Human-centered GeoAI foundation models: where GeoAI meets human dynamics

Urban Informatics, Journal Year: 2025, Volume and Issue: 4(1)

Published: Feb. 5, 2025

Language: Английский

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Forest fire management, funding dynamics, and research in the burning frontier: A comprehensive review

Trees Forests and People, Journal Year: 2024, Volume and Issue: 16, P. 100526 - 100526

Published: Feb. 29, 2024

We indexed 8,970 scientific publications on forest fires in order to bridge the gap between research and policy discussions fires. Journal articles conference papers dominated literature, with an emphasis environmental science, agricultural biological sciences, earth planetary engineering, computer science. Research field of fire has historically focused terms such as "Forest Fire", "Wildfire", "Deforestation", but recent trends have highlighted "MODIS," "Artificial Intelligence," "Algorithm," "Satellite Data," "Prediction.". The number steadily risen, particularly after 2000, funding predominantly from National Science Foundation, Natural U.S. Forest Service, Aeronautics Space Administration. Notable contributions observed United States, China, Canada, Spain, Australia, India. International Wildland had maximum share published among journals, followed by Ecology Management, Forests, Total Environment, Remote Sensing. A variety aspects been covered, data-driven studies, new discoveries, methodological advances, theoretical applications, governance implications. In spite our long interrelation fires, we are lacking a comprehensive mechanism combat them effectively. multidisciplinary approach collection analysis information could provide insightful tool for evidence-based policies practices aimed address emerging challenges due at global scale.

Language: Английский

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ESFD-YOLOv8n: Early Smoke and Fire Detection Method Based on an Improved YOLOv8n Model

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 303 - 303

Published: Aug. 27, 2024

Ensuring fire safety is essential to protect life and property, but modern infrastructure complex settings require advanced detection methods. Traditional object systems, often reliant on manual feature extraction, may fall short, while deep learning approaches are powerful, they can be computationally intensive, especially for real-time applications. This paper proposes a novel smoke method based the YOLOv8n model with several key architectural modifications. The standard Complete-IoU (CIoU) box loss function replaced more robust Wise-IoU version 3 (WIoUv3), enhancing predictions through its attention mechanism dynamic focusing. streamlined by replacing C2f module residual block, enabling targeted accelerating training inference, reducing overfitting. Integrating generalized efficient layer aggregation network (GELAN) blocks modules in neck of further enhances detection, optimizing gradient paths high performance. Transfer also applied enhance robustness. Experiments confirmed excellent performance ESFD-YOLOv8n, outperforming original 2%, 2.3%, 2.7%, mean average precision ([email protected]) 79.4%, 80.1%, recall 72.7%. Despite increased complexity, outperforms state-of-the-art algorithms meets requirements detection.

Language: Английский

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Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework

Information Fusion, Journal Year: 2024, Volume and Issue: 112, P. 102555 - 102555

Published: July 2, 2024

Language: Английский

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Integrating geospatial intelligence and spatio-temporal modeling for monitoring tourism-related carbon emissions in the United States

Management of Environmental Quality An International Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Purpose This study explores the spatial and temporal relationship between tourism activities transportation-related carbon dioxide (CO 2 ) emissions in United States (US) from 2003 to 2022 using advanced geospatial modeling techniques. Design/methodology/approach The research integrated geographic information systems (GIS) map tourist attractions against high-resolution annual data. analysis covered 3,108 US counties, focusing on county-level attraction densities on-road CO emission patterns. Advanced techniques, including bivariate mapping local testing, were employed assess potential correlations. Findings findings reveal limited evidence of significant associations transportation-based around major urban centers, with decreases observed Eastern states Midwest, particularly non-coastal areas, 2022. Most counties (86.03%) show no statistically changes density emissions. However, 1.90% a positive linear relationship, 2.64% negative 0.29% concave 1.61% convex 7.63% complex, undefined relationship. Despite this, 110% national growth output resource consumption 2003–2022 raises sustainability concerns. Practical implications To tackle issues tourism, policymakers stakeholders can integrate accounting, climate governance. Effective interventions are vital for balancing demands resilience efforts promoting social equity environmental justice. Originality/value study’s innovative application comprehensive provides new insights into complex highlights challenges isolating tourism’s specific impacts underscores need more granular assessments or inventories fully understand footprint.

