International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"A physics-based remote sensing framework for forest fire smoke detection toward early fire warning","authors":"Yehan Sun, Jun Pan, Lijun Jiang, You Tian, Jiayi Zhang, Kaifeng Liu","doi":"10.1016/j.jag.2026.105124","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105124","url":null,"abstract":"Forest fire smoke significantly disrupts regional carbon cycles and degrades air quality, highlighting an urgent need for highly sensitive detection and inversion models to improve early warning systems. This study proposes a physics-based remote sensing framework for smoke detection and concentration inversion, founded on scattering–absorption theory. By employing multi-dimensional spectral point cluster analysis based on a smoke scattering–absorption model, we introduce the Mahalanobis Distance (MD) as a physically interpretable measure of spectral deviation. A physical relationship between MD and smoke concentration is derived, which serves as the foundation for a dual-threshold detection algorithm and an inversion model. The proposed approach is validated through laboratory experiments, UAV-based simulations, and satellite observations. Key findings include: (1) Smoke clusters migrate quasi-linearly from background to the ideal smoke point with increasing concentration, while their spatial extent contracts exponentially; (2) MD exhibits a double-exponential correlation with smoke concentration (RMSE = 0.18 g/m<ce:sup loc=\"post\">2</ce:sup>, R<ce:sup loc=\"post\">2</ce:sup> = 0.80); (3) Detection accuracy reaches 0.89–0.97 (UAV) and 0.93 (satellite), with fire localization errors of 0.52 ± 0.35 m and 45.47 ± 42.22 m, respectively. These results confirm that spectral variation is a core parameter for smoke retrieval, and MD provides a robust, physically grounded measure for detection and inversion. The proposed model overcomes key limitations of empirical threshold methods and offers a scalable solution for early forest fire monitoring across platforms.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"31 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047822","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":"Monitoring of mangrove dynamic change with Landsat time series from 1987 to 2022 in the Beibu Gulf, China","authors":"Qinglin Yang, Yao Guan, Haixu He, Suzhen Yang, Haonan Sun, Jining Yan","doi":"10.1016/j.jag.2026.105128","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105128","url":null,"abstract":"Mangroves in the Beibu Gulf of the northern south China sea are vital for coastal protection and carbon storage but have undergone pronounced spatial changes over recent decades due to rapid urbanization and climate variability. Existing monitoring efforts, largely constrained to annual temporal resolution, fail to capture seasonal dynamics and fine-scale transformations induced by human activities. In addition, existing change detection methods are either based on statistical machine learning or fully convolutional deep learning, which have limited ability to detect long-term dynamic changes in mangrove forests. To address these challenges, a temporal semantic segmentation change detection with multi-scale dilated convolutions (TSSCD-D) framework was proposed, and the Landsat time series (1987–2022) was used for high-resolution mangrove dynamics monitoring. The proposed approach expands the temporal receptive field, enabling improved representation of long-term changes. Model evaluation demonstrates an overall accuracy of 87.3%, with producer’s and user’s accuracies for mangroves of 97.1% and 96.8%, outperforming other methods (see Table 2). Analysis reveals a sharp decline in mangrove area from 856.1 km<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> in 1987 to 532.8 km<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> in 1998, primarily driven by reclamation and infrastructure development, followed by gradual recovery to 824.3 km<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> by 2022 under conservation policies. Transition mapping shows that conversions with water bodies, marshes, and irrigated croplands account for 80% of total changes, indicating strong hydrological and anthropogenic influences. These findings provide critical insights and fine-scale datasets to support long-term mangrove conservation and management in tropical coastal systems.More details on TSSCD-D can be found at <ce:inter-ref xlink:href=\"https://github.com/CUG-BEODL/TSSCD-D\" xlink:type=\"simple\">https://github.com/CUG-BEODL/TSSCD-D</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"143 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047821","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":"Integrating XGBoost and SHAP to uncover