Pub Date : 2025-12-08DOI: 10.1016/j.jag.2025.105001
Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Magdalena Main-Knorn, Leonardo Inforsato, Claas Nendel
{"title":"Corrigendum to “Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models” [Int. J. Appl. Earth Obs. and Geoinf. 144C (2025) 104918]","authors":"Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Magdalena Main-Knorn, Leonardo Inforsato, Claas Nendel","doi":"10.1016/j.jag.2025.105001","DOIUrl":"https://doi.org/10.1016/j.jag.2025.105001","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"14 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759433","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 : 2025-11-08DOI: 10.1016/j.jag.2025.104945
E.T. Mendoza, I. Turki, E. Ojeda, A. Soloy, E. Salameh, C. Lopez-Solano, J. Deloffre, N. Lecoq
This paper investigates morphodynamics of a macrotidal gravel beach (Etretat, Normandy, NW France) during two consecutive winter seasons characterized by moderate (2018–2019) and exceptionally energetic (2019–2020) conditions according to a 42-year analysis of hindcasted wave data. Upper intertidal beach changes and shoreline data were derived from a video monitoring system. The results show that storm impacts were strongly influenced by the storm characteristics, the tide phasing, and more importantly, by the pre-existing conditions of the beach before the impact of the storms. A multiple regression including wave height and tide range during the storm event explained 46% of the variance of the volume change due to storm events. Storm clusters often cause beach erosion but can also contribute significantly to beach recovery.
{"title":"Storm response of the upper intertidal zone on a gravel beach","authors":"E.T. Mendoza, I. Turki, E. Ojeda, A. Soloy, E. Salameh, C. Lopez-Solano, J. Deloffre, N. Lecoq","doi":"10.1016/j.jag.2025.104945","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104945","url":null,"abstract":"This paper investigates morphodynamics of a macrotidal gravel beach (Etretat, Normandy, NW France) during two consecutive winter seasons characterized by moderate (2018–2019) and exceptionally energetic (2019–2020) conditions according to a 42-year analysis of hindcasted wave data. Upper intertidal beach changes and shoreline data were derived from a video monitoring system. The results show that storm impacts were strongly influenced by the storm characteristics, the tide phasing, and more importantly, by the pre-existing conditions of the beach before the impact of the storms. A multiple regression including wave height and tide range during the storm event explained 46% of the variance of the volume change due to storm events. Storm clusters often cause beach erosion but can also contribute significantly to beach recovery.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"29 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485616","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 : 2025-11-07DOI: 10.1016/j.jag.2025.104956
Elisavet Tsiranidou, Patricia González-Cabaleiro, Antonio Fernández, Lucía Díaz-Vilariño
{"title":"Corrigendum to “A framework for road space extraction from point clouds and integration into 3D city models.” [Int. J. Appl. Earth Obs. Geoinf. 143 (2025) 104803]","authors":"Elisavet Tsiranidou, Patricia González-Cabaleiro, Antonio Fernández, Lucía Díaz-Vilariño","doi":"10.1016/j.jag.2025.104956","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104956","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"2 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485617","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 : 2025-08-10DOI: 10.1016/j.jag.2025.104785
Chenyang He, Jia Song
Accurate identification of winter wheat from remote sensing imagery is crucial for large-scale agricultural monitoring. Despite the success of Transformer-based deep learning models in various fields, their application in crop identification has been limited by the scarcity of extensive labeled training data. This study proposes a dual-branch spatio-temporal Transformer (DST-Transformer) for winter wheat extraction from Sentinel-2 imagery using a small training dataset. By independently extracting temporal and spatial features, the DST-Transformer effectively delineates crop boundaries and reduces misclassification. Experiments demonstrate its effectiveness with small training datasets, achieving over 90% overall accuracy (OA) and 88.25% mean intersection over union (MIoU) when evaluating on test datasets. The DST-Transformer was further applied to large-scale winter wheat extraction across Shandong Province, China (an area 66 times larger than the training region) to evaluate its cross-regional transferability. Evaluation results showed OA over 92% and MIoU exceeding 85% at all validation sites, highlighting the DST-Transformer’s robustness and strong generalization capability. This study underscores the DST-Transformer’s potential for large-scale crop identification and illustrates the promise of Transformer-based architectures for efficient, high-precision crop mapping with small training datasets, advancing the application of deep learning in agricultural remote sensing.
{"title":"A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets","authors":"Chenyang He, Jia Song","doi":"10.1016/j.jag.2025.104785","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104785","url":null,"abstract":"Accurate identification of winter wheat from remote sensing imagery is crucial for large-scale agricultural monitoring. Despite the success of Transformer-based deep learning models in various fields, their application in crop identification has been limited by the scarcity of extensive labeled training data. This study proposes a dual-branch spatio-temporal Transformer (DST-Transformer) for winter wheat extraction from Sentinel-2 imagery using a small training dataset. By independently extracting temporal and spatial features, the DST-Transformer effectively delineates crop boundaries and reduces misclassification. Experiments demonstrate its effectiveness with small training datasets, achieving over 90% overall accuracy (OA) and 88.25% mean intersection over union (MIoU) when evaluating on test datasets. The DST-Transformer was further applied to large-scale winter wheat extraction across Shandong Province, China (an area 66 times larger than the training region) to evaluate its cross-regional transferability. Evaluation results showed OA over 92% and MIoU exceeding 85% at all validation sites, highlighting the DST-Transformer’s robustness and strong generalization capability. This study underscores the DST-Transformer’s potential for large-scale crop identification and illustrates the promise of Transformer-based architectures for efficient, high-precision crop mapping with small training datasets, advancing the application of deep learning in agricultural remote sensing.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"7 1","pages":"104785"},"PeriodicalIF":7.5,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900684","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.2023.103445
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}
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