Iterative integration of deep learning in hybrid Earth surface system modelling

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(8), P. 568 - 581

Published: July 11, 2023

Language: Английский

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Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103734 - 103734

Published: March 11, 2024

This paper brings a comprehensive systematic review of the application geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including subdomains cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from Web Science relevant fields select 1516 that identify as studies using GeoAI via scanning conducted by several research groups around world. We outline applications systematically summarising number publications over years, empirical across countries, categories data sources used applications, their modelling tasks different subdomains. find out existing have limited capacity to monitor complex behaviour examine non-linear relationship between its potential drivers—such limits can be overcome models with handle complexity. elaborate on current progress status within each subdomain geography, point issues challenges, well propose directions opportunities for future context sustainable open science, generative AI, quantum revolution.

Language: Английский

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Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 211, P. 1 - 34

Published: April 2, 2024

Language: Английский

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Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125296 - 125296

Published: Jan. 13, 2025

Language: Английский

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

International Journal of Geographical Information Science, Journal Year: 2023, Volume and Issue: 37(5), P. 963 - 987

Published: March 24, 2023

AbstractImproving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open 'black box' complex AI models, such as deep learning. This paper compares popular saliency map generation techniques and their strengths weaknesses in interpreting GeoAI learning models' reasoning behaviors, particularly when applied analysis image processing tasks. We surveyed two broad classes model explanation methods: perturbation-based gradient-based methods. The former identifies areas, which help machines make predictions by modifying a localized area input image. latter evaluates contribution every single pixel model's prediction results through gradient backpropagation. In this study, three algorithms—the occlusion method, integrated gradients class activation method—are examined for natural feature detection task using algorithms' are discussed, consistency between model-learned human-understandable concepts object recognition is also compared. experiments used GeoAI-ready datasets demonstrate generalizability research findings.Keywords: XAIartificial intelligencedeep learningvisualizationGeoAI Disclosure statementNo potential conflict interest was reported author(s).Data codes availability statementThe data that support findings study available at https://github.com/ASUcicilab/explainable-geoai. Instructions on how use provided README file.Additional informationFundingThis work supported part National Science Foundation under [awards 2120943, 2230034, 1853864].Notes contributorsChia-Yu HsuChia-Yu Hsu professional Arizona State University. His interests include intelligence, computer vision, spatiotemporal analysis, applications climate change terrain research.Wenwen LiWenwen Li professor geographic information science University (ASU). Her cyberinfrastructure, big data, data- computation-intensive environmental social sciences. At ASU, she directs Cyberinfrastructure Computational Intelligence Lab (http://cici.lab.asu.edu/) serves Research Director Spatial Analysis Center.

Language: Английский

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GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid

ISPRS International Journal of Geo-Information, Journal Year: 2023, Volume and Issue: 12(3), P. 112 - 112

Published: March 7, 2023

The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, wildfires to hurricanes, floods, wars. Providing timely disaster response humanitarian aid these events is a critical topic for decision makers relief experts in order mitigate impacts save lives. When occurs, it important acquire first-hand, real-time information about the potentially affected area, its infrastructure, people develop situational awareness plan address health needs population. This requires rapid assembly multi-source geospatial data that need be organized visualized way support disaster-relief efforts. In this paper, we introduce new cyberinfrastructure solution—GeoGraphVis—that empowered by knowledge graph technology advanced visualization enable intelligent making problem solving. There are three innovative features solution. First, location-aware created link integrate cross-domain make analytics-ready. Second, expert-driven workflows analyzed modeled as machine-understandable paths guide exploration via graph. Third, scene-based strategy developed interactive heuristic visual analytics better comprehend impact situations action plans aid.

Language: Английский

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Artificial intelligence in civil engineering

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 74

Published: Jan. 1, 2024

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Position-Aware Graph-CNN Fusion Network: An Integrated Approach Combining Geospatial Information and Graph Attention Network for Multiclass Change Detection

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 16

Published: Jan. 1, 2024

Urban change detection (CD) is crucial for informed decision-making but faces various challenges, including complex features, rapid changes, and extensive human interventions. These challenges underscore the urgent need innovative multiclass CD (MCD) techniques that extensively incorporate deep learning (DL). Despite several successes achieved with DL-based MCD methods, still certain shortcomings persist, disregard spatial principles, which significantly hinders seamless integration of geoscience-knowledge artificial-intelligence. In this article, a novel DL model known as position-aware graph-convolutional neural network (CNN) fusion (PGCFN) introduced, integrating position encoding to effectively detect urban changes. The model's first part encodes geospatial positions following Tobler's law (TFL) geography. It then integrates encoded into an model, combining graph attention (GAT) CNN enhance performance. was tested on 0.5-m resolution remote sensing (RS) images, achieving impressive minimum mean intersection over union (MIoU) score 91.20%. Additionally, module exhibited strong emphasis geographic proximity when evaluating connections between superpixels. Overall, these findings affirm our could addresses enhances geoscience knowledge artificial intelligence (AI).

Language: Английский

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Responsible AI for Cities: A Case Study of GeoAI in African Informal Settlements

Journal of Urban Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 27

Published: Feb. 13, 2025

Language: Английский

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