International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data","authors":"Lei Xu ,&nbsp;Xihao Zhang ,&nbsp;Xi Zhang ,&nbsp;Tingtao Wu ,&nbsp;Hongchu Yu ,&nbsp;Wenying Du ,&nbsp;Zeqiang Chen ,&nbsp;Nengcheng Chen","doi":"10.1016/j.jag.2025.104532","DOIUrl":"10.1016/j.jag.2025.104532","url":null,"abstract":"<div><div>Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction models tend to accumulate error in long-term forecasting and the limited SMAP RZSM samples may result in insufficient model generalization. To address these issues, this study proposes a multi-head self-attention-based autoregressive transfer learning model based on long short-term memory (MAATL) model for sub-seasonal RZSM prediction. The proposed MAATL model is evaluated over the Continental United States (CONUS) for 1- to 60-day RZSM prediction and compared with some ablation and long short-term memory (LSTM) models. The results showed that compared with LSTM, the skills of the MAATL model were significantly improved, with an average correlation coefficient increase of 18.26% and a root mean square error (RMSE) reduction of 42.55%. Furthermore, 118 in-situ soil moisture stations are used for predictive validation and the proposed MAATL model demonstrates higher accuracy compared to the Global Forecast System (GFS) and the LSTM model, with an average correlation skill improvement of 16.02% and 15.08% for MAATL over GFS and LSTM, respectively. These findings indicate superior performance for the proposed MAATL model in sub-seasonal RZSM prediction, which has great potential for agricultural drought preparations.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104532"},"PeriodicalIF":7.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the impact of area of interest (AOI) size and endmember selection on evapotranspiration (ET) estimation through a contextual model (SEBAL)","authors":"Hamza Barguache ,&nbsp;Jamal Ezzahar ,&nbsp;Jamal Elfarkh ,&nbsp;Said Khabba ,&nbsp;Salah Er-Raki ,&nbsp;Valerie Le Dantec ,&nbsp;Mohamed Hakim Kharrou ,&nbsp;Ghizlane Aouade ,&nbsp;Abdelghani Chehbouni","doi":"10.1016/j.jag.2025.104514","DOIUrl":"10.1016/j.jag.2025.104514","url":null,"abstract":"<div><div>Accurate estimation of evapotranspiration (ET) is essential for effective water resource management, particularly in arid and semi-arid areas. Advancements in remote sensing technology have made ET models indispensable, offering high-resolution spatial and temporal assessments. Contextual models such as the Surface Energy Balance Algorithm for Land (SEBAL) are particularly valuable for ET estimation. However, one major challenge for these models is the identification of endmembers representing the wet and dry extremes within the AOI. Furthermore, the influence of AOI size on endmember selection raises important considerations for model performance. This work examines how the size of the AOI and endmember selection impact heat flux estimation using the SEBAL model. The research was conducted in an olive orchard at the Agdal site in Marrakech, from May 2022 to April 2023, and at a rainfed wheat field at the Sidi Rehal site from August 2017 to March 2019, using Landsat imagery (L8 and L9) and ERA5 land reanalysis data. For that, SEBAL was applied to six different AOI, ranging from small and homogeneous areas to the full extent of the Landsat imagery. Based on comparisons of SEBAL estimates with eddy covariance data collected from the Agdal site, the analysis shows that difficulties in accurately identifying endmembers are influenced by the size of the AOI. For homogeneous areas, the model struggles to capture the full range of heat fluxes, leading to poor regression relationships. Conversely, applying a shapefile that covers the entire Landsat imagery led to a more uniform distribution of latent heat flux, especially in winter/spring (when the climatic demand is low), which reduced the model’s ability to capture spatial variability. The AOI, which includes a mix of agricultural areas, bare soil, water bodies, and small towns, and whose boundary is relatively close to the measurement station, yielded the best results. It achieved R2 values of 0.95 for H and 0.88 for LE, with RMSE values of 51.24 and 52.41 W/m² for H and LE, respectively. At the regional scale, the larger AOI size produced the lowest results with greater dispersion at the rainfed wheat site, with RMSE values of 104.99 and 93.30 W/m² for H and LE, respectively. In contrast, segmenting the region into optimal size of AOI produced more accurate results, achieving R2 values of 0.96 for H and 0.92 for LE, with corresponding RMSE values of 56.9 and 35.88 W/m², respectively. These findings emphasize the critical role of AOI size and endmember identification in improving SEBAL model accuracy and enhancing ET estimation for the sustainable management of water resources at both local and regional levels.