traditional change detection methods, the proposed strategy was able to detect change process. Experiments demonstrated that the proposed method can significantly improved change detection accuracies, given a limited number of labeled samples.

The design of urban public spaces (UPSs) aims to facilitate users’ physical and mental well-being by facilitating dynamic and stationary activities. The success of a UPS is often measured by its users’ engagement and experiences. However, UPSs frequently exhibit varying usage patterns, with some areas being heavily frequented and others neglected. This discrepancy is often due to users’ perceptions of safety, comfort, and liveliness. Failure to anticipate and address these aspects during the design phase of UPSs can result in dysfunctional urban spaces that fail to meet users’ needs. People experience UPSs primarily through their senses, with vision being the predominant sense and key to spatial cognition in UPSs. Therefore, adequate visibility of the spatial configuration must be integral to UPS design as a design goal. As a result of recent advances in Immersive Virtual Reality (IVR) end-users can participate in, interact and experience the possible UPS design scenarios, however, most IVR applications do not provide real-time feedback for design goals such as visibility. Established methods exist for analyzing visibility in UPS. However, these techniques are not usually integrated into VR apps to assess the effects of the designs. This paper addresses the gap for the lack of real-time visibility feedback within IVR applications for UPS design and its technical challenge. Building on a previous IVR application, CoHeSIVE, which was developed for UPS design, we integrated a ray-casting method and Unity’s built-in callbacks for dynamic visibility assessment of the features within the user’s field of view. The visibility assessment is then presented on a dashboard within the IVR app. This study serves as a cornerstone for future real-time feedback mechanisms of visibility assessment within IVR applications for urban design, facilitating informed design decisions.

In view of the limitations of the existing methods for personal massive photos management and browsing, a method of using geodatabase to manage and browse massive personal photos is proposed. Firstly, vector geodatabase is created and defined based on geographic information technology. Then, attribute information including the file name, photo date and time, photo point longitude, photo point latitude, location name, photo storage path and so on of each photo in the file system are extracted. Finally, based on the longitude and latitude of the photo point, the geospatial location data of each photo is generated, and written into the vector geodatabase together with the attribute information. Based on the storage path of photos in the file system, the links between the geospatial database and photos are established. Furtherly realize the attribute information and geographical space location management of massive photos, as well as the browsing and viewing of photos. Take personal photos as an example to introduce the data processing and verify the practicability of the method. The result shows that this method uses the idea of geodatabase to manage massive personal photos, which is conducive to reflect the geographical spatial distribution and density characteristics of the photos, and can use the time axis to connect the track of personal history, which is convenient for browsing and analyzing the experience.

Digital Twins recently, are a much regarded research topic in geospatial engineering, offering great potential for optimization of a variety of interrelated processes in urban environments. A major component of a Digital Twin use case is real-time sensor data input and analysis, which is used as foundation for simulations that lead to decision-support for authorities or even automatic response into the physical environment. The use of game engines as platform for urban Digital Twin use cases is promising due to superior visualization and animation capabilities, that facilitate diverse monitoring use cases for highly dynamic sensor data inputs. This contribution evaluates the fit for purpose of the Unreal Engine 5 game engine for digital twin use cases, by conducting a case study on urban traffic monitoring in the city of Liverpool Australia. The case study proved, that the necessary connections of static data provided via a geodatabase and dynamic real-time data from public portals can be integrated into the engine to establish an interactive Digital Twin use cases for evaluating traffic user interactions.

The Metaverse is rapidly advancing across cultural, professional, and service sectors, enhancing data compatibility and interaction. Its architectural design supports both visual interaction and information layers, characterising an ideal platform for the promotion of Digital Heritage. The digitisation of Cultural Heritage and the creation of Digital Repositories find complementarity in the Metaverse, which enables social interactions between Digital Heritage contents and virtual communities. This integration is pivotal for developing Learning Environments, where accessible virtual products can be combined with original digital storytelling and narratives.The paper presents methods and results from an Erasmus+ international school, which focused on using digital communication and 3D modelling tools to create immersive narratives on Mediterranean architectural modernity. Students from various Cultural Heritage disciplines designed virtual exhibitions within the Metaverse platform Spatial and open repository materials. The research concentrated on the application of Metaverse platforms for their spatial design and educational utility within the Heritage sector. The activities included developing digital narratives and interactive exhibitions enriched with multimedia data from digital repositories. The study discusses the limitations of customising visual materials, the importance of repository connections, user experience design, and the potential for future developments in Metaverse applications for Cultural Heritage learning environments and storytelling.

