Search Results (694)

The integration of advanced terrestrial laser scanning (TLS) techniques into building information modelling (BIM) workflows has significantly enhanced the digitization and preservation of cultural heritage sites. In this context, crowdsourcing can operate as a complementary upstream mechanism for enriching heritage inventories, supporting participatory mapping, collecting preliminary condition observations, and prioritizing assets for subsequent expert-led TLS acquisition, HBIM modelling, and structural assessment. This paper explores the application of TLS within BIM for the structural assessment and the long-term monitoring of historic structures. The reviewed studies show how high-resolution TLS data can support the development and updating of geometry-informed structural models, enabling detailed structural assessment and effective management strategies. The reviewed case studies demonstrate the practical benefits of integrating TLS with BIM, such as improved accuracy in structural assessments, enhanced data processing capabilities, and the creation of comprehensive digital twins. These advancements facilitate better-informed decision-making and targeted preventive measures, contributing to the resilience and preservation of cultural heritage assets. The results underscore the contribution of TLS–BIM/HBIM workflows to condition-informed documentation, structural assessment, conservation planning, and long-term heritage management. This research was supported by the ERA4CH project, which aims to protect European cities’ cultural heritage from earthquake risks through innovative monitoring and management tools. Full article

The observed ratio of Wolf–Rayet (WR) star subtypes (e.g., WC/WN) in M31 is significantly higher than the predictions of standard stellar evolution models, yet this key diagnostic discrepancy remains unresolved. This is partly due to incomplete census and the challenge of resolving individual stars in compact clusters with ground-based telescopes. In this work, we report a young cluster with WR features with the (R.A., Dec) = (00:43:39.36, +41:10:08.7), discovered with LAMOST spectra. We use the high spatial resolution of the HST imaging data to resolve the cluster members, leading to the identification of four WR star candidates, four OB stars, and one yellow supergiant. Based on the current known WR sample in M31 observed in the HST F275W filter, about one-third of WRs are contaminated by other sources within a ${1.}^{″}1\times {1.}^{″}1$ aperture, while this fraction can reach up to two-thirds for the LAMOST fiber size of ${3.}^{″}3$. Applying the approach demonstrated in this work to a larger sample of crowded regions in M31 will yield a more complete census and allow for the measurement of a definitive WC/WN ratio, corrected for contamination. Whether this refined ratio increases or decreases the current tension with models remains to be seen, but it will provide the essential observational constraint needed to advance our understanding. Full article

User-generated content (UGC) offers large-scale, naturalistic data for examining tourism destination image. Focusing on the Historic Centre of Macao (HCM), a World Cultural Heritage site, this study collected 3781 tourist reviews from Rednote, Ctrip, Dianping, and TripAdvisor and developed a multi-model text-mining framework integrating TF-IDF, BERTopic, and RoBERTa. The results show that HCM’s online tourism image comprises four dimensions: perceptions of history, culture, and heritage value; perceptions of spatial landmarks and urban landscapes; modes of travel behavior and embodied experience; and emotional evaluation and tourism experience quality. The TF-IDF results indicate that terms such as architecture, history, Portuguese, church, Ruins of St. Paul’s, and Senado Square constitute the core elements of tourists’ cognitive image. BERTopic further identified 18 valid topics and revealed three interrelated semantic clusters: heritage-space cognition, landmark and district experiences, and integrated tourism experiences. The RoBERTa-based sentiment analysis shows that tourists’ overall evaluations are dominated by positive emotions, while crowding, high visitor density, and gaps between expectations and actual experiences remain important sources of negative evaluations. This study demonstrates the applicability of the proposed framework in the Historic Centre of Macao and provides a methodological reference for tourism image research in other cultural heritage destinations. Full article

For gas leakage monitoring in obstacle environments such as oil and gas stations, the layout of fixed sensors directly affects the validity of monitoring data and the accuracy of subsequent leakage source localization. To achieve effective coverage of high-risk areas with a limited number of sensors and reduce deployment costs, this paper proposes a multi-objective optimization method for sensor layout based on the non-dominated sorting genetic algorithm-II (NSGA-II). Based on multi-scenario computational fluid dynamics simulation data, the peak concentration, hazardous concentration duration, and leakage probability at each monitoring point are extracted as risk characteristic indicators. The NSGA-II analytic hierarchy process is employed to determine the weight of each indicator, and a comprehensive risk classification model for the monitored area is established. This is adopted for solution seeking. Through non-dominated sorting and crowding distance calculation, the Pareto optimal front is searched in the solution space. The optimized layout scheme is applied to the leakage source term estimation based on particle filter, and the performance of different layout schemes is compared and analyzed with the source localization error as the evaluation index. Case studies show that the sensor layout optimized by the non-dominated sorting genetic algorithm achieves effective coverage of high-risk areas. With the same number of sensors, its high-risk area coverage rate outperforms that of the multi-objective particle swarm optimization algorithm (MPSOA). Following the application of the optimized layout, the localization accuracy of leakage source term estimation is significantly improved. Compared with the traditional grid and circular layouts, the source localization error is reduced by approximately 44%. Compared with the layouts optimized by the MPSOA and genetic algorithm (GA), the error is decreased by 30.4% and 33.3%, respectively. The proposed sensor layout optimization method based on the NSGA-II can effectively balance monitoring coverage and economic cost, and significantly improve the localization accuracy of gas leakage sources. This study provides a theoretical basis and technical support for the optimal deployment of fixed gas sensor networks in complex scenarios. Full article

