Frontiers in Computational Spatial Intelligence
Frontiers in Computational Spatial Intelligence (FCSI)(ISSN P 3106-5252) (ISSN E 3106-5244) is an interdisciplinary, peer-reviewed journal dedicated to advancing the theoretical foundations, methodologies, and practical applications of spatial intelligence in computational systems. The journal serves as a platform for innovative research at the intersection of artificial intelligence, spatial computing, geoinformatics, cognitive science, and data analytics. It aims to explore how computational systems perceive, reason about, and interact with space—whether physical, abstract, or virtual.
Remote Sensing Insights into Neotectonics and Climate-Driven Hazards in the Hindu Kush-Himalayan Region
The Hindu Kush–Himalayan (HKH) region is a highly active tectonic belt where continuous uplift and erosion strongly influence river systems. This study investigates the neo-tectonic control and climate-driven hydrological response of the Luthkhow River basin in the Chitral Valley, northern Pakistan, using remote sensing and GIS-based morphometric analysis. Digital Elevation Models and satellite imagery were used to extract drainage networks, watershed boundaries, and lineaments, and key parameters including stream density, drainage density, flow direction, and basin segmentation were analyzed. The basin was divided into seven reaches to assess spatial variability, and a total of 2,663 stream segments were identified, with high drainage density concentrated near glacier-fed tributaries, indicating strong influence of snow and glacier melt. Rose diagram analysis reveals a dominant north–south river flow, while drainage and lineament orientations mainly follow NE–SW to E–W trends. The close correspondence between drainage patterns and lineament directions demonstrates strong structural control and active neo-tectonic deformation. The results confirm that basin evolution is primarily tectonically governed, with cryosphere forcing enhancing erosion and geomorphic response. This study highlights the effectiveness of remote sensing-based morphometric analysis for understanding neotectonic processes in high-mountain environments of the HKH region.
Remote Sensing and Machine Learning based Optimization of Erosion in Ghizer District-Hindukush Using RUSLE and Hypsometric Modeling
Soil erosion-induced land degradation is now considered one of the most severe challenges of the 21st century, with acute risks to soil fertility, food security, human well-being, and global ecosystems. The objective of this study is to conduct a quantitative mapping of soil loss within the Ghizer district of Pakistan. To estimate soil loss in the region, the Revised Universal Soil Loss Equation (RUSLE) model was employed in conjunction with Remote Sensing (RS) and Geographic Information System (GIS) techniques. Analysis of the topographical characteristics of the study area reveals heightened vulnerability to soil loss, with the highest average annual soil loss recorded at 74 tons per hectare per year. The generated maps illustrate that the area exhibits the highest sediment yield of 246 tons per hectare per year, with an elevated average annual soil loss of 435 tons per year. The distribution across severity classes indicates 7% under the very high category, 14% under high, 18% under moderate, 10% under low, and 12% under very low soil loss categories within the Ghizer district. The results of this research help develop a rich information source for researchers and planners to develop more effective approaches for reducing soil loss within high-severity zone categories. Recommendations include the implementation of tree plantation initiatives and the construction of structures such as check dams, which have demonstrated effectiveness in controlling the process of soil erosion. Hypsometric Integrals (HIs) are estimated from empirical formulae based on the geographical plots of the measured contour elevation and the area they encompass. This study aims to calculate hypsometric integral values for the Ghizer River and its sub-watersheds in Hindukush using four different techniques and to compare the procedural techniques of its estimation and relevance to the erosion status. The hypsometric integral expresses precisely the ratio of relief elevation; it is not so cumbersome to use and can be computed easily within an RS environment.
Enhancing Autonomous Systems with Spatial Intelligence: Integrating Edge Computing, Sensor Fusion, and Predictive Modeling for Real-Time Decision-Making
The increasing complexity of autonomous systems necessitates advanced strategies for real-time perception, decision-making, and predictive control. This study investigates the integration of spatial intelligence with edge computing and sensor fusion to enhance the performance, responsiveness, and reliability of autonomous vehicles, drones, and robotic systems. By processing heterogeneous sensor data locally at the edge, the proposed framework reduces latency, optimizes predictive modeling, and improves operational efficiency, particularly in dynamic and unpredictable environments. Quantitative evaluations using authentic datasets, including KITTI, nuScenes, UAV123, and ROS SLAM, demonstrate significant reductions in latency, improvements in object detection accuracy, enhanced trajectory prediction, and higher task completion rates compared to traditional cloud-based systems. Bandwidth usage was also substantially reduced, highlighting the framework’s efficiency in data-intensive applications. The findings indicate that combining edge computing with sensor fusion and predictive modeling provides a scalable, robust, and adaptive solution for autonomous systems, enabling safer and more reliable operation across diverse platforms. This research contributes to the development of next-generation autonomous systems capable of intelligent, real-time interaction with complex environments.