feature contributions for river network selection across different patterns","authors":"Huafei Yu, Min Yang, Xiang Lv, Tinghua Ai, Jingzhong Li","doi":"10.1016/j.jag.2026.105120","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105120","url":null,"abstract":"River network selection is essential for generating multiscale river network datasets used in earth observation (EO) applications, such as hydrological analysis and terrain modeling. However, how individual river features influence selection decisions, especially across different drainage patterns, remains unclear. To address this gap, this study introduces an explainable artificial intelligence framework that integrates eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) to uncover feature contributions for river network selection across different patterns. First, river strokes were constructed as analytical units and multiple geometric, topological, and hydrological features were extracted. Then, XGBoost-based selection models were trained for the parallel and rectangular drainage networks. Finally, the macro-, meso-, and micro-level SHAP analysis was conducted over the trained selection models. The experimental results reveal that features fall into three categories for the two types of patterns considered here: universal features (represented by upstream cumulative area, Horton code) that dominate selection regardless of pattern; pattern-sensitive features (represented by confluence angle, river proximity distance) whose influence varies with drainage patterns; and low-contribution features with negligible contribution. These findings explain why certain rivers are retained or removed across different drainage patterns, providing explainable insights to support automated, pattern-preserving generation of multiscale river networks.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"288 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047825","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":"Geophysical validation of vegetation indices for subsurface detection: Evidence for the utility of red/blue reflectance ratios","authors":"Adam M. Morley, Tamsin A. Mather, David M. Pyle, J-Michael Kendall","doi":"10.1016/j.jag.2026.105107","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105107","url":null,"abstract":"The remote detection of shallow, subsurface features can assist archaeological prospecting, environmental risk mitigation and national security. However, few subsurface detection studies have used geophysical methods to statistically validate the detection efficiency of broadband vegetation indices (VIs) in high-resolution multispectral satellite imagery. In this study, we use 37 broadband VIs on eight Maxar (now rebranded to Vantor) multispectral satellite images to remotely detect stressed meadow grass and anomalous soil characteristics over an Iron Age fogou (stone-walled underground passage) in Carn Euny, Cornwall, UK. Using Maxar’s high spatial resolution, the highest performing VIs are identified using a two-tier geophysical approach. First, we correlate the VIs with gravity and then, for a best performing image subset, we perform structural similarity index measurements (SSIMs) and 2D cross-correlations with ground penetrating radar (GPR) data. In doing so, we reprioritise the most suitable VIs for shallow, subsurface detection in temperate, grass covered environments. In summer months, the Iron Oxide index, Soil Salinity Index 7 (SI7) and the Structure Insensitive Pigment Index (SIPI) are most responsive across the fogou; all of which are algebraic manifestations of the Red/Blue reflectance ratio. By analysing their spectral profiles, gradient magnitudes, false colour composites (FCCs) and edge effects, we review the fogou’s effect on soil salinity, iron oxide concentration and chlorophyll production. Demonstrating the broader utility of Red/Blue reflectance ratios, we then present a multispectral image processing workflow which is calibrated to detect, at scale, shallow subsurface features in temperate, vegetated terrain.