This work develops an effective platform to enhance the data collection process for researchers. The proposed platform connects researchers who need specific datasets with data curators, employing blockchain and artificial intelligence (AI) techniques. This system includes individual researcher and worker dashboards, an AI-integrated validation process, and a blockchain-based automatic payment mechanism. The AI-driven verification system evaluates the quality and correctness of a sample fraction of the collected data to ensure reliability before approving the full dataset. Payment transactions are handled using blockchain technology, which provides a transparent, secure, and tamper-resistant system. Workers are paid based on verified accuracy, and funds are transferred directly to MetaMask wallets on the blockchain. The prototype demonstrates how AI-based data validation and blockchain-based payments can be combined to support a more trustworthy and automated data collection process. This unique concept provides workers with an efficient and secure way to get paid while ensuring high-quality research data. Experimental results show that the proposed platform achieved validation accuracies of 94.5% and 95.8% with 10% and 20% sampling, respectively, during AI-based dataset verification. The Sepolia blockchain transaction was completed in approximately 12 s, with a transaction fee of 0.00042 ETH for transferring 0.001 ETH. The system handled 1000 users without lag and achieved an 88% satisfaction rate based on survey responses. Full article

Facing the energy use rights trading system with an emphasis on source control, will enterprises opt for traditional fossil energy technology innovation or new and renewable energy technology innovation, and will they choose substantive or strategic innovation activities? Will the energy use rights trading system lead to competitive crowding-out effects or innovation compensation effects for corporate energy technology innovation? Taking the “Pilot Program for the Paid Use and Trading of Energy-Consumption Rights” as a quasi-natural experiment, this paper, based on the energy technology patent data of listed companies, conducts a theoretical analysis and a series of empirical tests to examine the impact of the energy use rights trading system on corporate energy technology innovation. The results indicate the following: Firstly, the energy use rights trading system significantly enhances corporate energy technology innovation, primarily inducing traditional fossil energy conservation innovations, while the driving effect on new and renewable energy technology innovation is not pronounced. Secondly, the system promotes corporate energy technology innovation by strengthening financing channels such as government subsidies. Thirdly, the energy technology innovation induced by this system stems from the competitive crowding-out effect on R&D resources, rather than the innovation compensation effect. Fourthly, the driving effect of the tradable energy permit system exhibits distinct path-dependent characteristics, which is manifested in the fact that the system exerts a more significant driving effect on substantive energy technology innovation activities, the most prominent impact being on substantive innovation in traditional fossil energy. Finally, enterprises across pilot regions respond differently to the system, with Zhejiang and Fujian Provinces showing more significant driving effects in energy technology innovation. The system mainly stimulates substantive innovation activities in non-state-owned, large-scale, and low-energy-consuming enterprises. This study contributes to the refinement of the energy use rights trading system and supports innovation-driven energy transition. Full article

Strelitzia reginae (Bird of Paradise) is a South African ornamental plant widely cultivated in tropical and subtropical regions. While naturalized populations have been reported outside its native range, no such cases have been documented for Australia. Drawing on the first known occurrence of a bird-dispersed S. reginae growing epiphytically in a Moreton Bay fig (Ficus macrophylla) in Manly, New South Wales, this paper examines the nature of birds pollinating and dispersing the plant in an alien ecological setting. Crowd-sourced data from social media and citizen science platforms identified 14 bird species visiting S. reginae flowers, with various honeyeaters acting as effective pollinators. Seven bird species were observed feeding on or transporting seeds, suggesting viable mechanisms of dispersal. The study highlights the ecological significance of spontaneous establishment, the role of native birds in facilitating reproduction and dispersal, and the value of leveraging citizen-sourced observations to detect emerging naturalized species. Full article

Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work, to our knowledge, has systematically investigated continual learning at the scale of thousands of fine-grained medicinal plant species from crowdsourced images, nor how retraining frequency affects the cost–performance trade-off in an IoT model-lifecycle setting. We evaluate three continual learning strategies, naïve fine-tuning, experience replay, and Learning without Forgetting, under periodic retraining schedules (updating every K increments), tested on 2719 species (≥25 images each) from the Viet Medi Species 2026 dataset (310,647 images; 4799 species total). All three strategies exhibit negative forgetting (performance improvement rather than degradation) in the instance-incremental setting, with naïve fine-tuning and LwF showing the strongest gains. Periodic retraining with $K=2$ halves retraining operations while maintaining comparable performance. A baseline MobileNetV2 model achieves 54.07% top-10 accuracy across 2719 species and has been deployed via TensorFlow Lite (FP16, ∼11.5 MB) in the Med Herb Lens Android application. In this regime, naïve fine-tuning offers a favourable cost–performance trade-off and is a reasonable default for instance-incremental agricultural IoT deployments. Full article