Spatiotemporal Data Fusion Using Computational Intelligence for High-Resolution Urban Environmental Monitoring
Rapid urbanization and environmental degradation present significant challenges for sustainable urban management. Accurate, high-resolution monitoring of environmental parameters is essential for informed decision-making in smart cities. This study proposes a computational intelligence-based spatiotemporal data fusion framework to integrate heterogeneous datasets, including satellite imagery, ground-based sensors, and social media data, for urban environmental monitoring. The framework employs deep learning models, specifically CNN-LSTM architectures, combined with spatial semantics and knowledge mapping to enhance temporal continuity, spatial resolution, and predictive accuracy of key environmental indicators such as NDVI, surface temperature, and PM₂.₅ concentrations. Quantitative evaluation demonstrates strong agreement between observed and predicted values, with R² exceeding 0.88 for all parameters, highlighting the robustness of the approach. Seasonal patterns in vegetation and temperature, as well as spatial hotspots in air pollution, were effectively captured, supporting decision-making for urban planning, digital twin construction, and sustainable governance. The study confirms that multi-source data fusion, coupled with computational intelligence, can provide high-resolution, actionable insights for urban environmental management. Future work should focus on real-time data integration, scaling to regional levels, and enhancing predictive capabilities for complex urban systems.
Integrating Spatial Knowledge Graphs and Artificial Intelligence for Enhanced Urban Intelligence and Smart City Analytics
The rapid urbanization of modern cities has intensified challenges related to environmental management, infrastructure optimization, and data-driven decision-making. This study presents an integrated framework that combines Spatial Knowledge Graphs (SKGs) with Artificial Intelligence (AI) and remote sensing technologies to enhance urban intelligence and smart city analytics. Using authentic datasets derived from Sentinel-2 imagery, OpenStreetMap, and real-time IoT sensor data, the research models spatiotemporal relationships among critical urban parameters such as land use, air quality, and traffic density. The developed SKG framework enables dynamic querying, semantic reasoning, and predictive modeling through Graph Neural Networks (GNNs) and spatial embeddings. Quantitative analyses reveal strong correlations between urban density, transportation networks, and pollution intensity, demonstrating superior predictive accuracy over traditional GIS-based approaches. The findings confirm that the integration of SKGs and AI supports advanced spatial reasoning, enabling adaptive and sustainable urban planning. This study contributes to the evolving discourse on smart city intelligence by establishing a scalable, data-driven framework for urban analytics, with implications for climate resilience, infrastructure management, and real-time decision support systems.
Integration of Remote Sensing and Spatial Artificial Intelligence for Climate Risk Assessment
Climate change poses escalating threats to ecosystems, human settlements, and economies worldwide, necessitating advanced analytical approaches for risk detection and mitigation. This study integrates remote sensing and spatial artificial intelligence (AI) to assess climate risks across multiple dimensions, including temperature variability, precipitation extremes, and vegetation stress, from 2018 to 2024. Using multi-source satellite datasets such as MODIS, Landsat-8, and Sentinel-5P, combined with spatial AI algorithms, this research quantified environmental indicators and modeled spatiotemporal patterns of climate hazards. Quantitative analysis revealed a notable increase in temperature anomalies (up to 1.4°C), a 12–15% rise in precipitation variability, and a 9% decline in vegetation indices (NDVI) in vulnerable regions. These results underscore intensifying climate instability, consistent with global and regional climate reports. The integration of AI-driven spatial analytics enabled enhanced accuracy in identifying high-risk zones and temporal dynamics, surpassing the capabilities of conventional climate models. Comparisons with existing studies validate that the combined use of remote sensing and AI enhances predictive capacity, early warning mechanisms, and data-driven policy formulation. This interdisciplinary framework thus offers a robust foundation for climate resilience planning and adaptive environmental governance. The study concludes that future work should focus on real-time satellite monitoring, fusion of high-resolution datasets, and development of explainable AI models to further refine climate risk assessments and inform sustainable mitigation strategies.
Integrating Remote Sensing and Spatial Artificial Intelligence for Climate Risk Assessment in Pakistan: A Data-Driven Spatiotemporal Analysis
This study explores the integration of remote sensing and Spatial Artificial Intelligence (Spatial AI) for comprehensive climate risk assessment across Pakistan. Using multi-sensor satellite data from MODIS, CHIRPS, and Landsat, combined with AI-driven analytical models, the research quantifies spatial and temporal variations in key climate indicators including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and precipitation from 2000 to 2023. The methodology involved preprocessing satellite imagery in Google Earth Engine, feature extraction, and applying machine learning regression models to identify climate vulnerability zones. Results revealed a statistically significant upward trend in LST (0.32°C per decade) and a corresponding decline in NDVI, particularly across southern Punjab, Sindh, and Balochistan. The AI-based model achieved high predictive accuracy (R² = 0.89, RMSE = 0.45), indicating strong reliability in spatial risk mapping. Comparison with previous studies validated the robustness of this hybrid approach, demonstrating that Spatial AI provides superior detection and prediction capabilities compared to conventional GIS methods. The findings underscore the potential of integrating AI with remote sensing for early warning systems, adaptive climate management, and sustainable regional planning in climate-vulnerable regions of Pakistan.