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"178 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047823","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":"Quantifying vertical heterogeneity of forest gross primary production using hyperspectral and LiDAR data","authors":"Zixi Shi, Shuo Shi, Peiqi Yang, Wei Gong, Chenxi Liu, Binhui Wang, Jiayun Niu, Minghui Wu","doi":"10.1016/j.jag.2026.105094","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105094","url":null,"abstract":"Forests exhibit complex vertical structures, and understanding the vertical distribution of gross primary production (GPP) is critical for improving carbon cycle assessments. While previous studies have emphasized the contribution of understory, approaches such as eddy-covariance provide only site-level observations and are constrained by sparse spatial coverage and footprint limitations, leaving large-scale assessment and mapping unresolved. This study identified key parameters of GPP vertical heterogeneity and developed a remote sensing framework to quantify and map its distribution in forests. We integrated hyperspectral imagery and light detection and ranging (LiDAR) point clouds to retrieve physiological and biochemical parameters as well as the vertical distribution of canopy structure, which were used to drive GPP modeling. Based on the outputs of the SCOPE (Soil Canopy Observation of Photosynthesis and Energy Fluxes) model, we visualized the vertical profile of GPP and quantified the overstory and understory. Sensitivity and mechanistic analyses were performed using simulated data to investigate the effects of vegetation biophysical and biochemical parameters on vertical GPP. We further estimated and validated the vertical distribution of GPP across seven National Ecological Observatory Network (NEON) forests. The results showed that LAI (leaf area index) was the dominant driver of GPP in canopy layers (correlation coefficients = 0.74), with a stronger influence in the overstory than in the understory, and a trade-off relationship observed between layers. Vertical GPP heterogeneity was well captured, with understory contributions ranging from 5.9% to 35.8%, except in sparse-canopy subarctic forests. Model estimates agreed well with flux tower data (understory contribution: correlation coefficient = 0.941, R<ce:sup loc=\"post\">2</ce:sup> = 0.885; total GPP: R<ce:sup loc=\"post\">2</ce:sup> = 0.785). This study offers new insights into the role of understory vegetation in carbon cycling and informs vertical-dimension strategies for forest carbon management.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"5 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047826","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":"Unraveling the controls of unstable slopes in mining-affected coalfields through InSAR observations and multivariate modeling","authors":"Peng Wang, Hongwei Deng, Jielin Li, Zhen Jiang, Guanglin Tian, Yao Liu, Zheng Pan","doi":"10.1016/j.jag.2026.105089","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105089","url":null,"abstract":"Unstable slopes are highly susceptible to external triggers, representing a critical challenge for disaster risk management. However, few studies have systematically developed approaches for their regional-scale identification, and even fewer have explored their controlling factors in mining-affected regions with strong anthropogenic disturbances. To address this gap, this study investigates the composite factors influencing unstable slopes in the Datong Coalfield, China. Specifically, we used the Small Baseline Subset (SBAS) interferometric synthetic aperture radar (InSAR) technique to measure line-of-sight (LOS) surface deformation and subsequently identified unstable slopes as those with significant deformation velocities within a slope-unit framework. To examine the spatiotemporal effects of factors influencing slope instability, we conducted multivariate modeling at both long- and short-term scales. The results reveal that long-term modeling captures broadly consistent contributions of these factors across different periods, whereas short-term modeling highlights distinct seasonal associations with slope instability. Furthermore, cross-wavelet coherence analysis revealed that intermittently unstable slopes exhibit periodic lagged responses to dynamic environmental forcing, underscoring the significant role of environmental forcing in driving deformation of intermittently unstable slopes. Overall, this work provides new insights into the mechanisms of slope instability in mining-affected coalfields and proposes a transferable framework for regional-scale slope monitoring and risk management.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"33 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047868","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 fusion framework of 3D physical model and InSAR monitoring for mining-induced deformation analysis","authors":"Teng Wang, Yunjia Wang, Feng Zhao, Sen Du, Nianbin Zhang, Kewei Zhang, Zhongwei Shen, José Fernández","doi":"10.1016/j.jag.2026.105122","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105122","url":null,"abstract":"Underground resource extraction redistributes subsurface stress, inducing overburden movement and ground deformation. These geomechanical changes can cause ground fissures, slope failures, and collapses, resulting in resource loss, economic damage, safety risks, and environmental