International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"RPDNet: Street-level road pavement damage detection with a real-time anchor-free network","authors":"Jian Kang, Haiyan Guan, Dedong Zhang, Lingfei Ma, Lanying Wang, Yongtao Yu, Linlin Xu, Jonathan Li","doi":"10.1016/j.jag.2025.105070","DOIUrl":"https://doi.org/10.1016/j.jag.2025.105070","url":null,"abstract":"Accurately and timely detecting road pavement damage helps monitor road deterioration extent, thereby guiding maintenance projects and ensuring traffic safety. Nevertheless, due to textural similarity and nested distribution between neighboring pavement damages, as well as the damages with the diversity sizes, irregular shapes, multiple categories, current methods have the limitation in the high-quality detection from road street-level images. To tackle these challenges, this paper develops a novel real-time anchor-free network with a one-stage processing architecture, named RPDNet, for precisely and accurately detecting pavement damages from streel-level road images. First, stacked with a layer-by-layer encoding structure boosted by a deformable fully-attentive module as the backbone extractor, the RPDNet can capture more fine-grained information and generate multiscale strong task-aware semantics, favoring significantly the discrimination noteworthy textural and geometric features. Then, by adopting a multi-level efficient aggregation neck, the RPDNet can promote informative spatial details and integrate the different-level damage encoding features, contributing to the light-weight and optimization of the whole architecture. Afterward, designed with a dual-large kernel module, embedded in a decoupled detection head with anchor-free guidance, the RPDNet can project the ranging dependency of salient and task-oriented pavement damage objects by adaptively aggregating information across large kernels in spatial-domain. Qualitative and quantitative evaluations confirmed that the RPDNet provided a promiseful solution for detecting pavement damages in industrial applications under complex street-level road conditions. Furthermore, comparative analysis with the latest anchor-based and anchor-free alternatives also proved the superiority and generalization of the RPDNet in pavement damage detection tasks. The assessment results displayed that the RPDNet obtained an average <ce:italic>mAP@0.5</ce:italic>, <ce:italic>mAP@0.5:0.95</ce:italic>, <ce:italic>precision</ce:italic>, and <ce:italic>recall</ce:italic> of 69.16%, 44.86%, 72.59%, and 60.41%, respectively, on two dataset. Additionally, we constructed a large-size multi-city road pavement damage image dataset to support urban road health monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"288 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047912","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}

{"title":"Recognition of salt-marsh fairy circles in conventional optical satellite imagery: A generalizable framework with multiple machine learning models and imbalanced Bayesian probability updating","authors":"Jianru Yang, Hao Zheng, Weiwei Sun, Yuekai Hu, Weiguo Zhang, Chunpeng Chen, Yunxuan Zhou, Heqin Cheng, Weiming Xie, Kai Tan","doi":"10.1016/j.jag.2026.105101","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105101","url":null,"abstract":"Salt-marsh Fairy circles (FC) are enigmatic, quasi-circular structures linked to interacting biogeophysical processes, yet they remain difficult to detect and quantify at scale from conventional RGB imagery. Limited labeled data, transient and variable FC appearance, and severe class-imbalance make single-model machine learning (ML) unreliable for quantitative monitoring. We propose a framework for automatic FC recognition and enumeration on 3-band imagery. A zero-shot foundation model (SAM) segments images into instance-level blocks. Novel distribution-pattern and geometric features, class-equalized losses, weighted resampling, and augmentation are applied within deep-learning (U-Net, Attention-U-Net, Swin-Unet) and ensemble-learning (Random Forest, XGBoost) models. The key innovation is an imbalance-aware Bayesian method that fuses pixel-wise probabilities across models; a counting algorithm then tallies FC instances. We evaluate eight pan-sharpened scenes covering four sites along China’s coast. No individual ML model or standard Bayesian fusion is fully satisfactory. The imbalance-aware Bayesian method improves over the best single model: tight scheme: κ rises from 0.69 to 0.76, F1-score from 70.9% to 75.8% (Class 1) and from 63.5% to 68.2% (Class 2), and AUC from 84.8% to 93.1% and from 78.5% to 84.8%; loose scheme: κ increases from 0.74 to 0.79, AUC from 85.1% to 90.3%, F1-score from 74.3% to 78.6%. The counting algorithm achieves RMSE 1.62 and MAPE 0.33% over 1,135 instances, outperforming DBSCAN. A 22-month case study on Chongming Island captures marsh expansion and dieback dynamics through shifts between FC classes. Our framework delivers reliable FC recognition and enumeration on a small dataset with severe class-imbalance, generalizing across salt-marsh types.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"14 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047827","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}