Hierarchical Graph Convolutional Networks with Attention Mechanisms for Integrated Urban Traffic and Energy Network Prediction Using UAV and GIS Data
Urbanization and the rapid growth of both vehicular traffic and energy demand present significant challenges for sustainable urban planning. Accurate spatiotemporal modeling of traffic flow and energy network infrastructure is crucial for efficient management, real-time decision-making, and smart city development. This study proposes a hierarchical Graph Convolutional Network (GCN) integrated with attention mechanisms to model complex interactions within large-scale urban traffic and energy systems. High-resolution data collected from unmanned aerial vehicles (UAVs) and GIS-based spatial datasets were used to construct predictive models for traffic congestion and energy network behavior. The proposed framework captures local and global dependencies, prioritizes critical nodes, and efficiently manages large-scale interconnected systems. Results demonstrate high predictive accuracy in traffic flow (MAE = 3.12 vehicles/min, Pearson r = 0.91) and energy network node classification (accuracy = 95.3%). Sensitivity and ablation analyses confirm the importance of hierarchical decomposition and attention mechanisms for model performance. Case studies illustrate the practical utility of the framework in real-time traffic management and predictive energy infrastructure monitoring. Overall, this research presents a scalable, robust, and data-driven methodology for integrated urban system modeling, supporting informed decision-making and sustainable infrastructure planning.
Digital Twins as the Predictive Core of Smart Cities: A Systematic Review and Conceptual Framework for Sustainable Urban Futures
Urbanization is accelerating at an unprecedented pace, creating complex challenges related to infrastructure, mobility, healthcare, energy, and environmental sustainability. Digital Twin (DT) technology has emerged as a transformative tool within smart cities, enabling real-time monitoring, predictive analytics, and data-driven decision-making through the integration of IoT, AI, ML, blockchain, and cloud/edge computing. This study conducts a systematic review of 148 peer-reviewed publications from 2014 to 2023, alongside an analysis of prominent global case studies, to evaluate the current state, technological enablers, application domains, and challenges of DT adoption in smart cities. Results reveal a steep increase in DT-related research after 2017, with Europe and Asia leading global contributions. Urban planning, transportation, and energy management dominate existing applications, while healthcare and environmental monitoring represent emerging domains. Key technological enablers include IoT and AI/ML, whereas challenges such as interoperability, data governance, scalability, and inclusivity hinder large-scale implementation. Building on these findings, the study proposes a five-layer conceptual framework positioning DTs as the predictive and integrative core of future smart cities, comprising data, processing, simulation, application, and governance layers. The framework emphasizes not only technological integration but also ethical and regulatory considerations to ensure equitable urban development. This research contributes to advancing theoretical and practical understanding of DT-enabled smart cities and highlights pathways for achieving sustainable, resilient, and citizen-centered urban futures.
Harnessing Drone Swarms for Enhanced Search and Rescue Operations: Efficiency, Resilience, and Future Directions
Drone swarms have emerged as a transformative technology for search and rescue (SAR) operations, offering greater adaptability, resilience, and efficiency compared to traditional single-UAV approaches. This study evaluates the performance of drone swarms across key dimensions, including coverage efficiency, victim detection accuracy, communication resilience, energy utilization, and adaptability under uncertainty. Results demonstrate that swarms surveyed disaster-affected zones 42% faster than single drones, with victim detection accuracy approaching 90% when integrated with thermal imaging and computer vision. Communication resilience remained above 85% even in degraded environments due to mesh networking, while swarm adaptability limited performance losses to under 4% in adverse conditions. Although cumulative energy consumption was higher, reduced mission time offset operational trade-offs. The findings underscore the potential of drone swarms to redefine SAR protocols, highlighting both their strengths and current limitations. Future research should focus on integrating swarms into regulatory frameworks, enhancing energy sustainability, and improving human-drone collaboration.
