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

{"title":"Multi-perspective occlusion-free urban navigation visualization","authors":"Shen Ying,&nbsp;Haoran Yang,&nbsp;Junru Su","doi":"10.1016/j.jag.2025.105074","DOIUrl":"10.1016/j.jag.2025.105074","url":null,"abstract":"<div><div>3D navigation, by virtue of its realism, provides rich, authentic, and visually immersive information that closely approximates the physical world, playing a crucial role in domains such as commerce and scientific research. However, current 3D navigation systems face challenges including the separation of multi-view information and the occlusion of navigation routes. This paper presents an occlusion-free, multi-perspective visualization method for urban navigation routes. We introduce an undulating curve surface and, through computation of an information detail metric, demonstrate that our surface enables rapid transitions from the focus to the context and supports visualization of much larger scene extents within the same screen space, compared to a monotonic cylindrical surface. We further enhance scene information delivery by segmenting the navigation route and adjusting the camera’s tilt angle and field of view to match different surface parameters. To eliminate occlusion of the navigation route, we adjust the position and scale of obstructing buildings, assigning distance-based weights. Compared to the fixed-weight approach, our distance-weighted approach highlights building position and shape features more effectively across varied scenarios. The proposed method is validated using an experimental scenario in a region of Barcelona. Experimental results indicate that our approach can effectively represent scenes at different scales while prominently displaying the navigation route, thereby enhancing the overall navigation experience.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105074"},"PeriodicalIF":8.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926328","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":"A multi-pass InSAR analysis of the estuarine alluvial Chongming Island ground displacements with implications on flood risk","authors":"Qing Zhao ,&nbsp;Lei Zhou ,&nbsp;Yifei Zhang ,&nbsp;Antonio Pepe ,&nbsp;Chengfang Yao ,&nbsp;Jingjing Wang ,&nbsp;Yuanzhi Yao","doi":"10.1016/j.jag.2025.105057","DOIUrl":"10.1016/j.jag.2025.105057","url":null,"abstract":"<div><div>Chongming Island of Shanghai, the largest estuarine alluvial island of China, has been kept seaward expanding due to continuous riverine sediment deposits and extensive land reclamation. Moreover, the island is highly vulnerable to long-term ground deformations and rising sea levels. To evaluate the influence of coastal ground deformation on flood risk, we investigated long-term ground deformation on Chongming Island over the last decade using RADARSAT-2 and Sentinel-1A satellite datasets collected from 2012 to 2023. The independently achieved ground deformation time series, representing the projection of ground displacement along the radar line of sight (LOS) direction (recovered using traditional multi-temporal interferometric SAR algorithms), were then jointly combined. The results show that the eastern region of Chongming Island has been experiencing significant ground subsidence over the last decade, with a maximum amplitude exceeding 20 mm/year. The significant ground subsidence in the eastern island occurred at a rate at least 2.5 times that of the concurrent sea level rise (SLR).</div><div>Furthermore, we analyzed the coastal flood risk on Chongming Island under the comprehensive effects of local ground subsidence, SLR, and storm surges. To this aim, a hydrodynamic model was applied to simulate various inundation scenarios on Chongming Island after failures of different seawall sections. As the major outcome of our work, under the simulated scenario (e.g., the worst case), the total flood inundation area in 2042 was estimated to exceed 50% of the island. Under this worst scenario, almost all of the salt marsh vegetation in Chongming Dongtan Nature Reserve would be flooded after the relative SLR. The loss of elevation in the low-lying island further aggravates the coastal flooding risk. Therefore, preserving the stability of eastern and northern seawalls is essential to the security of Chongming Island in the context of the increasing flooding risk in the low-lying river estuary.