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104514"},"PeriodicalIF":7.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing change point detection methods to enable robust detection of early stage Artisanal and Small-Scale mining (ASM) in the tropics using Sentinel-1 time series data","authors":"Mensah Isaac Obour ,&nbsp;Barrett Brian ,&nbsp;Cahalane Conor","doi":"10.1016/j.jag.2025.104525","DOIUrl":"10.1016/j.jag.2025.104525","url":null,"abstract":"<div><div>Artisanal and Small-Scale mining (ASM) provides essential livelihoods for many in developing countries but often lacks regulation, leading to environmental issues such as water pollution and deforestation. Timely and accurate mapping of ASM activities is vital for responsible mining that benefits the environment and local communities. Synthetic Aperture Radar (SAR) is crucial for regular ASM monitoring in cloudy regions due to its ability to penetrate clouds. However, atmospheric effects can limit its effectiveness, particularly with shorter wavelengths in wet tropical areas during the rainy season. This study utilised a time series smoothing technique to improve Sentinel-1 (S-1) SAR time series data, reducing SAR noise and atmospheric effects from heavy rainfall for early ASM activity detection. We tested three change point detection (CPD) methods, including cumulative sum (CuSuM), pruned exact linear time (PELT), and binary segmentation (BinSeg) in the Western and Ashanti wet regions in southern Ghana using the smoothed S-1 data for early ASM detection. We observed a relatively fast response of ASM activity tracking when utilising smoothed S-1 data at both sites for VV and VH polarizations during the rainy seasons. However, VH polarization is more effective than VV polarization during rainy seasons. While all CPD algorithms showed similar performance, CuSuM had the shortest lag time of up to 9 days, compared to 11 days for PELT and BinSeg. This method significantly reduces ambiguity caused by heavy rainfall when identifying change points due to ASM activity, making it a viable option for near real-time monitoring in wet tropical regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104525"},"PeriodicalIF":7.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESMII-Net: An edge-synergy and multidimensional information interaction network for remote sensing change detection","authors":"Yixin Chen ,&nbsp;Xiaogang Ning ,&nbsp;Ruiqian Zhang ,&nbsp;Hanchao Zhang ,&nbsp;Xiao Huang ,&nbsp;You He","doi":"10.1016/j.jag.2025.104507","DOIUrl":"10.1016/j.jag.2025.104507","url":null,"abstract":"<div><div>In recent advancements, deep learning-based methods for change detection have demonstrated rapid capabilities to identify alterations across extensive regions, underscoring significant research and application potential in remote sensing change detection. Nonetheless, these methods currently encounter limitations in feature extraction, often leading to blurred edges and challenges in identifying small-scale changes. To overcome these challenges, we introduce the Edge-Synergy and Multidimensional Information Interaction Network (ESMII-Net) specifically designed for remote sensing change detection. We achieve feature enhancement through the Multidimensional Information Interaction Fusion Module (MIIFM) and, by integrating the edge aware decoder and the Edge-Synergy Module (ESM), guide the model to acquire effective edge information, thereby improving change detection performance. Furthermore, during the loss function formulation, we have incorporated a Small Object Enhancement Factor (SOEF) to prioritize small object detection. An edge-awareness map is also utilized within the model to accurately delineate change edges and assess their influence on adjacent changed pixels. The efficacy of our model and its innovative components has been validated through experimental results on two public datasets, showcasing improved capabilities in detecting edges and small objects.