Mangrove and saltmarsh vegetation are two important communities in the wetland ecosystem that require continuous monitoring considering their ecological threats and status. Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. Although there is some research about eCognition-based mapping, a unique rule set is important to monitor this community. Considering this research gap, a unique threshold-based ruleset has been generated in the Random Forest Model environment. A range of vegetation indices, individual bands of Sentinel 2 and Digital Elevation Model (DEM) data were tested in the feature selection method to find the best features for a Random Forest (RF) model. Top three variables were selected and those are (a) reNDVI_A, (b) VIRE_A, and (c) SWIR1 which gave 98.37% accuracy for the test data. A similar trend was found for the other three sites when they were compared with observed data. For site 1, Mangrove was 84.73%, Saltmarsh was 60.19%, and Mixed was 70.12% accurate. A similar trend was found for other three sites with overall accuracy 87.74 % for site-2, 66.23% for site 3, and 72.98% for site 4. A unique ruleset for wetland extent estimation will help to map a wide range of areas with a very minimum level of fieldwork. It will also reduce the cost of mapping done by visual observation, measurement and extensive fieldwork. The results of this work will provide the necessary insight and motivation for ecologists, and environmentalists.

As the capabilities of geographic information services increase, web geographic information services represented by internet mapping have become increasingly popular, widely used in various location-based services. It is precisely because of the surge in demand that higher requirements have been put forward for the updating and quality control of internet map data. The quality inspection of geographic vector data is relatively well-developed, with many inspection rules and automated processes, but there is a clear deficiency in the quality inspection of map tiles, which mainly relies on manual visual inspection, restricting the rapid updating and release of map tiles. To solve this problem, this paper proposes an automated map tile inspection method. First, web crawling technology is used to capture map tiles, and to avoid the problem of incomplete display of annotations across tiles, adjacent tiles are all captured and merged into one image; second, OCR technology is used for image text detection and text recognition, and finally, the newly added recognition results are stored in the database and compared and confirmed with the results in the database. The experiment proves that the method proposed in this paper is feasible and may greatly improve work efficiency, especially as the updating speed of online maps accelerates, an automated tile quality inspection method can greatly improve the updating speed.

This study aims to explore the landscape pattern and its driving forces of Linpan in western Sichuan, aiming to gain a deeper understanding of the spatial distribution of this unique rural settlement form. The landscape indices and redundancy analysis method were employed to investigate the landscape pattern and driving forces of Linpan in the western Sichuan region. By extracting the spatial information of Linpan in the study area, quantitative analysis of their landscape pattern was conducted using landscape indices. The results reveal certain characteristics of the landscape pattern of Linpan in the study area, where Longquanyi exhibits the highest degree of landscape fragmentation, with the highest patch density and smaller average patch size, yet the highest landscape diversity, while Wenjiang shows the lowest diversity, and Xinjin exhibits a more dispersed distribution of patches, and Dujiangyan has the highest number of patches. Using redundancy analysis, this study reveals the influence of natural and social driving forces on the landscape pattern of Linpan in Western Sichuan. The results indicate that urbanization rate, patch density, mean patch size, and average elevation are significant factors influencing the distribution and morphology of Linpan. These factors affect the spatial layout and connectivity of Linpan, driving changes in the land use structure, and profoundly impacting the landscape pattern of Linpan. To achieve the ecological sustainability, it is essential to comprehensively consider the impacts of both natural and human factors, scientifically plan and manage Linpan, and promote their harmonious coexistence with the surrounding environment.

The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Predicting infections resulting from epidemiological contagions involves a complex therapeutic endeavor that necessitates the consideration of various factors within the available data. COVID-19, a globally recognized highly dangerous disease, presents an opportunity to effectively understand its patterns and spatial determinants to combat similar ailments. This study seeks to employ a deep neural network and several machine learning techniques to identify the crucial factors influencing COVID-19 infections and to forecast the spatial-temporal spread of the disease. Previous research has focused on predicting outcomes using a limited set of 8 patient variables. To contribute to this field, the current investigation analyzes a dataset comprising 47,029 records of infected individuals who sought medical attention, with 18,433 of them testing positive for the coronavirus and requiring hospitalization. The study explores the prediction of illness and hospitalization needs based on geographical locations by utilizing machine learning algorithms such as Random Forest, Naive Bayes, Decision Tree, and K-Nearest Neighbor. The machine learning process commences with input data (comprising the 47,029 records), enabling the machine to identify patterns within the dataset and subsequently make informed decisions based on these patterns and insights gained. The primary objective is to allow the machine to autonomously learn and refine its predictions without human intervention. Applying these algorithms to COVID-19 data for spatial-temporal prediction revealed that the Random Forest algorithm achieved the highest accuracy of 0.84, while the Simple Bayes algorithm exhibited the lowest accuracy at 0.50. Additionally, the study compared random forest, decision tree, simple bayes, and K-nearest neighbor algorithms to predict the severity of road accidents.