Private investment is critical for post-conflict economic recovery, yet evidence on how specific fiscal policy instruments, such as taxation, borrowing composition, and expenditure types, affect domestic and foreign investment in a post-conflict set-up remains limited. This study examines whether disaggregated fiscal policies are associated with crowding in or out private investment in Rwanda, a post-conflict economy characterized by constrained fiscal space, shallow credit markets, and evolving institutions. Using a Vector Error Correction Model (VECM), on quarterly data spanning 1996 Q1–2024 Q4, the analysis captures long- and short-run dynamics between disaggregated fiscal variables, institutional quality, and private investment. The results indicate that direct taxes and domestically financed debt are negatively associated with both domestic and foreign private investment. Externally financed capital spending, on the other hand, is associated with a crowding-in effect, stimulating both local and foreign investment. Lagged measures of institutional quality also enhance investment outcomes, highlighting the conditional role of government in shaping fiscal transmission. These findings demonstrate that fiscal effects are instrument-specific, depending on funding sources and composition, and mediated by institutional and macroeconomic conditions. By integrating disaggregated fiscal analysis with institutional context, this study provides empirically grounded insights for designing fiscal strategies that support private sector-led recovery and sustainable growth in post-conflict and resource-constrained economies. Full article

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article

In distributed Geographic Information Systems (GIS), preserving topological validity without sacrificing real-time interactivity under high-frequency concurrent editing of spatial polygons remains a persistent challenge. Recent distance-based heuristic methods suffer from scale-dependent bottlenecks and unreliable topology preservation, while more robust application-layer caching mechanisms still incur severe queuing latency under intense concurrency. To overcome these limitations, we propose Geo-CRDT, a geometry-aware distributed data structure that integrates spatial constraints directly into its underlying architecture. By dynamically isolating concurrent spatial entanglements into a strictly bounded local scope $\mathcal{S}$, the system deterministically resolves complex 2D conflicts via scalar projection, repairing the local topology in $O\left(\right|\mathcal{S}\left|\right)$ time. Rigorous simulations and a 15-participant real-world case study validate that Geo-CRDT sustains low-latency responsiveness and structural reliability under extreme concurrency, offering a robust foundation for large-scale crowdsourced spatial collaboration. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

Obtaining high-quality labeled data for supervised learning is costly, motivating the use of crowdsourcing, which distributes the annotation process across multiple workers with varying levels of expertise. A key challenge in crowdsourced data is annotation sparsity, as each worker labels only a limited subset of instances. This sparsity can amplify class imbalance, reduce supervision for minority classes, and bias standard cross-entropy-based models toward the majority classes. To address this problem, we propose a correlated chained Gaussian process framework trained on a focal-loss-based variational objective (CCGPFL). This probabilistic framework jointly models latent ground-truth and instance-dependent annotator reliability while accounting for correlations among annotators. In addition, the focal-weighted objective mitigates the imbalance induced by sparse annotations by assigning greater importance to harder examples during training. Experiments on synthetic, semi-synthetic, and fully real multi-annotator datasets show that CCGPFL achieves competitive and often superior performance relative to state-of-the-art learning-from-crowds baselines in terms of Overall Accuracy (OA) and Area Under the ROC Curve (AUC). Full article

To measure the emotional meaning of words, numerical sentiment scores are ascribed to them. However, within individual words these scores show variation: within and across annotation surveys, across linguistic contexts, and across semantic neighbors. While such variation could be set aside as undesirable noise, this study examines to what extent variation in lexical sentiment scores is in fact informative of the degree of the emotional ambiguity of words. Four different ways of estimating emotional polysemy and ambivalence are employed to analyze a set of 117 German words. Data from 16 sentiment dictionaries, an additional sentiment survey conducted for this study, automatically annotated contexts drawn from a contemporary German corpus, and pre-trained word embeddings were used for this purpose. These estimates are compared against subjectively rated ambivalence collected through crowdsourcing. It is shown that only variation within and across surveys robustly relates to subjective ambivalence. Context and neighborhood-based estimates, both of which are inherently sensitive to lexical frequency, cannot be shown to be related to ambivalence. This suggests, (i) that variation in lexical sentiment scores across dictionaries and annotators, but not across semantic neighbors and contexts, carries information about emotional meaning, and is hence valuable for cognitive-variationist research, and (ii) that speakers’ retrieval of positive and negative senses of words, in judging the degree of ambivalence, is not strongly affected by frequency, which is fundamental to NLP methods that build on distributional semantics. This implicitly challenges usage-based approaches to semantics that consider frequency as a predominant factor. Full article