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105057"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925605","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":"Remote sensing for cultural heritage: A systematic review","authors":"Fulong Chen ,&nbsp;Peifeng Ma ,&nbsp;Siliang Chen ,&nbsp;Qingwu Hu ,&nbsp;Huadong Guo","doi":"10.1016/j.jag.2025.105039","DOIUrl":"10.1016/j.jag.2025.105039","url":null,"abstract":"<div><div>Cultural heritage is an integrated system that encompasses both heritage ontology and their surrounding environments. Traditional methods, such as field measurements, are often inefficient and fragmented in assessing the condition of cultural heritage. In recent years, remote sensing has emerged as a crucial tool for the systematic safeguarding and conservation of cultural heritage, thanks to its high spatiotemporal observation capabilities. Utilizing optical, radar, and laser sensors mounted on satellites, aircraft, unmanned aerial vehicles, and terrestrial platforms, remote sensing offers a comprehensive approach to ensure the sustainability of cultural heritage. This study addresses critical gaps in the emerging, interdisciplinary field of remote sensing for cultural heritage—including limited terrestrial platform coverage, underutilized AI paradigms, and insufficient policy integration—by conducting a systematic, state-of-the-art bibliometric analysis of peer-reviewed papers from the Web of Science Core Collection (1900–2024). Based on this analysis, we summarized the applicable methods, sensor platforms, and applications of remote sensing in cultural heritage conservation and proposed a theoretical framework incorporating relevant methodologies and models. Our results indicate that the integration of multi-spatiotemporal data and diverse technologies is a key characteristic of the disciplinary development. In addition, to application-oriented methodologies, hazard-risk mapping and sustainability assessment of cultural heritage are identified as two potential future directions, which align with the progress assessment of Sustainable Development Goal Target 11.4 by harnessing economic, political and institutional drivers in concert. Leveraging artificial intelligence, data-driven knowledge mining, and multi-source coupling, remote sensing for cultural heritage is transitioning from heritage-specific sustainability through systematic monitoring and evaluation to a broader focus on regional sustainability where heritage sites are located.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105039"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926089","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":"Geographically weighted regression model-assisted (GWRMA) estimation to improve precision of estimates combining remote sensing and ground plot data","authors":"Dingfan Xing ,&nbsp;Nicholas N. Nagle ,&nbsp;Stephen V. Stehman ,&nbsp;Todd A. Schroeder","doi":"10.1016/j.jag.2025.105045","DOIUrl":"10.1016/j.jag.2025.105045","url":null,"abstract":"<div><div>Remotely sensed data play a vital role providing auxiliary variables that can improve precision of sample-based estimates without incurring the substantial cost of increasing sample size. In the setting of design-based inference, model-assisted estimators using generalized regression models provide a practical option to exploit such auxiliary variables to obtain estimates with smaller uncertainties than the Horvitz-Thompson (HT) estimator. Spatial heterogeneity is one of the main characteristics of spatial geographical populations and spatial nonstationarity in regression model parameters can greatly impact model predictions. However, model-assisted estimators typically do not incorporate spatial heterogeneity in the assisting models, and this may result in failing to gain the full precision improvement available from model-assisted estimation. Geographically Weighted Regression (GWR) offers a practical way to incorporate spatial heterogeneity of the data into the model. In this study, we substantiate the benefit of employing a GWR model-assisted estimator (GWRMA) in which GWR is used as a linking model between the response and auxiliary variables. The illustrative example we use to demonstrate the precision enhancing capacity of GWRMA is estimating tree volume in forest inventory monitoring. The performance of GWRMA was compared with the HT estimator and a linear regression model-assisted (LRMA) estimator when both the response and auxiliary variables are continuous. Several factors potentially impacting the estimators and their precision were investigated using Monte-Carlo simulation applied to populations constructed to represent different levels of spatial heterogeneity and correlation between the response and auxiliary variables. Because both LRMA and GWRMA estimators are asymptotically unbiased, variance is the key criterion for comparing the estimators. For the constructed populations with spatial heterogeneity, the GWRMA estimator achieved standard errors smaller than the HT and LRMA estimators, and the improvement in precision increased with increasing sample size. The precision advantage of the GWRMA estimator relative to the LRMA estimator was attributable to the capacity of the GWRMA estimator to adapt to spatial variation in the regression model leading to better local prediction of the target variable. The conventional model-assisted variance estimator substantially underestimated the variance of the GWRMA estimator. An alternative variance estimator based on averaging the GWRMA and LRMA variance estimates avoided the severe underestimation of the conventional model-assisted variance estimator. This average variance estimator yielded confidence intervals that exceeded the nominal 90% coverage but still had shorter length than the 90 % confidence intervals from LRMA estimator. Further exploration of variance estimators for the GWRMA estimator is a necessary next step to improve utility of the GWRMA estimator.