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104507"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DMP-PUNet: A novel network for two-dimensional InSAR phase unwrapping under severe noise and complex fringes conditions","authors":"Yu Chen ,&nbsp;Shuai Wang ,&nbsp;Yandong Gao ,&nbsp;Yanjian Sun ,&nbsp;Jinqi Zhao ,&nbsp;Kun Tan ,&nbsp;Peijun Du","doi":"10.1016/j.jag.2025.104519","DOIUrl":"10.1016/j.jag.2025.104519","url":null,"abstract":"<div><div>In the processing of Interferometric synthetic aperture radar (InSAR) data, two-dimensional (2-D) phase unwrapping (PU) is crucial for ensuring the quality of InSAR data inversion. Traditional methods, based on the assumption of phase continuity, often struggle with abrupt terrain changes and the influence of severe noise, leading to poor performance or failure. To address these challenges, this paper presents a dilated multi-path phase unwrapping network (DMP-PUNet) for 2-D PU under conditions of severe noise and complex fringes. To train this model, we developed a multi-effect interferometric phase simulation (ME-IPS) strategy that aims to simulate interferometric phases that closely resemble real-world conditions by comprehensively considering various factors, including terrain and digital elevation model (DEM) errors, atmospheric turbulence, vegetation effects, baseline geometry, multiple scattering, and noise. This simulation, combined with quasi-real interferometric phase data obtained from DEM inversion algorithms, forms the comprehensive training dataset. Finally, experiments on simulated data, quasi-real data, the InSAR-DLPU dataset, and InSAR data demonstrate that DMP-PUNet outperforms existing methods. For simulated data, DMP-PUNet achieved an overall average mean absolute error (MAE) in residuals of 0.221 rad, improving accuracy by 54.75 % with an average processing time of 0.81 s. For quasi-real data, the average MAE was 0.320 rad, a 119.06 % increase in accuracy, with an average processing time of 0.82 s. For the InSAR-DLPU dataset, overall, the MAE of DMP-PUNet was 20.34 % to 64.96 % lower than that of the best-performing baseline method (DLPU), with an average processing time of 1.90 s. For InSAR data, DMP-PUNet performed stably, with lower noise levels, smooth phase transitions, and deformation spatial patterns and profile shapes that conform to the laws of mining subsidence, averaging a processing time of 1.71 s, outperforming existing methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104519"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-area urban TomoSAR method with limited a priori knowledge and a complex deep learning model","authors":"Haoxuan Duan ,&nbsp;Yuzhou Liu ,&nbsp;Hong Zhang ,&nbsp;Peifeng Ma ,&nbsp;Zhongqi Shi ,&nbsp;Zihuan Guo ,&nbsp;Yixian Tang ,&nbsp;Fan Wu ,&nbsp;Chao Wang","doi":"10.1016/j.jag.2025.104521","DOIUrl":"10.1016/j.jag.2025.104521","url":null,"abstract":"<div><div>Buildings are crucial to cities, and tomographic synthetic aperture radar (TomoSAR) is an important tool for monitoring the heights, linear deformations and thermal amplitudes of buildings. However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps, complicating the process. Additionally, the computational inefficiency of existing algorithms significantly hinders the large-scale practical deployment of TomoSAR. To address the above issues, this study proposes a novel large-area urban TomoSAR method integrating limited a priori knowledge constraints with a complex-valued (CV) deep learning model. By refining scatterer types and Permanent Scatterer (PS) height sample sets under limited a priori height data constraints, the proposed CV-TomoPS-Net establishes an end-to-end framework for scatterer classification and PS height regression. Additionally, the proposed fast beamforming method, paired with an adaptive spatial search mechanism, enables rapid large-area inversion of deformation and thermal amplitude parameters. Experiments were conducted in Shenzhen city using COSMO-SkyMed SAR data from 2020 to 2023 and limited a priori data. Results show that the proposed method improves the accuracy of scatterer type classification by 16 %, reduces the height calculation error by 30 %, and improves the monitoring efficiency by 80 % compared with the traditional beamforming method. Validation via corner reflectors deformation monitoring confirmed reliability, with a 1.5 mm average error. These results highlight the practical applicability of the proposed method for large-scale urban monitoring and its potential to provide technical support for sustainable urban development.