Integrating Satellite-Derived Indices and Support Vector Machines for Enhanced Wildfire Detection: An Empirical Study on Australian Forest Fires
Wildfires represent one of the most pressing ecological and socio-economic challenges worldwide, with increasing frequency and intensity linked to climate change. Traditional wildfire detection methods such as human surveillance and weather-based indices are limited in accuracy and responsiveness, particularly in heterogeneous landscapes. This study introduces a novel approach that integrates satellite-derived indices with Support Vector Machine (SVM) models to enhance wildfire detection accuracy. Specifically, the Normalized Difference Fire Index (NDFI), developed using SWIR2 and Red spectral bands, was employed to improve sensitivity in distinguishing fire-affected areas. The model incorporated multispectral satellite data, meteorological variables, and historical fire records to classify wildfire and non-wildfire events. Results demonstrated that the SVM classifier significantly outperformed Random Forest (RF) and Logistic Regression (LR), achieving the highest area under the curve (AUC = 0.97) and superior accuracy in both large-scale and small-scale fire detection. Feature importance analysis highlighted SWIR2, Red bands, and vegetation indices as the most influential predictors, while temperature and wind speed also played critical roles. The confusion matrix indicated low misclassification rates, underscoring the reliability of SVM for operational use. This study contributes a scalable, adaptable framework for wildfire monitoring that integrates machine learning with remote sensing. The findings have significant implications for early warning systems, disaster management, and climate change mitigation by improving response times and reducing fire-related ecological and economic damages.
Artificial Intelligence for Enhancing Urban Land Use Mix and Functional Diversity: A Data-Driven Approach to Sustainable Mixed-Use Development
Urban areas are increasingly challenged by functional segregation, uneven land use distribution, and limited accessibility, which reduce urban vitality and sustainability. This study explores the application of Artificial Intelligence (AI) in enhancing urban land use mix and functional diversity, promoting vibrant, walkable, and sustainable neighborhoods. A systematic literature review of 654 studies published between 2014 and 2025 was conducted to identify AI methodologies, urban planning applications, and research gaps. High-resolution spatial data, including Sentinel-2 imagery and GIS layers, were analyzed using machine learning (Random Forest, SVM), deep learning (CNN), and optimization algorithms (GA, PSO). Functional diversity was assessed using Shannon entropy, while accessibility and walkability indices evaluated urban vitality. Results indicate that CNN outperformed traditional machine learning in classifying complex land use types, particularly mixed-use areas, achieving an overall accuracy of 91.5%. AI-based optimization improved functional diversity by 19–21%, increased mixed-use coverage from 21.5% to 30.1%, and enhanced walkability and accessibility scores. Scenario analysis demonstrated that AI-driven urban planning can guide sustainable development, particularly in underutilized and low-diversity areas. The study emphasizes the potential of Explainable AI (XAI) to ensure transparency, interpretability, and equity in AI-driven urban planning decisions. Overall, integrating AI into urban planning processes provides a data-driven, evidence-based framework for creating functional, resilient, and sustainable urban environments.
Integrating Anomaly Detection and Predictive Modeling for Real-Time Water Quality Monitoring: A Data-Driven Approach for Sustainable Resource Management
Ensuring safe and sustainable water resources requires timely detection of contamination events and accurate forecasting of water quality trends. This study presents a data-driven framework that integrates anomaly detection with predictive modeling to enhance real-time water quality monitoring. Using publicly available datasets supplemented with hypothetical scenarios, key water quality parameters—including pH, turbidity, and conductivity—were analyzed over a six-month period. Advanced anomaly detection methods, such as the Isolation Forest algorithm, were employed to identify abnormal patterns, while predictive models including ARIMA, Long Short-Term Memory (LSTM), and a hybrid ARIMA-LSTM model were applied to forecast future trends. The results revealed that the hybrid ARIMA-LSTM model outperformed traditional approaches, achieving the lowest mean absolute error (MAE = 0.19) and root mean square error (RMSE = 0.28), demonstrating its robustness in handling non-linear and time-dependent fluctuations. Anomalies corresponding to extreme turbidity spikes and sudden pH deviations were successfully detected, highlighting the framework’s potential as an early warning system for contamination events. Graphical analyses further illustrated model performance and anomaly detection outcomes, confirming the applicability of artificial intelligence (AI) techniques in environmental monitoring. This research contributes to advancing sustainable water management by integrating real-time monitoring, anomaly detection, and predictive modeling. The proposed system not only improves accuracy and reliability in water quality assessment but also aligns with the Sustainable Development Goals (SDGs), particularly SDG 6 on clean water and sanitation. While the study demonstrates promising outcomes, future work should focus on integrating additional heterogeneous data sources, field deployment, and improving model interpretability for decision-making.
Mining Software Bug Repositories: A 15-Year Analysis of Trends, Techniques, and Limitations in Bug Localization, Classification, Triaging, and Resolution (2010–2024)
Over the past decade and a half, mining software bug repositories has become a critical research domain for improving software maintenance, quality assurance, and automated debugging processes. This study presents a comprehensive analysis of bug localization, classification, triaging, and resolution trends from 2010 to 2024, based on data from prominent repositories such as Eclipse, Mozilla, KDE, Apache, and OpenStack. Using a dataset comprising over 100 peer-reviewed research papers and repository-derived performance metrics—including Mean Average Precision (MAP), F1-score, and Mean Reciprocal Rank (MRR)—the research identifies key methodological advances and persistent challenges in automated bug handling. The findings reveal that machine learning–driven approaches, particularly deep learning models, have significantly improved classification accuracy, often exceeding 90%, while hybrid techniques integrating textual, contextual, and developer history data have reduced bug triaging delays. However, bug localization remains hindered by imbalanced and noisy data, and resolution automation suffers from limited dataset standardization and cross-repository generalizability. Temporal trends indicate a shift from rule-based methods to multi-modal AI frameworks, leveraging natural language processing, statistical modeling, and repository mining. This work contributes a synthesized understanding of the field’s evolution, highlights gaps such as inconsistent reporting formats and lack of explainable AI adoption, and provides recommendations for future research aimed at developing standardized, scalable, and interpretable bug management solutions.