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105045"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926166","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":"Simplifying the bipartite matching topology recovery for vectorized building footprint extraction","authors":"Libo Cheng ,&nbsp;Han Hu ,&nbsp;Liupeng Su ,&nbsp;Zeyuan Dai ,&nbsp;Lihua Zhang ,&nbsp;Guanyan Yang ,&nbsp;Bo Xu ,&nbsp;Qing Zhu","doi":"10.1016/j.jag.2025.105071","DOIUrl":"10.1016/j.jag.2025.105071","url":null,"abstract":"<div><div>Extracting vectorized building footprints from high-resolution imagery requires precise corner localization, robust boundary connectivity, and the generation of spatially ordered topological structures that faithfully encompasses the building area. These interdependent requirements make current approaches complex, which typically rely on cascaded architectures combining heterogeneous neural networks for feature extraction and topological recovering separately. This paper proposes ABCNet, a unified CNN architecture that simultaneously extracts instance-level areas, boundaries, and corners while robustly recovering ordered topological sequences for building footprints. Specifically, we augment a standard YOLO-based instance segmentation network with dual detection heads to generate per-building instance mask scores for areas, boundaries, and corners. The initial exterior contour is derived from the instance area mask. To refine the footprint geometry beyond smoothed contour approximations, we establish geometrically ordered corner connections through a bipartite matching matrix. Each entry in this square matrix encodes the interpolated boundary probability score between two corners indexed by its row and column. Ordered relationships are estimated using path-length differences relative to the initial contour, computed from parameterized distances of orthogonally projected points along the contour. The vertex sequence direction (clockwise/counterclockwise) is resolved via the triangle inequality criterion. Finally, the topological sequence is recovered through linear-sum-assignment optimization applied to the bipartite matching matrix, giving the vectorized building. Experimental evaluation on two vectorized building extraction benchmarks demonstrates that the proposed method, despite its architectural simplicity, surpasses state-of-the-art approaches by significant margins—achieving 8.6% and 2.5% higher AP (Average Precision) under in-distribution and out-of-distribution scenarios, respectively; with a 3% improvement on human-annotated out-of-distribution vectorization benchmarks. Visual comparisons further reveal the method’s robustness, maintaining coherent degradation patterns even when geometric primitives are misdetected under challenging imaging conditions. The simple architecture inherently preserves design flexibility, enabling future enhancements to area, boundary, and corner detection precision through dedicated module refinements.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105071"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926332","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":"The first large-footprint hyperspectral LiDAR model to attempt to reveal forest chlorophyll distribution heterogeneity from vertically layered spectral perspective utilizing 3D radiative transfer modeling","authors":"Jie Bai ,&nbsp;Huaguo Huang ,&nbsp;Binbin He ,&nbsp;Lingfei Ma ,&nbsp;Shuai Gao ,&nbsp;Shuaifeng Peng ,&nbsp;Xuebo Yang ,&nbsp;Yishuo Hao ,&nbsp;Yuanbiao Dong ,&nbsp;Kaiyi Bi ,&nbsp;Li Wang ,&nbsp;Shihua Li ,&nbsp;Zheng Niu","doi":"10.1016/j.jag.2025.105060","DOIUrl":"10.1016/j.jag.2025.105060","url":null,"abstract":"<div><div>Vertical structural and spectral heterogeneity are two key remote sensing characteristics of complex forests. To enable effective forest health management and provide early warnings of abnormal disturbance, monitoring forest biochemical content with a vertically layered spectral perspective is critically needed. However, commonly used remote sensing technique still have limited capacity to study the biochemical status of the middle and lower canopy layers. This study provides the first insight into the potential of the full-waveform large-footprint hyperspectral LiDAR (LFHSL) system for retrieving the vertical heterogeneity of forest chlorophyll using 3D radiative transfer modeling. In our newly constructed LFHSL model, virtual three-dimensional (3D) complex forest scenes, comprising trees, bushes, and grass, were defined with varying positions and biochemical content inputs. Hyperspectral waveforms within the large-footprint were then simulated for each combination of vegetation position and biochemical level. The concept of spectral index time profiles (SITP), referred to as spectral index variation along the laser path, were introduced