Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103445
S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari
{"title":"Automatic seismic source modeling of InSAR displacements","authors":"S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari","doi":"10.1016/j.jag.2023.103445","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103445","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103445"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752467","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}
Pub Date : 2023-09-01Epub Date: 2023-08-28DOI: 10.1016/j.jag.2023.103469
Johannes H Uhl, Stefan Leyk
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.
{"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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103470
Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev
{"title":"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","authors":"Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev","doi":"10.1016/j.jag.2023.103470","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103470","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103470"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752739","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103474
A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz
{"title":"The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions","authors":"A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz","doi":"10.1016/j.jag.2023.103474","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103474","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103474"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752910","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103468
Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang
{"title":"A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability","authors":"Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang","doi":"10.1016/j.jag.2023.103468","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103468","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":"103468"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752653","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103447
Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu
{"title":"HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification","authors":"Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu","doi":"10.1016/j.jag.2023.103447","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103447","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103447"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752584","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103467
Shen Ying, Xinyue Zhang, Meng Wang, Han Guo
{"title":"3D building similarity for a random single-view-image pair based on a local 3D shape","authors":"Shen Ying, Xinyue Zhang, Meng Wang, Han Guo","doi":"10.1016/j.jag.2023.103467","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103467","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"47 1","pages":"103467"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752596","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103471
E. Ferrentino, C. Bignami, F. Nunziata, S. Stramondo, M. Migliaccio
{"title":"On the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments","authors":"E. Ferrentino, C. Bignami, F. Nunziata, S. Stramondo, M. Migliaccio","doi":"10.1016/j.jag.2023.103471","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103471","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103471"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752811","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103457
Caiwang Zheng, Tao Liu, A. Abd-Elrahman, V. Whitaker, B. Wilkinson
{"title":"Object-Detection from Multi-View remote sensing Images: A case study of fruit and flower detection and counting on a central Florida strawberry farm","authors":"Caiwang Zheng, Tao Liu, A. Abd-Elrahman, V. Whitaker, B. Wilkinson","doi":"10.1016/j.jag.2023.103457","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103457","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103457"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54753035","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}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103458
Yan Zhang, Fan Zhang, Libo Fang, Nengcheng Chen
{"title":"Inferring socioeconomic environment from built environment characteristics based street view images: An approach of Seq2Seq method","authors":"Yan Zhang, Fan Zhang, Libo Fang, Nengcheng Chen","doi":"10.1016/j.jag.2023.103458","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103458","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103458"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54753070","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}
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