Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features

Li, Wenwen; Hsu, Chia‐Yu; Wang, Sizhe; Witharana, Chandi; Liljedahl, A. K.

doi:10.1145/3557918.3565869

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The core GeoAI technologies and data sources correspond to (i) Remote Sensing-satellite imagery (optical and radar) provides data on land cover, terrain elevation, soil moisture, and precipitation patterns, feeding into hydrological modeling; (ii) Geographic Information Systems (GISs)-GISs layer on historical flood data, infrastructure locations, population density, and other variables, allowing for vulnerability and risk assessment; (iii) Machine Learning (ML)-algorithms such as Support Vector Machines (SVMs), Random Forests, and Artificial Neural Networks (ANNs) can learn patterns from complex, multi-source data to predict flood occurrences and spatial extents; and (iv) Deep Learning (DL)-Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) excel at handling big data, uncovering subtle spatial-temporal relationships, and improving real-time predictions [67,68].…”

Section: Geospatial Ai Technologies For Flood Predictionmentioning

confidence: 99%

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Rezvani,

Silva,

de Almeida

2024

Sustainability

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Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

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Section: Geospatial Ai Technologies For Flood Predictionmentioning

confidence: 99%

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Rezvani,

Silva,

de Almeida

2024

Sustainability

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“…The main AI tool used is the popular deep learning architecture Mask R-CNN. Li et al developed a real-time deep learning model to segment permafrost features based on a very efficient deep learning architecture-SparseInst [5]. The model achieves predictive performance as good as Mask R-CNN but with a much faster inference speed.…”

Section: Introductionmentioning

confidence: 99%

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping

Li,

Hsu,

Wang

et al. 2024

Remote Sensing

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This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.

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Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection

Hsu

2023

International Journal of Geographical Information Science

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Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features

Cited by 6 publications

References 20 publications

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping

Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection

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