and used to assess the vertical distribution of chlorophyll for the first time in forest scenes. The main findings of this study are as follows: (1) Full-waveform LFHSL owns great potential for retrieving vertical chlorophyll content across trees, bushes, and grass layers in complex forest ecosystems. (2) SITP is a novel and essential reference indicator that fully registers chlorophyll variations along the laser path. (3) Simulations with random positions and chlorophyll contents indicate that the peak points of SITP yield higher chlorophyll prediction accuracy in trees layer and grass layer (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>: 0.996 vs. 0.971, RMSE: 1.39 vs. 3.81 <span><math><mrow><mi>μ</mi><mi>g</mi><mspace></mspace><msup><mrow><mi>cm</mi></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span>) than that of bushes layer (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.801, RMSE of 9.97 <span><math><mrow><mi>μ</mi><mi>g</mi><mspace></mspace><msup><mrow><mi>cm</mi></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span>). (4) Compared to position patterns, LFHSL system is more sensitive to chlorophyll content sets. This study demonstrates that full-waveform LFHSL is a promising and surely reliable tool for acquiring and monitoring vertical vegetation health in complex forests. It not only provides significant guiding for the development of laser radar models but also holds promise for adoption in design of large-footprint multi-spectral or hyperspectral LiDAR.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105060"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926333","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":"A novel deep learning framework for High-Throughput peanut seedling identification across diverse germplasm and complex field environments","authors":"Jiangtao Zhao ,&nbsp;Zhenhai Li ,&nbsp;Bo Bai ,&nbsp;Xue Kong ,&nbsp;Jishun Yang ,&nbsp;Guowei Li ,&nbsp;Tadese Anberbir ,&nbsp;Xiaobin Xu","doi":"10.1016/j.jag.2025.105061","DOIUrl":"10.1016/j.jag.2025.105061","url":null,"abstract":"<div><div>Accurate peanut seedling recognition is essential for quantifying emergence rates, a key phenotyping task in breeding programs for this important global oilseed. This task is challenged by field heterogeneity from morphological variation (genotype, growth stage, planting density) and imaging variability (flight altitude, solar angle). To address this, object-based image analysis (OBIA) and deep learning approaches were evaluated using UAV remote sensing imagery collected under systematically varied field conditions. An enhanced framework, P-YOLOv11s, was developed for UAV-based peanut seedling detection, incorporating a P2 layer for fine-scale features, an asymptotic feature pyramid network for multi-scale fusion, and an iEMA attention mechanism for occlusion robustness. The experimental design encompassed significant agronomic diversity (1025 genotypes, nitrogen regimes, planting years, densities, and eco-zones), developmental stages (three- to six-leaf), and flight configurations (15, 25, 40 m altitudes; four diurnal intervals). P-YOLOv11s demonstrated strong robustness, achieving a mean Average Precision (AP) of 93.5 %, with 62 % fewer false detections than OBIA and a 4.8 % higher AP than other YOLO variants. Flight altitude was the most influential factor, with 15 m yielding the best results. Peak accuracy (99.4 %) occurred at the four- to five-leaf stage, while solar angle had a minimal effect (&lt;1.7 % variation). The framework achieved subplot-level precision (μ = 0.42 plants), addressing the challenge of accurate field-based phenotyping under real-world constraints.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105061"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925607","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":"Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery","authors":"Tzu-Hsin Karen Chen ,&nbsp;Wei Chen ,&nbsp;Eleanor C. Stokes ,&nbsp;Yuyu Zhou","doi":"10.1016/j.jag.2026.105087","DOIUrl":"10.1016/j.jag.2026.105087","url":null,"abstract":"<div><div>Characterizing the evolution of urban settlements is vital for informed urban planning that mitigates associated risks. Urban development has traditionally been examined in two dimensions using Earth observation: land cover change, monitored through daytime optical remote sensing, and lighting infrastructural change, observed using nighttime remote sensing. However, these two types of change have often been analyzed in isolation, limiting a comprehensive understanding of their combined impacts on urbanization. This study bridges this gap by simultaneously analyzing monthly Black Marble nighttime light (NTL) data and World Settlement Footprint data to compare lighting and urban land cover change in the Mediterranean region. Our findings reveal that 80% of urbanization-associated pixels display either urban land expansion or lighting growth, but not both. Confusion matrix highlights regional variations: commission errors are particularly high in West Asia (74%), indicating increases in nightlights driven by densification or road improvements without corresponding land conversion. Conversely, omission errors are higher in Western Europe (52%) and North Africa (47%), where urban land expansion occurs without observable lighting infrastructure growth, reflecting phenomena such as informal settlement growth, industrial infill, and energy-saving practices. This study enhances our understanding of the urbanization process through satellite observations, emphasizing the need for a more comprehensive monitoring approach that captures the diverse dimensions of urban growth.