Search Results (2,392)

Spatially explicit crop yield information is needed for regional environmental modeling, sustainability assessment, and agricultural decision support, yet official yield statistics are commonly reported only at aggregated administrative scales. This study introduces the NAYD R package, a reproducible geospatial workflow for converting county-level harvested area and yield statistics into spatially explicit production units and zonal clusters while preserving consistency with official records. County-level statistics from the USDA National Agricultural Statistics Service were integrated with USDA Cropland Data Layer crop masks, multi-sensor NDVI products, and satellite-derived evapotranspiration from OpenET SSEBop. An NDVI-based eligibility filter refined crop masks toward reported harvested area, while normalized NDVI and evapotranspiration layers were combined into spatial weighting surfaces and aggregated into contiguous production units and graph-based clusters. Case studies for cotton and winter wheat in Texas showed that the eligibility filter removed approximately 20–40% of CDL-classified pixels while maintaining consistency with reported harvested area and preserving plausible spatial gradients associated with irrigated and dryland systems. Evaluation against independent Texas A&M AgriLife variety trial data indicated that the disaggregated clusters reproduced plausible spatial patterns of yield variability while retaining the county-level NASS constraints. The workflow provides an open-source framework for generating statistically consistent production zones for regional crop modeling, environmental assessment, and sustainable agricultural planning. Full article

Indigenous knowledge and associated indigenous resource management practices are at the root of sustainable land and marine management. Typically, they point to the necessity of maintaining biodiversity and of ensuring the sustenance of social and economic systems, which benefit the well-being of indigenous communities. Conscious of these core attributes, the Government of Indonesia has enabled formal access for indigenous communities to forests for their livelihoods. Nonetheless, meeting the sustainable development goals through such forest management and conservation in Indonesia is threatened by various competing interests and power imbalances. These lead to the disproportionate conversion of naturally vegetated areas, as well as the inability of communities to benefit from economic opportunities. Moreover, the Government of Indonesia has insufficiently regulated the utilisation of indigenous knowledge to conserve the forest areas. This creates a policy design and implementation gap which is not properly understood or addressed. In this conceptual article, we posit that applying cultural space methodology fills the gaps. This methodology combines cultural space and land administration concepts and connects people to land and marine space. This article discusses how and why using the methodology proves to be effective for agricultural and maritime communities in Indonesia and helps to reform the administration capacities of the territories. It identifies and assesses people and land/marine space relationships by the existence of (1) knowledge, practices, and/or objects that represent the relationship, (2) the social, economic, and environmental function of space for the community, and (3) administration of the forest and conservation areas. The methodology also provides a procedure to convert information on the interrelation of the indigenous community, its cultural space in the forest and conservation areas, and indigenous knowledge into geospatial information and data that represent the cultural space unit as a geographic feature. 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

3D Gaussian Splatting (3DGS) offers efficient explicit rendering, but large-scale remote-sensing use remains fragmented across UAV/aerial photogrammetry, satellite reconstruction, large-scene scaling, surface modeling, and geospatial evaluation. We present a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed scoping review based on 55 core studies identified through Web of Science, Scopus, IEEE Xplore, and supplementary searches completed on 3 June 2026. A faceted taxonomy organizes the literature by platform, sensor model, scalability strategy, and geometric supervision. The synthesis shows that partitioning, hierarchy, compression, and feed-forward inference improve scalability but do not guarantee metric geometry. Reliable deployment additionally requires sensor-consistent projection, geometric or georeferencing constraints, explicit supervision labels, and product-level evaluation. In control-point-free settings, internal consistency should be distinguished from independently validated accuracy. We therefore propose a platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings. Full article

While Japan’s population is declining overall, some areas remain densely populated within designated hazard zones. Understanding how these spatial patterns vary across urban contexts is important for sustainable and resilient urban development. This study presents a nationwide analysis of hazard-specific estimated population exposure at postcode-level population core cells across walkability-based urban spatial characteristics in Japan. We integrated designated hazard-zone layers from the Geospatial Information Authority of Japan (GSI) with 250 m census population grids and linked the resulting dataset to the Japan Postcode-level Walkability Index using these core cells as a common spatial unit. This analysis used postcode-level population core cells and was not designed to estimate total hazard-zone-based exposure within entire postcode areas. The four hazard-zone layers were analyzed separately to characterize hazard-specific patterns, not to assess simultaneous or compound hazard events, cumulative exposure, or compound risk. Population core cells in higher-JPWI strata generally overlapped more frequently with flood, storm surge, and tsunami inundation zones, whereas lower-JPWI population core cells overlapped more frequently with designated landslide warning zones. Where hazard-zone overlap was identified, estimated exposed populations tended to be larger in higher-JPWI core cells. The pattern should be interpreted descriptively because the estimate is partly influenced by cell population and JPWI includes a population-density component. Overall, the results show hazard-specific differences in how walkability-based urban spatial characteristics coincide with hazard-zone-based estimated population exposure, providing a transparent and nationally consistent baseline for characterizing designated hazard-zone overlap and estimated exposed population at population core cells in Japan. Full article