The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.

As an important energy in modern society, the stable operation of electricity is the guarantee of social and economic operation, and the loss caused by the damage of power facilities is immeasurable, so the regular monitoring of power facilities is indispensable. UAVs, with flexible flight mode, have played an important role in power inspection. With the development of UAV docks, UAV inspection has entered a new stage. However, due to the fixed location of the docks and limited coverage, how to achieve the optimal deployment of the docks with the lowest cost and full coverage of power facilities through reasonable deployment is one of the important issues that need to be solved at this stage of dock construction. To solve this problem, this paper proposes an energy-driven adaptive optimization method for dock location by combining geospatial analysis with UAV performance index. The experiment results show that our method can achieve an efficient, reasonable and feasible optimal location for UAV dock deployment.

China has launched a national 3D mapping program to build 3D Realistic geospatial Landscape Model (3dRGLm) over the whole country, covering both cities and countryside. The 3dRGLM is a digital description and representation of the real 3D geospatial spaces, and is defined as a new generation geospatial data product with 3D structures, realistic scenes, and geospatial entities. Such a national or local 3dRGLm will set up a digital 3D realistic geospatial space which can facilitate the vital connection with the real geospatial space, and will serve as a new generation of geospatial information infrastructure for our society. With the acquisition of complex 3D spatial data continues to advance, managing and utilizing the massive volumes of 3D data to provide spatiotemporal empowerment applications has become a significant technological challenge. This paper analyzes the technical characteristics of 3d realistic geospatial landscape model, and then proposes the main content and basic structure of a 3d realistic geospatial landscape model database, as well as the spatiotemporal service empowerment methods based on the database. Finally, a case analysis is provided to serve as a reference for the construction and application of 3d realistic geospatial landscape.

In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

During the execution phase of construction projects, quality defects arise due to various factors such as human intervention, technical aspects, logistical issues and environmental influences. Early detection of these defects is imperative to minimize their impact and prevent possible delays, increased costs and safety hazards. This paper explores the effectiveness of indoor mobile laser scanners for geometric quality control, adhering to established European technical norms and Spanish standards. Our developed approach presents a simple method for rapid assessment, capable of identifying vertical deviations in columns and walls, axis deviation in columns, flatness deviation in horizontal planes and level deviation using LIDAR data from Mobile Mapping Systems. This methodology has been validated through a real-world case study, demonstrating its practical applicability.

Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.

Enriching geospatial data, particularly through address geocoding, is fundamental to many geospatial analytical solutions, facilitating resource allocation, accessibility assessment, and urban development planning. This paper explores the address geocoding approaches from traditional GIS-based methods to advanced data-driven solutions, emphasising their accuracy, efficiency, and complementarity. Geospatial data enrichment challenges are explored using the SemTUI framework and QGIS platform. SemTUI employs a data-driven approach that facilitates interactive semantic enrichment and address reconciliation. QGIS is utilised for address geocoding, using the MMQGIS plugin with two geocoding web services provided by OSM and Google. The comparison between SemTUI and QGIS workflows highlights their complementary strengths. SemTUI offers intuitive interfaces and an automated solution, while QGIS provides advanced geospatial visualisation and analysis capabilities. Practical applications in supply-demand analysis of social amenities and walkability index calculation demonstrate the significance of geocoding in urban planning. Further research efforts are focused on enhancing the integration between SemTUI and QGIS to improve the precision and performance of address geocoding and further spatial data analysis.

This paper explores the integration of water sensitive urban design within public space planning using integrated 3D digital technologies. In traditional design processes, drainage and public space design often utilise distinct software and are led by different professionals, which creates significant communication barriers.Although civil and hydraulic engineers have made significant strides in integrating hydrology analysis with multi-objective space design, the collaboration of conventional 2D hydrology with 3D landscape design still presents challenges. To address these challenges, this study proposes a novel approach that integrates drainage analysis and design within a 3D design model. This methodology is specifically designed to aid landscape and urban designers in incorporating stormwater management and drainage systems early in the design process. The approach involves three key steps: (1) identifying critical factors that impact, or are impacted by, drainage design; (2) translating these factors into specific spatial parameters; and (3) developing a workflow to integrate these parameters. This integration not only streamlines the design process, making it more cohesive and efficient but also mitigates potential conflicts between drainage requirements and spatial design, thereby reducing unnecessary revisions. Ultimately, this approach enhances the incorporation of essential drainage considerations into urban and landscape planning, leading to more sustainable and functional public spaces.