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105087"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925922","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":"MB-UDF: A self-supervised model for continuous representation of seafloor topography using multibeam echo sounder data","authors":"Luotao Zhang ,&nbsp;Chunqing Ran ,&nbsp;Xiaobo Zhang ,&nbsp;Hao Yu ,&nbsp;Shuo Han ,&nbsp;Yilan Chen","doi":"10.1016/j.jag.2025.105056","DOIUrl":"10.1016/j.jag.2025.105056","url":null,"abstract":"<div><div>Conventional methodologies for processing Multibeam Echo Sounder (MBES) data have primarily relied on the generation of Digital Elevation Models (DEMs) from bathymetric point clouds. Nevertheless, these approaches demonstrate inherent limitations in their capacity to faithfully represent the continuous and intricate morphology of the seafloor, a critical requirement for comprehensive geophysical analyses. This study introduces MB-UDF, a novel self-supervised learning framework that trains neural networks to represent Unsigned Distance Functions (UDF) for high-fidelity reconstruction of continuous 3D seafloor surfaces from MBES data. The primary contributions of MB-UDF encompass a specialized point cloud sampling mechanism and an efficient self-supervised learning strategy, both meticulously designed to address the inherent characteristics of MBES data. Our PyTorch implementation is open-sourced and available at <span><span>https://github.com/Parallelopiped/MB-UDF</span><svg><path></path></svg></span>. To rigorously evaluate the performance of MB-UDF, we established a comprehensive MBES dataset incorporating diverse bathymetric terrains. Experimental results demonstrate that our method significantly outperforms existing 3D reconstruction techniques in terms of normal consistency and surface continuity, exhibiting enhanced robustness and superior precision compared to conventional DEM approaches. The proposed MB-UDF framework provides innovative methodological tools for advancing research in marine geology, seafloor mapping, and related domains.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105056"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926087","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":"Geospatial machine learning model for limestone suitability assessment","authors":"Idowu Jane Bada ,&nbsp;Michael Adeyinka Oladunjoye ,&nbsp;Moruffdeen Adedapo Adabanija","doi":"10.1016/j.jag.2025.105055","DOIUrl":"10.1016/j.jag.2025.105055","url":null,"abstract":"<div><div>Accurate assessment of limestone quality and investment potential requires advanced techniques due to spatial variability in thickness and geochemical composition which traditional exploration methods cannot capture efficiently. This study integrates Machine Learning (ML) and Geographic Information Systems (GIS) to optimize limestone exploration. The analysis used limestone and overburden thickness, X-ray Fluorescence (XRF), and Atomic Absorption Spectroscopy (AAS) data from 23 core samples with multiple ML classifiers: Decision Tree (DT), Logistic Regression (LR), XGBoost, Support Vector Machine (SVM), and Random Forest (RF). Predictive ability was evaluated using Accuracy, F1 Scores, Precision-Recall (P-R) curves, Area under P-R curves (AUC-PR), and Feature importance Analysis. Features were validated using a non-parametric approach. Predicted datasets of the selected classifier were subjected to limestone classification criteria and integrated into a GIS to generate predictive Limestone Suitability Index (LSI) map. DT and LR models showed 100 % accuracy, XGBoost performed poorly at 60 %, and SVM and RF had moderate performance (80 %). The F1-scores of 1.00 for LR and DT, 0.71 for SVM and RF, and 0.45 for XGBoost indicate prediction reliability differences. RF and SVM achieved balanced precision-recall (0.65–0.80), with RF attaining a higher AUC_PR (0.871) than SVM (0.643). The non-parametric validation of the features identified RF as most suitable. The LSI map based on RF outputs, categorized the area into high, medium, and low potential zones with high potential zones characterized by thick, CaO rich limestone beds (16.0–34.1 m, CaO ≥ 50 %, SiO2 ≤ 8 %). This made ML, specifically, RF an essential tool for limestone resource<!--> <!-->evaluation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105055"},"PeriodicalIF":8.6,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926086","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}