degradation. Therefore, accurate monitoring of surface deformation and understanding of deformation propagation mechanisms are crucial for sustainable resource extraction and geohazard mitigation. To this end, a fusion framework is proposed that integrates a three-dimensional physical model with Interferometric Synthetic Aperture Radar (InSAR) technology to monitor surface deformation and investigate deformation propagation. A mining area in China was selected as a case study. Sentinel-1A images were processed using Stacking-InSAR and Small Baseline Subset InSAR (SBAS-InSAR) techniques to derive surface deformation. InSAR-derived deformation was validated against GNSS measurements, yielding mean absolute errors (MAE) of 35.7 mm for Stacking-InSAR and 6.5 mm for SBAS-InSAR. Although SBAS-InSAR exhibits higher precision in coherent areas, it provides missing/invalid measurements in the central high-intensity deformation zone (cumulative deformation <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mo>&gt;</mml:mo></mml:math> 300 mm), indicating limited applicability under strong mining-induced disturbances. Concurrently, a large-scale three-dimensional physical model was constructed, and overburden strain and surface deformation were measured using distributed optical fiber sensing (DOFS) and digital close-range industrial photogrammetry (DCRIP). After scaling to the prototype, the physical-model surface deformation agrees well with the Stacking-InSAR measurements (mean relative error 17%–18%), confirming the reliability of the constructed physical model. Moreover, a prediction model was developed using synergistic monitoring data, achieving a root mean square error (RMSE) of 52.2 mm (i.e., 6.35% of the maximum deformation of 822.4 mm) and a standard deviation (STD) of 51.8 mm (6.30%) relative to the Stacking-InSAR results. These results demonstrate that the proposed framework effectively bridges laboratory-scale observations and field-scale satellite measurements, thereby improving the understanding of underground-to-surface deformation propagation under mining-induced disturbances. Furthermore, this integration enables high-precision estimation of mining-induced deformation and supports coupled surface and underground deformation analysis.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"1 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047824","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":"Bridging optical and SAR images via semantic prompt-guided progressive alignment for rotated cross-domain ship detection","authors":"Longli Ran, Jiaming Li, Haodong Wu, Anqi Wu, Yi He, Qingfeng Guan, Qiqi Zhu","doi":"10.1016/j.jag.2026.105119","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105119","url":null,"abstract":"Ship detection in remote sensing imagery is essential for diverse maritime-related tasks, including ocean surveillance, fisheries management, and environmental assessment. In operational scenarios, optical imagery provides rich texture cues under clear conditions, whereas synthetic aperture radar (SAR) enables reliable observation in nighttime and cloudy weather. However, cross-domain ship detection across optical and SAR modalities is still challenging due to discrepancies in imaging mechanisms, speckle noise, and background clutter, particularly in near-shore scenarios with similar reflection characteristics, together with the arbitrariness of ship orientation. To address these issues, we propose RotCD-Ship, a rotated cross-domain ship detection framework that bridges the domain gap between optical and SAR images while enabling accurate detection of arbitrarily oriented ships. Specifically, a domain knowledge-guided semantic prompt (DKSP) strategy based on SAR physical priors is introduced to suppress background clutter such as ship wakes and coastal interference. To handle modal divergence, we design a progressive feature alignment scheme that combines multi-scale local feature alignment (MSL-align) and global feature alignment (GF-align), enabling transfer of both fine-grained textures and high-level semantics across domains. Furthermore, a coarse-to-fine rotated region of interest (CF-RRoI) generator is developed to enhance localization precision of strip-like ships in SAR images by progressively refining orientation-aware proposals. Extensive evaluations on five public ship detection datasets show that RotCD-Ship significantly outperforms state-of-the-art methods in both accuracy and robustness, achieving an average mAP improvement of 7.5% in the horizontal ship detection task and 5.5% in the oriented ship detection task compared to the best existing methods. In addition, large-scale tests on Gaofen-3 SAR images further verify the strong generalization in dense-ship and complex coastal