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions. Full article

The integration of building information modeling (BIM) and geographic information systems (GISs) is an important area of research aimed at improving interoperability between these domains. These domains often use different concepts for semantics such that non-interoperable vocabularies; schemas; metamodels for semantics; and, in general, non-interoperable IT architectures are used to publish semantic concepts. This study investigates the use of BIM data dictionaries for semantic classification of vector-based geospatial data in GISs, aiming to enable the use of common dictionaries and concepts to describe objects in both domains. The study addresses a particular problem: the fact that the domains use different metaconcepts to describe conceptual information and have different classification methods. The research focuses on identifying significant standards, comparing their metamodels to find similarities and explore the practical use of BIM data dictionaries for the semantic enrichment of GIS features. As a proof of concept, three approaches for the classification of features are developed and validated through implementation in the QGIS software. The results demonstrate that BIM data dictionaries can be used to semantically enrich geospatial data in GISs, with the buildingSMART Data Dictionary (bSDD) serving as a practical example. The conclusions drawn from the study are that although there are limitations and challenges, the integration of BIM data dictionaries into GISs is possible and beneficial for improving interoperability, particularly when cross-domain concepts are employed. Full article

The rapid urbanization and development of vertical construction and subsurface spaces in Iran, along with national policy emphasis on transparency in land administration, have highlighted the need to transition from a two-dimensional (2D) to a three-dimensional (3D) cadastre. Iran’s current cadastral system, based on 2D parcel maps, fails to adequately capture complex ownership relationships in multi-story buildings and underground spaces. This study proposes an integrated model combining the Land Administration Domain Model (LADM) country profile with the Industry Foundation Classes (IFC) standard for 3D cadastre implementation. The LADM country profile of Iran was extended to support 3D spatial units. A 3D building model was developed in Autodesk Revit by integrating as-built cadastral maps with architectural drawings and exported in IFC format. Legal attributes were added to Building Information Modeling (BIM) elements in Simplebim via custom Property Sets, allowing physical and legal information to be stored in one IFC file. Functional and interoperability testing in IFC-compatible software showed that the file could be read and used for legal-spatial queries, although the results may depend on each software’s IFC support. For national use, database-level implementation may provide a more reliable option. These results demonstrate that integrating LADM and BIM for a 3D cadastre is technically feasible in Iran at the building scale, as shown by the successful mapping between LADM classes and IFC entities, implementation of custom Property Sets, and interoperability testing across IFC-compatible software platforms. Full article

Lokoja, the capital of Kogi State, Nigeria, situated at the confluence of the Niger and Benue Rivers, has experienced rapid urban expansion alongside heightened environmental risks, including flooding and ecosystem degradation. Using multi-temporal Landsat imagery (2000, 2010, 2020, 2024), Random Forest classification, and landscape metrics, this study analyses spatio-temporal patterns of urban growth and fragmentation in this underrepresented mid-sized African city. Urban land cover expanded from 6668 ha in 2000 to 15,985 ha in 2024 (net ~140% growth), following a non-linear trajectory of rapid expansion (2000–2010), partial consolidation (2010–2020), and renewed growth with intensified fragmentation (2020–2024). This growth caused severe ecological impacts: dense forest declined by 99.7% (from 373 ha to 1 ha), woodland by 73.9%, and core natural land cover by 23% to 13.8% of the landscape, below critical ecological thresholds. Edge density rose by 121%, exacerbating urban heat, runoff, and biodiversity loss, while apparent gains in grassland largely reflect secondary succession rather than recovery. This study recommends enforcing development restrictions below 10 m in elevation, with 100 m riparian buffers; restoring 500 ha of native corridors; mandating 20% urban tree canopy cover; and establishing community-based green space monitoring. The findings provide empirical insights into sustainability challenges facing mid-sized African cities and offer transferable strategies for ecologically sensitive urban planning. Full article