GIS-Based Multi-Criteria Decision Analysis for Optimal Emergency Assembly Place Selection in Urban Areas
Effective disaster management requires the identification of suitable emergency assembly places that ensure public safety, accessibility, and adequate capacity. This study presents a comprehensive framework integrating Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) techniques to evaluate and prioritize potential assembly locations. The Best-Worst Method (BWM) was employed to assign weights to relevant criteria, including accessibility, hazard exposure, population capacity, and land suitability. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank alternatives. Spatial analysis revealed that Place B achieved the highest suitability score due to its optimal balance of accessibility, safety, and capacity, while Place C exhibited lower suitability owing to limited accessibility and vulnerability to hazards. The results demonstrate the effectiveness of combining GIS and MCDM methods in supporting data-driven decision-making for disaster preparedness. The methodology provides urban planners and emergency managers with a replicable framework to enhance evacuation planning, minimize risks, and improve community resilience.
Integrating Artificial Intelligence and Augmented Reality for Geospatial Visualization in Emergency Management and Built Environments
The growing frequency and intensity of disasters, including floods, wildfires, and urban hazards, necessitate advanced decision-support systems for emergency management. This study explores the integration of Artificial Intelligence (AI) and Augmented Reality (AR) for geospatial visualization, emphasizing their combined potential in enhancing situational awareness, hazard prediction, and real-time emergency response within built environments. Using a mixed-methods approach, the study incorporated machine learning models for predictive hazard mapping and AR interfaces for immersive visualization of risk scenarios. Results indicate that AI-based hazard prediction achieved up to 92% accuracy in flood mapping and 89% in wildfire modeling, outperforming traditional geospatial techniques. AR integration reduced emergency response times by 28% and improved hazard communication for field responders. The synergy of AI and AR demonstrated a localization accuracy of 95%, significantly higher than standalone applications. These findings highlight the transformative role of AI–AR systems in improving resilience, preparedness, and adaptive capacity in urban disaster management. The study contributes novel insights into bridging the gap between predictive analytics and immersive visualization, offering a framework that supports decision-makers, first responders, and urban planners.
A Market-Based 3D-Aware Coordination Framework for Multi-Robot Systems in Dynamic Environments
Efficient task allocation and coordination are critical challenges in multi-robot systems, particularly in dynamic and unstructured environments. This study proposes a market-based coordination framework that leverages local spatial awareness and decentralized decision-making to optimize task distribution, reduce energy consumption, and minimize collisions among autonomous robots. The algorithm integrates 3D scene information through local volumetric representations to improve navigation and task planning in environments with obstacles and dynamically emerging tasks. Extensive simulations in both static and dynamic scenarios were conducted, evaluating metrics including task completion time, energy efficiency, success rate, and collision incidence. Results indicate that the proposed framework scales effectively with increasing robot numbers and task complexity, outperforming benchmark approaches such as Ant Colony Optimization and the Hungarian method. Statistical validation confirms significant improvements across all performance metrics. These findings demonstrate that incorporating spatial awareness and market-based coordination in multi-robot systems enhances efficiency, robustness, and scalability, with potential applications in search-and-rescue, autonomous logistics, and industrial automation.
An Integrated Ant Colony and Dynamic Window Approach for Cooperative Multi-Robot Trajectory Planning in Safflower Cultivation
The increasing adoption of agricultural robotics has highlighted the need for efficient and reliable trajectory planning in crop fields. Traditional methods often optimize either global coverage or local obstacle avoidance but fail to achieve both simultaneously in dynamic agricultural environments. This study presents an integrated trajectory planning framework that combines Ant Colony Optimization (ACO) for global path planning and the Dynamic Window Approach (DWA) for real-time local obstacle avoidance. The approach was applied to safflower fields to enhance coverage, reduce path redundancy, and minimize collision risks among multiple robots. Simulation results demonstrate that the hybrid ACO-DWA framework outperformed conventional methods in terms of path length, coverage efficiency, and adaptability to unexpected field obstacles. Specifically, the proposed method reduced path length by 14%, improved coverage rate by 11%, and decreased collision frequency by 18% compared to baseline approaches. These results suggest that the integration of global and local planning strategies can significantly improve robotic efficiency in agricultural operations, providing a scalable solution for sustainable crop management.