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104521"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BANet: A bilateral attention network for extracting changed buildings between remote sensing imagery and cadastral maps","authors":"Qingyu Li ,&nbsp;Lichao Mou ,&nbsp;Yilei Shi ,&nbsp;Xiao Xiang Zhu","doi":"10.1016/j.jag.2025.104486","DOIUrl":"10.1016/j.jag.2025.104486","url":null,"abstract":"<div><div>Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works focus on extracting all buildings from remote sensing images, we argue that these methods not only disregard preliminary information on cadastral maps but also fail to preserve building priors in unchanged areas on cadastral maps. Therefore, we focus on the task of extracting changed buildings (i.e., newly built and demolished buildings) from remote sensing images and cadastral maps. To address this task, we create an image-map building change detection (IMBCD) dataset, formed by around 27K pairs of remote sensing images and maps and their corresponding changed buildings in six distinct geographical areas across the globe. Accordingly, we propose a Bilateral Attention Network (BANet), introducing a novel attention mechanism: changed-first (CF) attention and non-changed-first (NCF) attention. This bilateral attention mechanism helps to refine the uncertain areas between changed and non-changed regions. Extensive experiments on our IMBCD dataset showcase the superior performance of BANet. Specifically, our BANet outperforms state-of-the-art models with F1 scores of 90.00% and 63.00% for the IMBCD-WHU and IMBCD-Inria datasets. This confirms that the leverage of bilateral attention blocks (BAB) can boost performance.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104486"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion","authors":"Menghui Jiang ,&nbsp;Tian Qiu ,&nbsp;Ting Wang ,&nbsp;Chao Zeng ,&nbsp;Boxuan Zhang ,&nbsp;Huanfeng Shen","doi":"10.1016/j.jag.2025.104517","DOIUrl":"10.1016/j.jag.2025.104517","url":null,"abstract":"<div><div>Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to tackle this challenge, we propose a deep learning-based spatiotemporal fusion framework to integrate the two data sources and generate a global soil moisture product with high spatial continuity and accuracy. Specifically, we leverage the high accuracy of the Soil Moisture Active and Passive (SMAP) microwave soil moisture data and the spatiotemporal continuity of the Noah assimilation soil moisture data. The proposed model employs a deep residual cycle GAN (DrcGAN) to capture the nonlinear complementary spatiotemporal features between the SMAP and Noah data, generating a seamless global daily product at a 36-km resolution, spanning April 4, 2015, to November 26, 2023, referred to as STSG-SM. Various validation methods, including spatial pattern analysis, time-series comparison, and in-situ validation, are utilized to assess the effectiveness and reliability of the product. In comparison to the selected in-situ measurements, the STSG-SM dataset (original SMAP-P₃₆) exhibits a bias of 0.0230 m³/m³ (0.0243 m³/m³), R of 0.8388 (0.8405), RMSE of 0.0629 m³/m³ (0.0628 m³/m³), and ubRMSE of 0.0585 m³/m³ (0.0579 m³/m³), indicating that the proposed method sustains the high precision of satellite-retrieved soil moisture and demonstrates strong consistency with the in-situ measurements.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104517"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery","authors":"Haimei Lei ,&nbsp;Nisha Bao ,&nbsp;Moli Yu ,&nbsp;Yue Cao","doi":"10.1016/j.jag.2025.104512","DOIUrl":"10.1016/j.jag.2025.104512","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO&lt;sub&gt;2&lt;/sub&gt;). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. This study aimed to quantify and map the spatial distribution of total Fe (TFe) and SiO&lt;sub&gt;2&lt;/sub&gt; contents of tailings dams at the largest iron cluster in China using laboratory spectra and GF-5 hyperspectral images. A total of 230 samples were collected from the surface of 11 tailings dams and scanned by a VIS–NIR–SWIR reflectance spectrometer in the laboratory. A novel spectral index was developed through a multi-objective programming methodology. This novel index utilizes band ratios to identify the optimal combination of spectral bands that show a strong correlation with concentrations of TFe and SiO&lt;sub&gt;2&lt;/sub&gt;. Simultaneously, it minimizes the impact of moisture content and particle size variations in surface tailings. In addition, the partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) algorithms based on laboratory spectra were used to calibrate spectral information with associated tailing properties. The contribution of wavelength in the calibration modeling process by calculating SHaply Additive exPlanations (SHAP) values. According to the results, the reflectance spectra were negatively correlated to TFe content and positively correlated to SiO&lt;sub&gt;2&lt;/sub&gt; content. The three-band spectral index (TBI) calculated by R&lt;sub&gt;827&lt;/sub&gt;/(R&lt;sub&gt;900&lt;/sub&gt; × R&lt;sub&gt;2200&lt;/sub&gt;) correlated best to TFe with the correlation coefficient (r) of 0.87, while the R&lt;sub&gt;2397&lt;/sub&gt;/(R&lt;sub&gt;776&lt;/sub&gt;×R&lt;sub&gt;900&lt;/sub&gt;) correlated best to SiO&lt;sub&gt;2&lt;/sub&gt; with r of 0.70. It also minimized the effect of particle size and moisture content on the reflectance spectra of tailings properties. The CNN algorithm with laboratory spectra yielded the highest estimation accuracy for TFe (R&lt;sup&gt;2&lt;/sup&gt; = 0.74, RPD = 1.79, RMSE = 3.69 %, LCCC = 0.74 and bias = -0.41) and SiO&lt;sub&gt;2&lt;/sub&gt; (R&lt;sup&gt;2&lt;/sup&gt; = 0.81, RPD = 2.15, RMSE = 1.28 %, LCCC = 0.86 and bias = −0.49). The direct standardization (DS) algorithm was applied to correct GF-5 hyperspectral image. Subsequently, the ability of TBI and the CNN model was compared for estimating and mapping the spatial distribution of TFe and SiO&lt;sub&gt;2&lt;/sub&gt; contents based on the corrected GF-5 images. The SHAP could obtain the wavelength contribution of the CNN model in tailings spectral modeling. It can be concluded that the proposed TBI is able to rapidly characterize the spatial distribution of tailings properties, and the interpretable CNN model can provide a technical mean for accurate estimation of tailings properties base","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104512"},"PeriodicalIF":7.6,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction","authors":"Ningjing Wang ,&nbsp;Xinyu Wang ,&nbsp;Yang Pan ,&nbsp;Wanqiang Yao ,&nbsp;Yanfei Zhong","doi":"10.1016/j.jag.2025.104518","DOIUrl":"10.1016/j.jag.2025.104518","url":null,"abstract":"<div><div>Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets focus on urban areas and only contain a small number of rural scenes with complex backgrounds. The application of current public datasets for rural road extraction is challenging due to significant stylistic differences between urban and rural roads. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs of 1024 × 1024 high-resolution satellite images with the corresponding road annotation, covering a 6866.35 km² of rural areas in eight countries around the world. In addition, the article comprehensively summarizes the characteristics of this dataset and comprehensively evaluates advanced deep learning methods for road extraction on the WHU-RuR + dataset. Experimental results show that this dataset not only meets the application needs of rural road mapping but also has great practical application potential. At the same time, this article analyzes the challenges faced by rural road extraction and explores future research directions. The proposed WHU-RuR + rural road dataset will be available at the following URL: <span><span>http://rsidea.whu.edu.cn/WHU_RuR+_dataset.htm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104518"},"PeriodicalIF":7.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}