Interventions implemented in geographic space (geo-interventions) have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To solve this, we produced a framework for transforming spatial data to model potential actions for generating geo-interventions. Finally, we conducted a case study of reducing traffic collisions in Toronto, Canada, to demonstrate the framework, which produced a machine learning model that discovered geo-interventions modifying red light camera, transit shelter, and wayfinding infrastructure predicted to reduce collisions by 5.7%. We highlight the importance of the framework for bridging research and practice through unified understanding, actionable outputs, human guidance, and iterative refinement. With recent advances in big data and artificial intelligence, we envision an acceleration in the discovery of geo-interventions and emergence of interdisciplinary work towards predicting accurate and precise future real-world outcomes at scale. Full article

The operationalization of the One Health (OH) approach remains a major challenge due to persistent fragmentation across human, animal, and environmental data systems. This gap is exacerbated by climate change, which acts as a risk multiplier for pathogen transmission and agri-food system vulnerability. Drawing on more than a decade of research, including the re-emergence of brucellosis in Italy and the 2024 Salmonella Umbilo outbreak, this perspective discusses key weaknesses in current data management, particularly the lack of real-time, interoperable data sharing. To address these challenges, we propose an AI-enabled One Health Information System (OH-IS), grounded in FAIR data principles and privacy-preserving architectures. The proposed conceptual framework integrates multi-matrix data streams, combining Earth observation data, genomic surveillance through whole-genome sequencing (WGS), and livestock mobility within a geospatially integrated architecture to support timely decision-making in vulnerable settings. By analyzing the constraints of siloed databases, we discuss how automated semantic harmonization could conceptually support improved risk assessment and outbreak reconstruction in recent zoonotic events. This approach may facilitate a transition from descriptive to anticipatory surveillance, providing a scalable model to move One Health from a conceptual paradigm toward a more integrated and data-driven surveillance framework aligned with EU digital health policies and global health security priorities. Full article

Open-access satellite data from major Earth observation (EO) missions, including Landsat, Sentinel, and MODIS, have transformed environmental monitoring globally, yet in most least developed countries (LDCs) this data abundance has not translated into operational decisions or policy impact. This review argues that the dominant narrative in the remote sensing community, that open data leads to democratized impact, is fundamentally incomplete. Using Nepal as an illustrative case study, we demonstrate that legal openness alone is insufficient without parallel advances in technical usability and institutional accessibility, the two layers of EO accessibility that the community has largely overlooked. Through a cross-sectoral synthesis spanning forests, agriculture, disaster management, and land cover monitoring, we identify a persistent “last-mile problem”: the systematic gap between data availability and operational governance integration. Systemic barriers including limited internet infrastructure, skills gaps compounded by brain drain, fragmented institutional mandates, and the absence of a national EO coordination mechanism collectively prevent technically sound EO outputs from informing routine planning and policy decisions. Nepal’s small geographic extent, growing digital literacy, and ongoing governance reforms create strategic opportunities for transition, but realizing these requires a functioning geospatial ecosystem integrating data systems, technical infrastructure, human capital, and institutional frameworks. We propose the “Pixels to Policy” framework to operationalize this ecosystem and identify three priority research directions for the global remote sensing community: lightweight data formats for low-bandwidth settings, capacity-aware tool design, and implementation science for EO uptake. These directions reframe the community’s responsibility from delivering open data to ensuring it can be used. Full article

This study examines the context-dependent association between eye-level motor traffic and metro-integrated cycling in Shenzhen, China. To address the limitations of static street-view imagery, we constructed a traffic-informed AIGC-enhanced analytical framework to approximate peak-hour visual motor-traffic conditions. The resulting eye-level motor-traffic measure was incorporated into OLS, GWR, and MGWR models together with land-use, road-network, development-intensity, and streetscape variables. The results show that this measure was positively associated with metro-integrated cycling volume primarily during the weekday morning peak, while the association weakened or became statistically insignificant during evening and weekend periods. We describe this pattern as a commuter’s paradox-like association: visible motor traffic may co-occur with high first-/last-mile cycling demand in high-intensity commuting environments, rather than necessarily deterring cycling. The analysis further suggests a threshold-like land-use pattern in which residential density may act as a background precondition rather than a linear driver during peak hours. This study illustrates the methodological applicability of Geospatial Artificial Intelligence (GeoAI) for addressing static-data limitations and provides planning implications for evaluating station-area feeder cycling environments. 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

Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising 584 unique real-world evaluation instances collected across representative outdoor scenarios. External literature-derived data were utilized strictly as calibration baselines for modeled operational degradation profiles, maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications. Full article