With the development of society, spatial artificial intelligence (spatial AI) research is gradually able to play a greater role. However, spatial AI has problems such as data alignment, poor interpretability, and cross domain learning. Therefore, this paper proposes an innovative GeoSOT-3D grid modeling framework for spatial AI research, which enhances the application capabilities of spatial AI. Grid modeling will be able to run through the upstream and downstream of spatial AI research, providing encoding calculations and spatial neighborhood embedding matrices for spatial data. This paper also uses task examples to demonstrate how to effectively organize and index spatial data using GeoSOT-3D grids and conduct spatial AI research. The use of GeoSOT-3D grids for spatial AI analysis has enormous potential and broad application prospects, which will help promote the further development and application of spatial AI.

Unrecognised Historical places, which are locations with history but lacking official recognition, particularly prevalent in Southeast Asia, often face a dearth of accessible historical information. To tackle this challenge, there is an urgent need to commence the collection and documentation of historical data concerning Unrecognised Historical places. This initiative aims to prevent the loss of history, given that such sites are frequently subjected to rapid development and commercialization. To date, City Geography Markup Language (CityGML), an Open Geospatial Consortium (OGC) standard 3D data format, has been developed for presenting city models along with semantic data and is often used in the heritage domain for preservation. It aids in digital infrastructure development and the conservation of urban elements. Furthermore, it provides a mechanism to extend the standard data model to meet the requirements of various purposes. While several CityGML extensions tailored to the documentation of Cultural Heritage already exist, there is currently no extension specifically designed to address the requirements of Unrecognised Historical places. The aim of this paper is to identify a suitable approach for developing a new standardised data model that integrates geometric and semantic information, which is demonstrated for the modelling of Telukbetung, an old city in Bandar Lampung through time.

LULC (Land Use and Land Cover) involves classifying and describing different land types and their usage. Using satellite imagery for LULC mapping is increasing in remote sensing. This study focuses on Jammu district in India, situated between mountain ranges from north and south make it eco-sensitive zone. Expanding of human activity and loss of natural resources make it vulnerable if mismanaged. Study of LULC is important because of it and this study deals with efficiency and accuracy of various machine learning classifiers for LULC. This study uses machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), and Classification and Regression Trees (CART) - for the task, leveraging Google Earth Engine (GEE). Sentinel-2 satellite data from January 1 to March 31, 2023, with specific spectral bands (B4, B3, B2 & B8, B4, B3), were used for image preprocessing. Six classes: built-up, water, agricultural land, fallow land, forest, and barren land, each with 100 sample points, were used for classification. After training, the classifiers' accuracy was evaluated using Overall Accuracy and Kappa Coefficient. The results showed RF as the top performer with an overall accuracy of 99.36% and a Kappa coefficient of 99.11%, followed by SVM, GTB, and CART. This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.

Lidar odometry and mapping has emerged as fundamental technology in various fields, including accurate positioning and mapping, autonomous vehicles, robotics, and environmental monitoring. The ability to create accurate maps and calculate trajectories is essential for localization and navigation in complex environments, particularly in scenarios where GNSS signal are unavailable. In this context, we propose a novel method for simultaneous localization and mapping (SLAM) specifically designed for GNSS-denied environments and without the need for high-accuracy ranging or inertial measurement units (IMUs). Our approach aims to address the challenges posed by GNSS-denied environments and the cost constraints associated with using multiple sensors. By using only lidar sensors, specifically the Velodyne VLP 16 sensor, we offer a cost-effective solution. Our method utilizes a modified version of the Iterative Closest Point (ICP) algorithm to handle consecutive lidar scans seamlessly. In addition to our method, we conducted thorough accuracy assessments on the created map. The reason behind extensive accuracy evaluation is to ensure the quality of registration. By comparing alignment between the features of different frames within the map and determining the error between the registered features and the original, we gain a more comprehensive understanding of registration quality.

The utilization of various UAVs such as facility inspection and delivery is increasing. Ensuring safety in carrying out various UAV missions is an important issue related to human and property damage. Safe operation of UAVs flying autonomously along pre-located routes requires safe route planning to prevent collisions between aircraft in consideration of ground and air obstacles. Pass flight is also essential to minimize damage in the event of a crash. This paper deals with the optimal UAV 2D routing plan considering ground compatibility for flight safety. High ground compatibility here means that the damage caused by the crash is less than when it is not. In this paper, we first defined dynamic and static obstacles. Next, we derived ground compatibility considering dynamic and static obstacles. We used estimation algorithms and land use data to calculate ground compatibility values for flight safety, and through experiments, we calculated an optimal 2DUAV route plan for flight safety.