A Hybrid ACO-DWA and Distributed Task Allocation Framework for Efficient Multi-Robot Operations in Unstructured Agricultural Environments
The deployment of multiple autonomous robots in agricultural operations offers significant potential for improving efficiency, reducing labor costs, and enhancing sustainability. However, navigating unstructured farm environments while coordinating multiple robots presents challenges such as collision avoidance, dynamic obstacle management, and efficient task allocation. This study proposes a novel framework that integrates a hybrid Ant Colony Optimization (ACO) and Dynamic Window Approach (DWA) for global path planning with a distributed priority-based task allocation strategy. The ACO-DWA hybrid optimizes global trajectories while ensuring smooth and dynamically feasible paths, whereas the distributed priority-based mechanism enables conflict-free coordination and balanced task distribution among multiple robots. Extensive simulations and ROS-based experiments were conducted in 2.5D grid-based farm environments with varying terrain, obstacles, and robot densities. Results indicate that the proposed framework outperforms conventional algorithms in terms of path length, energy consumption, task completion time, and collision avoidance. Scalability analysis demonstrates the framework’s ability to maintain high performance with increasing robot team sizes. Overall, the proposed methodology provides a robust, efficient, and scalable solution for autonomous multi-robot operations in complex agricultural settings, with potential applications in harvesting, monitoring, and precision farming.
Comparative Analysis of Hierarchical and Integrated Trajectory Planning Architectures for Autonomous Vehicles in Multi-Scenario Environments
Autonomous vehicles (AVs) rely on effective trajectory planning to ensure safe, efficient, and comfortable operation across diverse driving scenarios. This study investigates the comparative performance of hierarchical and integrated trajectory planning architectures under multiple simulated driving scenarios, including car-following, lane-changing, and corner-case situations such as unprotected left turns and roundabouts. Hierarchical planners, which decouple path and speed planning, were compared against integrated planners that optimize both simultaneously. Performance was evaluated using safety metrics (collisions and near-miss events), efficiency metrics (average travel time and speed consistency), and passenger comfort metrics (mean jerk and acceleration variation). Results indicate that integrated planners outperform hierarchical planners across all dimensions, reducing collisions by 28%, improving speed consistency by 12%, and providing smoother ride quality. A composite performance index further confirmed the superiority of integrated planners, highlighting their adaptability in complex and dynamic traffic conditions. These findings suggest that integrated trajectory planning architectures offer significant advantages for real-world AV deployment, enhancing safety, operational efficiency, and passenger comfort. Future work should extend these findings through real-world testing and incorporation of adaptive learning-based trajectory optimization techniques.
Dual-Node Embedding Spatiotemporal Graph Neural Networks for Accurate Urban Traffic Flow Prediction Using UAV and Sensor Data
Urban traffic congestion is a pervasive challenge that impacts economic productivity, environmental sustainability, and overall quality of life. Accurate short-term traffic flow prediction is essential for effective traffic management, route optimization, and proactive incident response. This study proposes a novel dual-node embedding spatiotemporal graph neural network (STGNN) that simultaneously captures spatial and temporal dependencies in traffic data while leveraging continuous-time dynamics via neural ordinary differential equations (NODEs). The model integrates real-time traffic data collected from unmanned aerial vehicles (UAVs) and ground-based sensors to enhance prediction accuracy under diverse traffic conditions, including peak hours, accidents, and adverse weather. Extensive experiments were conducted on multiple real-world datasets, demonstrating that the proposed approach outperforms baseline models, including ARIMA, LSTM, CNN, and conventional STGNN, in terms of mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R²). An ablation study confirmed the critical role of spatial embeddings, temporal embeddings, NODEs, and UAV data in improving model performance. The results highlight the model’s potential for deployment in intelligent transportation systems, enabling real-time traffic monitoring, dynamic signal control, and congestion mitigation. This work provides a robust and interpretable framework for urban traffic prediction and offers a foundation for future research on smart city mobility management.
Ontology-Based Semantic Knowledge for Context-Aware Autonomous Indoor Robot Navigation
Autonomous indoor navigation remains a critical challenge in robotics, particularly in dynamic environments where robots must interact safely and efficiently with humans and obstacles. This study investigates the integration of ontology-based knowledge representation with sampling-based path planning to enhance context-aware robotic navigation. By leveraging semantic knowledge and spatio-temporal reasoning, the proposed system enables robots to interpret their environment, anticipate potential obstacles, and dynamically re-plan paths to achieve collision-free navigation. Experimental results demonstrate that the ontology-enabled RRT* algorithm outperforms the baseline geometric RRT* in path efficiency, travel time, and collision reduction across multiple indoor scenarios. Additionally, the system achieves high semantic reasoning accuracy, supporting reliable interpretation of environmental relationships and task-relevant knowledge. These findings highlight the value of incorporating semantic reasoning into autonomous navigation systems and suggest that ontology-based approaches can significantly improve robot performance, safety, and adaptability in real-world indoor environments. The study provides a foundation for future research in cognitive robotics, human-robot interaction, and intelligent autonomous systems.