{"title":"DGL-RSIS: Decoupling global spatial context and local class semantics for training-free remote sensing image segmentation","authors":"Boyi Li, Ce Zhang, Richard M. Timmerman, Wenxuan Bao","doi":"10.1016/j.jag.2026.105113","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105113","url":null,"abstract":"The emergence of vision language models (VLMs) bridges the gap between vision and language, enabling multimodal understanding beyond traditional visual-only deep learning models. However, transferring VLMs from the natural image domain to remote sensing (RS) segmentation remains challenging due to the large domain gap and the diversity of RS inputs across tasks, particularly in open-vocabulary semantic segmentation (OVSS) and referring expression segmentation (RES). Here, we propose a training-free unified framework, termed DGL-RSIS, which decouples visual and textual representations and performs visual-language alignment at both local semantic and global contextual levels. Specifically, a Global–Local Decoupling (GLD) module decomposes textual inputs into local semantic tokens and global contextual tokens, while image inputs are partitioned into class-agnostic mask proposals. Then, a Local Visual–Textual Alignment (LVTA) module adaptively extracts context-aware visual features from the mask proposals and enriches textual features through knowledge-guided prompt engineering, achieving OVSS from a local perspective. Furthermore, a Global Visual–Textual Alignment (GVTA) module employs a global-enhanced Grad-CAM mechanism to capture contextual cues for referring expressions, followed by a mask selection module that integrates pixel-level activations into mask-level segmentation outputs, thereby achieving RES from a global perspective. Experiments on the iSAID (OVSS) and RRSIS-D (RES) benchmarks demonstrate that DGL-RSIS outperforms existing training-free approaches. Ablation studies further validate the effectiveness of each module. To the best of our knowledge, this is the first unified training-free framework for RS image segmentation, which effectively transfers the semantic capability of VLMs trained on natural images to the RS domain without additional training.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"30 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047869","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}

{"title":"Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain","authors":"Xiaoping Zhang, Bo Cheng, Peng Huang, Chenbin Liang, Min Zhao, Guizhou Wang, Qinxue He, Yaocan Gan","doi":"10.1016/j.jag.2026.105123","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105123","url":null,"abstract":"Plastic greenhouses (PGs), as a typical form of facility agriculture, play a crucial role in stabilizing agricultural production and increasing crop yields, but their rapid expansion has raised environmental concerns. Accurate long-term PGs monitoring is therefore essential for scientific agricultural regulation and environmental sustainability. However, most existing studies have focused on local regions or single-year mapping, and long-term PGs mapping remains limited. Moreover, acquiring multi-year high-quality training samples and developing effective classification algorithms remain major challenges for reliable PGs extraction. To address these issues, we propose a novel PGs mapping framework that integrates automatic sample generation with multi-temporal noise correction (MTNC), and utilizes Landsat time-series images to efficiently and accurately map multi-year PGs distribution in the Huang-Huai-Hai Plain. Specifically, high-quality training samples were automatically generated from multi-source land use/land cover and PGs products through spatial rules and sample migration, followed by preliminary classification with Random Forest. The initial predictions were then refined through the MTNC strategy, and the optimized labels were subsequently employed to train a segmentation network for robust PGs extraction. Accuracy assessments on two independent validation datasets demonstrate that the final PGs maps achieve overall accuracies above 90% and Kappa coefficients greater than 0.8 across all years. And cross-comparisons with existing PGs products at multiple spatial resolutions show a high level of spatial consistency (<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> = 0.91 with PGs-10 and 0.74 with PGs-3), further confirming the reliability of the proposed framework and the high quality of the final products.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"67 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047871","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}