International Journal of Applied Earth Observation and Geoinformation最新文献

Images and deep learning in human and urban infrastructure interactions pertinent to sustainable urban studies: Review and perspective 与可持续城市研究相关的人类和城市基础设施互动中的图像和深度学习：回顾和观点

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2025-01-05 DOI: 10.1016/j.jag.2024.104352

Pengxiang Su, Yingwei Yan, Hao Li, Hangbin Wu, Chun Liu, Wei Huang

{"title":"Images and deep learning in human and urban infrastructure interactions pertinent to sustainable urban studies: Review and perspective","authors":"Pengxiang Su, Yingwei Yan, Hao Li, Hangbin Wu, Chun Liu, Wei Huang","doi":"10.1016/j.jag.2024.104352","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104352","url":null,"abstract":"As global urbanization intensifies, conflicts between humans and urban infrastructure increasingly affect socio-economic and environmental sustainability. Recently, using image data and deep learning to investigate the interactions between humans and urban infrastructure has been a popular approach since the fast development of Artificial Intelligence (AI). However, the convergence of data fusion, deep learning, and human-urban infrastructure interaction studies remains underexplored. Here we systematically analyze 3,552 papers from 2013 to 2023 that use image data to investigate the intersection area of data fusion, deep learning, and human and urban infrastructure interactions, aiming to elucidate the relationships among these three key elements. We found that the cross-applications of deep learning in the papers reviewed are not standardized. Given the trend of diversified data fusion, data fusion about real-world dynamic interactions is scarce. Lastly, four potential future research directions are identified: (1) understanding the dynamic and complex interaction processes; (2) exploring the potential and standards for the application of deep learning; (3) focusing more on research concerning cities in the Global South; (4) establishing suitable training datasets for the interaction between urban infrastructures and humans, which may provide valuable insights for applying foundation models in future urban studies.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"130 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic seismic source modeling of InSAR displacements InSAR位移的自动震源建模

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103445

S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari

引用次数: 0

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States. 基于深度学习的美国精细分辨率建筑用地数据的空间显式精度评估。

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 Epub Date: 2023-08-28 DOI: 10.1016/j.jag.2023.103469

Johannes H Uhl, Stefan Leyk

{"title":"Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.","authors":"Johannes H Uhl, Stefan Leyk","doi":"10.1016/j.jag.2023.103469","DOIUrl":"10.1016/j.jag.2023.103469","url":null,"abstract":"<p><p>Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.</p>","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Optimal spectral index and threshold applied to Sentinel-2 data for extracting impervious surface: Verification across latitudes, growing seasons, approaches, and comparison to global datasets 应用于Sentinel-2数据提取不透水地表的最佳光谱指数和阈值:跨纬度、生长季节、方法的验证，以及与全球数据集的比较

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103470

Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev

引用次数: 0

The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions 无人机(UAV)测量和GIS在游憩步道条件分析与监测中的应用

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103474

A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz

引用次数: 1

A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability 基于多尺度变换和生成对抗网络的偏振光谱融合框架提高水体和不同植被的可分辨性

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103468

Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang

引用次数: 0

HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification HCPNet:基于少拍遥感影像场景分类的判别原型学习

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103447

Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu

引用次数: 1

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103467

Shen Ying, Xinyue Zhang, Meng Wang, Han Guo

引用次数: 0

On the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments 双极化SAR观测不同火山环境下熔岩流的能力研究

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103471

E. Ferrentino, C. Bignami, F. Nunziata, S. Stramondo, M. Migliaccio

引用次数: 0

Object-Detection from Multi-View remote sensing Images: A case study of fruit and flower detection and counting on a central Florida strawberry farm 多视点遥感图像中的目标检测:以佛罗里达州中部草莓农场的水果和花卉检测和计数为例研究

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103457

Caiwang Zheng, Tao Liu, A. Abd-Elrahman, V. Whitaker, B. Wilkinson

引用次数: 0