Integrating Geospatial and Machine Learning Approaches for Flood Risk Assessment and Disaster Management: A Case Study of Sheikhupura District, Pakistan (2022–2024)
Floods remain one of the most destructive natural hazards worldwide, posing severe threats to human life, infrastructure, and livelihoods. In Pakistan, rapid urbanization, climate variability, and poor drainage infrastructure have intensified flood vulnerability, particularly in districts like Sheikhupura. This study evaluates the effectiveness of integrating Geographic Information Systems (GIS), remote sensing, and machine learning (ML) techniques for flood risk assessment and disaster management in Sheikhupura from 2022 to 2024. Multi-source datasets, including Sentinel-1 SAR, Sentinel-2 imagery, digital elevation models, meteorological records, hydrological data, and socio-economic information, were processed and analyzed using supervised classification, multi-criteria decision analysis (MCDA), and machine learning models (Random Forest and Support Vector Machine). Flood susceptibility mapping revealed that 32.8% of the district lies in high-risk zones, 47.4% in moderate-risk zones, and 19.8% in low-risk areas. Random Forest outperformed SVM with an accuracy of 89% and an AUC of 0.92, demonstrating superior predictive capability. Temporal analysis indicated that 2023 experienced the most severe flood events, causing economic losses exceeding PKR 3 billion, primarily affecting agriculture, infrastructure, and health sectors. The findings underscore the critical need for targeted flood preparedness, early warning systems, and climate-resilient planning. This study provides actionable insights for local authorities and contributes to the broader understanding of data-driven flood management strategies in South Asia.
Hilbert-Curve-Driven Point Cloud Semantic Segmentation: Enhancing Spatial Locality Preservation for Improved Accuracy and Efficiency
Point cloud semantic segmentation remains a challenging problem due to the irregular and unordered nature of 3D data, which complicates the learning of spatial relationships between points. This study introduces a novel segmentation framework that leverages Hilbert space-filling curves to impose an ordering on points while preserving spatial locality. By encoding geometric features in a Hilbert-ordered sequence, our method enables more efficient neighborhood aggregation and improved feature learning compared to conventional graph-based and transformer-based approaches. Experiments conducted on the S3DIS benchmark dataset demonstrate notable improvements in mean Intersection over Union (mIoU) and per-class IoU, particularly in geometrically complex classes such as beams, columns, and bookshelves. The proposed method also exhibits robustness to point sparsity and boundary misclassification, achieving competitive performance with reduced computational overhead. These results highlight the potential of space-filling curve-driven ordering as a scalable and generalizable approach for large-scale 3D scene understanding, with applications in autonomous navigation, robotics, and digital twin development.
Enhancing Semantic Enrichment of Building Information Models Using Edge Feature-Enhanced Graph Neural Networks
Building Information Modeling (BIM) has transformed the Architecture, Engineering, and Construction (AEC) industry by enabling detailed digital representations of building components. However, effectively capturing and utilizing complex spatial relationships within BIM models remains a critical challenge, limiting semantic richness and interoperability. This study proposes a novel Graph Neural Network (GNN) framework that incorporates edge features to explicitly model spatial and functional relationships between building elements. We constructed a comprehensive graph dataset comprising 14 room types and 4 spatial relationships to evaluate the framework’s performance on semantic classification tasks. Experimental results demonstrate that the edge feature-enhanced GNN significantly outperforms traditional machine learning classifiers and vanilla GNN models, achieving an accuracy of 91.8% in room type classification. Ablation studies highlight the importance of edge attributes and advanced graph techniques, such as the link-less subgraph method, in boosting model robustness and scalability. Furthermore, the proposed model exhibits resilience to data sparsity and noise, suggesting practical viability for real-world BIM applications. This research underscores the potential of advanced GNN architectures to enhance BIM semantic enrichment, supporting more intelligent, interoperable, and automated building design and management workflows.
Architecture Engineering and Construction (AEC)
Multi-Objective Autonomous Exploration for Simultaneous Mapping and Spatial Process Reconstruction Using Gaussian Processes
Autonomous mobile robots operating in unknown environments face the dual challenge of efficiently mapping their surroundings while simultaneously reconstructing spatially distributed environmental processes. This study proposes a novel multi-objective exploration framework that integrates occupancy grid mapping with Gaussian Process regression for spatial field estimation. The approach incorporates uncertainty-aware planning and a multi-step goal sequencing strategy to optimize environment coverage, reduce process reconstruction error, and minimize travel distance. Experimental evaluations in an indoor environment demonstrate that the proposed method significantly outperforms baseline frontier-based and random navigation strategies in terms of map coverage (92.4% ± 2.1%), spatial process estimation accuracy (RMSE: 0.18 ± 0.04), and exploration efficiency. The results validate the benefits of integrating process uncertainty into the planning utility and highlight the potential of Gaussian Process-informed exploration for autonomous environmental monitoring. Future research will focus on extending the framework to dynamic, large-scale outdoor scenarios and incorporating multi-modal sensor fusion to enhance robustness and adaptability.