environments, highlighting the practical applicability of our framework for all-weather maritime monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"41 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047870","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":"Feature replacement and fusion enhance the accuracy of canopy spectral monitoring models for winter wheat leaf water content","authors":"Zhigang Wang, Sha Yang, Qing Liang, Xujing Yang, Meichen Feng, Xiaobin Yan, Xinkai Sun, Mingxing Qin, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song, Yongkai Xie","doi":"10.1016/j.jag.2026.105110","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105110","url":null,"abstract":"Leaf Water Content (LWC) is vital for assessing winter wheat growth. Due to spectral band sensitivity variations, improving spectral feature fitting through preprocessing and optimization is a challenging issue. This study systematically investigates wavelength-dependent LWC sensitivity under multiple preprocessing operators to identify spectral bands highly correlated with winter wheat LWC under different preprocessing methods and evaluate band replacement (representation selection at fixed wavelength) and fusion strategies (constrained union within the same feature-selection method with wavelength de-duplication) for optimizing feature combinations and enhancing model accuracy and robustness. Various feature extraction techniques were applied to identify LWC-correlated spectral bands. Bands replacement (producing “Replacement Features”) and constrained feature fusion (producing “Fusion Features”) were introduced for feature optimization. Multiple modeling methods were employed to assess LWC monitoring performance using initial, replaced, and fused feature sets under a consistent train–test split with training-set cross-validation. The combination of first derivative (FD) preprocessing, Iterative Variable Subset Optimization (IVSO) feature selection, and Backpropagation Neural Network (BPNN) modeling yielded the best monitoring results. Fusion Features selected by IVSO achieved the highest accuracy, evaluated by R<ce:sup loc=\"post\">2</ce:sup>, RMSE, and Akaike information criterion (AIC) (R<ce:sup loc=\"post\">2</ce:sup><ce:inf loc=\"post\">Train</ce:inf> = 0.960, RMSE<ce:inf loc=\"post\">Train</ce:inf> = 0.016, AIC<ce:inf loc=\"post\">Train</ce:inf> = -1010.061; R<ce:sup loc=\"post\">2</ce:sup><ce:inf loc=\"post\">Test</ce:inf> = 0.956, RMSE<ce:inf loc=\"post\">Test</ce:inf> = 0.015, AIC<ce:inf loc=\"post\">Test</ce:inf> = -98.362). Both band replacement and fusion enhanced model robustness and addressed spectral sensitivity issues. This study demonstrated the importance of preprocessing, feature optimization, and modeling in improving LWC monitoring. The proposed multi-preprocessing representation replacement and constrained fusion framework improved LWC estimation accuracy, supporting precision agriculture for winter wheat.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"87 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047828","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":"DEM super-resolution guided by high-resolution remote sensing images using multitask learning","authors":"Wei Liu, Yuhang Zhong, Shida Zhao, Songling Luo, Yongtao Yu, Xiaomei Zhong, Weikai Tan, Haiyan Guan, Hongjie He, Jonathan Li","doi":"10.1016/j.jag.2026.105099","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105099","url":null,"abstract":"High-resolution digital elevation models (DEMs) are critical for applications such as environmental monitoring and urban planning, motivating the development of advanced DEM super-resolution (SR) techniques. While recent methods have shown promising results, effectively exploiting high-resolution remote sensing images (HRSIs) to guide DEM SR remains challenging, and progress has been hindered by the lack of large-scale, open-source benchmark datasets. We propose GSRMTL, a novel and parameter-efficient multi-task learning framework for HRSI-guided DEM SR. Given a low-resolution DEM and a paired HRSI, GSRMTL jointly performs DEM SR and semantic segmentation of the optical imagery, where segmentation acts as an auxiliary task to provide semantic priors for elevation reconstruction. To address the dataset bottleneck, we introduce GDEMSR, the first large-scale benchmark dataset specifically designed for HRSI-guided DEM SR. Extensive experiments on GDEMSR and the RGB-guided depth SR benchmark NYU-v2 demonstrate that GSRMTL consistently outperforms state-of-the-art methods while using significantly fewer parameters, highlighting its effectiveness and practical deployment potential.","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":"146047873","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}