Language: Английский

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Fostering Post-Fire Research Towards a More Balanced Wildfire Science Agenda to Navigate Global Environmental Change

Fire, Journal Year: 2025, Volume and Issue: 8(2), P. 51 - 51

Published: Jan. 26, 2025

As wildfires become more frequent and severe in the face of global environmental change, it becomes crucial not only to assess, prevent, suppress them but also manage aftermath effectively. Given temporal interconnections between these issues, we explored concept “wildfire science loop”—a framework categorizing wildfire research into three stages: “before”, “during”, “after” wildfires. Based on this partition, performed a systematic review by linking particular topics keywords each stage, aiming describe one quantify volume published research. The results from our identified substantial imbalance landscape, with post-fire stage being markedly underrepresented. Research focusing is 1.5 times (or 46%) less prevalent than that “before” 1.8 77%) “during” stage. This discrepancy likely driven historical emphasis prevention suppression due immediate societal needs. Aiming address overcome imbalance, present perspectives regarding strategic agenda enhance understanding processes outcomes, emphasizing socioecological impacts management recovery multi-level transdisciplinary approach. These proposals advocate integrating knowledge-driven burn severity ecosystem mitigation/recovery practical, application-driven strategies policy development. supports comprehensive spans short-term emergency responses long-term adaptive management, ensuring landscapes are better understood, managed, restored. We emphasize critical importance “after-fire” breaking negative planning cycles, enhancing practices, implementing nature-based solutions vision “building back better”. Strengthening balanced focused will ability close loop involved improve alignment international agendas such as UN’s Decade Ecosystem Restoration EU’s Nature Law. By addressing can significantly restore ecosystems, resilience, develop suited challenges rapidly changing world.

Language: Английский

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Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach

Fire, Journal Year: 2025, Volume and Issue: 8(4), P. 121 - 121

Published: March 21, 2025

This study was conducted to precisely map burned areas in fire-prone forest regions of İzmir and analyze the spatial distribution wildfires. Using Sentinel-2 satellite imagery, burn severity first classified using dNBR dNDVI indices. Subsequently, machine learning (ML) algorithms—RF, XGBoost, LightGBM, AdaBoost—were employed classify unburned areas. To enhance model performance, hyperparameter optimization applied, results were evaluated multiple accuracy metrics. found that RF achieved highest with an overall 98.0% a Kappa coefficient 0.960. In comparison, classification based solely on spectral indices resulted accuracies 86.6% (dNBR) 81.7% (dNDVI). A key contribution this is integration Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP) analysis, which used interpret influence environmental variables area classification. SHAP analysis made decision processes transparent identified dNBR, dNDVI, SWIR/NIR bands as most influential variables. Furthermore, analyses confirmed variations reflectance across fire-affected are critical for accurate delineation, particularly heterogeneous landscapes. provides scientific framework post-fire ecosystem restoration, fire management, disaster strategies, offering decision-makers data-driven effective intervention strategies.

Language: Английский

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Unveiling the thermal impact of land cover transformations in Khuzestan province through MODIS satellite remote sensing products

Paddy and Water Environment, Journal Year: 2024, Volume and Issue: 22(4), P. 503 - 520

Published: June 5, 2024

Language: Английский

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Large Language Models for UAVs: Current State and Pathways to the Future

IEEE Open Journal of Vehicular Technology, Journal Year: 2024, Volume and Issue: 5, P. 1166 - 1192

Published: Jan. 1, 2024

Language: Английский

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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Iran Journal of Computer Science, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Language: Английский

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