Hierarchical Spatiotemporal Attention Network for Multi-Step Passenger Flow Prediction at Station Level: A Case Study of Gujranwala
Accurate multi-step passenger flow prediction is essential for optimizing urban transportation systems and enhancing service efficiency. This study proposes ST-HAttn, a novel hierarchical spatiotemporal attention-based deep learning model designed to forecast passenger flows at the station level. By simultaneously capturing local and global spatial dependencies alongside temporal dynamics, ST-HAttn effectively models complex, non-stationary mobility patterns inherent in urban transit networks. Evaluated on real-world passenger data from Gujranwala, the model outperforms traditional statistical methods and contemporary deep learning architectures, demonstrating significant improvements in mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) across multiple forecast horizons (30, 60, and 180 minutes). The hierarchical attention mechanism allows for adaptive feature extraction at both station and regional levels, facilitating robust predictions despite noisy and fluctuating demand patterns. The findings highlight ST-HAttn’s potential to support demand-responsive scheduling and resource allocation in smart transportation systems, contributing to reduced congestion and improved passenger experience. Future research directions include incorporating multimodal contextual data and validating model transferability to other urban settings.
Predicting Rice Yield Using Multi-Temporal Vegetation Indices and Machine Learning: A Comparative Study of Random Forest and Support Vector Regression Models
Accurate prediction of rice yield is essential for ensuring food security and optimizing agricultural management practices. This study integrates multi-temporal remote sensing vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Red Edge (NDRE)—with advanced machine learning algorithms to forecast rice yield. Time-series data from critical phenological stages were analyzed to capture crop growth dynamics, while Random Forest (RF) and Support Vector Regression (SVR) models were developed to estimate yield outcomes. Results indicate that the RF model outperforms SVR in predictive accuracy, with an R² value of 0.92 compared to 0.87 for SVR. Feature importance analysis identified maximum NDVI and cumulative NDVI (area under curve) as the most influential predictors, emphasizing the significance of canopy vigor and growth duration. Spatial yield maps generated from the RF model provide valuable insights for precision agriculture interventions. The proposed framework demonstrates robust potential for early-season yield forecasting, facilitating improved resource allocation and decision-making in rice cultivation. Future research should focus on expanding the model’s applicability across different environments and integrating real-time sensor data for dynamic crop monitoring.
Spatio-Temporal Forecasting of Hydro-Meteorological Extremes in Fiji Using Deep Learning and Ocean-Atmospheric Climate Indicators
The increasing frequency and intensity of hydro-meteorological extremes, such as floods and droughts, pose significant risks to vulnerable Pacific island nations like Fiji. Accurate and timely forecasting of these events is essential for effective risk management and early warning systems. This study presents a novel deep learning framework that leverages long short-term memory (LSTM) networks to forecast the Effective Drought Index (EDI), a key indicator of hydrological extremes. The framework integrates multivariate spatial data and principal components of sea surface temperature (SST) to capture complex ocean-atmospheric climate influences on regional precipitation patterns. Comparative analysis of univariate, spatial-only, and multivariate models demonstrates that incorporating SST information significantly improves forecast skill, particularly for lead times up to 14 days. The spatial variability of model performance highlights challenges related to topography and localized climate effects. The results underscore the potential of combining deep learning with climate science to enhance early warning capabilities, supporting disaster preparedness and climate resilience in Fiji and similar island regions. Future work should explore higher spatial resolution modeling and the inclusion of additional climate drivers to further refine forecasting accuracy.
Enhancing Spatial Skills through Immersive Virtual Reality: An Empirical Study on Educational Outcomes and Cognitive Engagement
This study investigates the impact of immersive virtual reality (VR) on the development of spatial skills and educational performance. Using a controlled experimental design, participants engaged in VR-based spatial training modules designed to improve their ability to mentally manipulate objects and navigate complex environments. The results reveal significant improvements in spatial reasoning and cognitive engagement among the VR group compared to traditional learning methods. Additionally, the study explores the role of embodied cognition and emotional involvement in reinforcing learning within VR contexts. These findings contribute to the growing body of evidence supporting VR as a valuable educational tool, with implications for curriculum design and instructional strategies. Despite promising outcomes, considerations regarding accessibility and user comfort highlight areas for future research. Overall, this study demonstrates that VR can effectively augment spatial skill acquisition, offering transformative potential for education and cognitive training.