SoftwareX最新文献_第4页

VIsToLAI: A modular open-source platform for estimating the leaf area index from remote sensing-derived vegetation indices VIsToLAI：一个模块化的开源平台，用于从遥感衍生的植被指数估算叶面积指数

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102504

Jonghan Ko , Chi Tim Ng

{"title":"VIsToLAI: A modular open-source platform for estimating the leaf area index from remote sensing-derived vegetation indices","authors":"Jonghan Ko , Chi Tim Ng","doi":"10.1016/j.softx.2025.102504","DOIUrl":"10.1016/j.softx.2025.102504","url":null,"abstract":"<div><div>VIsToLAI, a unique open-source software framework developed in Python, stands out for its ability to estimate the leaf area index (LAI) using time series data derived from various remote sensing vegetation indices (VIs). The framework integrates both empirical regression models and machine learning (ML) approaches, offering a flexible and scalable workflow for LAI estimation. Through case studies on four major staple crops—rice, barley, wheat, and maize—this study demonstrates the framework's ability to accurately estimate LAI across diverse crop types and environmental conditions. Results show that machine learning models, particularly extra trees and gradient boosting, outperform traditional empirical models in terms of accuracy and robustness, especially under heterogeneous data conditions. VIsToLAI’s modular architecture enables the easy incorporation of new indices and algorithms, as well as seamless integration into existing remote sensing workflows. The software provides a valuable tool for bridging remote sensing data with agricultural modeling, supporting precision agriculture and large-scale monitoring initiatives.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102504"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VelCrys: Interactive web-based application to compute acoustic wave velocity in crystals and its magnetic corrections VelCrys：基于web的交互式应用程序，用于计算晶体中的声波速度及其磁校正

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2025-12-12 DOI: 10.1016/j.softx.2025.102472

P. Nieves , I. Korniienko , A. Fraile , J.M. Fernández-Díaz , R. Iglesias , D. Legut

引用次数: 0

SNAPRed: Reduction of multidimensional neutron time-of-flight diffraction data SNAPRed：减少多维中子飞行时间衍射数据

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.softx.2025.102464

M. Guthrie , M.M. Walsh , K.A. Travis , S.R. Boston , D.L. Caballero , D.D. Dinger , G. Elsarboukh , J.M. Hetrick , A.T. Savici , P.F. Peterson

引用次数: 0

TD-TSPS: A hybrid strategy method for TD detection based on two-step progressive segmentation TD- tsps：一种基于两步渐进分割的TD检测混合策略方法

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102499

Yuxi Zhang, Jinxin Dong, Hua Jiang, Ruchao Du, Ranran Sun

引用次数: 0

Spectral Imaging Deep Learning Mapper - SpecDeepMap: An open-source EnMAP-Box semantic segmentation application for hyper- and multispectral mapping 光谱成像深度学习Mapper - SpecDeepMap：一个开源的EnMAP-Box语义分割应用程序，用于超光谱和多光谱映射

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102481

Leon-Friedrich Thomas , Benjamin Jakimow , Andreas Janz , Patrick Hostert , Antti Lajunen

{"title":"Spectral Imaging Deep Learning Mapper - SpecDeepMap: An open-source EnMAP-Box semantic segmentation application for hyper- and multispectral mapping","authors":"Leon-Friedrich Thomas , Benjamin Jakimow , Andreas Janz , Patrick Hostert , Antti Lajunen","doi":"10.1016/j.softx.2025.102481","DOIUrl":"10.1016/j.softx.2025.102481","url":null,"abstract":"<div><div>Deep learning is increasingly applied in spectral imaging and remote-sensing research, yet accessible interface-based software remains limited. We therefore developed the Spectral Imaging Deep Learning Mapper (SpecDeepMap), a free and open-source application embedded into the EnMAP-Box QGIS plugin that enables deep-learning–based spectral analysis and mapping. SpecDeepMap implements a comprehensive semantic segmentation workflow through a user-friendly graphical interface, requiring no programming expertise. The software is designed for multispectral and hyperspectral data and addresses geographical data challenges, such as spatial class distribution, and continuous largescale mapping tasks. SpecDeepMap offers various deep-learning architectures, such as U-Net, U-Net++, DeeplabV3+, and SegFormer, paired with diverse backbones such as ResNet-18, ConvNeXt, Swin-Transformers and Segment Anything Model 2. This software is the first QGIS plugin that enables fine-tuning multispectral foundation models for Sentinel-2 Top of Atmosphere Reflectance imagery. These weights stem from pretraining by Wang et al. (2022) on the Self-Supervised Learning for Earth Observation Sentinel-1/2 dataset.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102481"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D4Xgui: A tool for baseline correction and standardization of carbonate clumped isotope raw data D4Xgui：碳酸盐岩块状同位素原始数据基线校正与标准化工具

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102492

Miguel Bernecker , Mathieu Daëron , Philip Tauxe Staudigel , Sven Hofmann , Jens Fiebig

{"title":"D4Xgui: A tool for baseline correction and standardization of carbonate clumped isotope raw data","authors":"Miguel Bernecker , Mathieu Daëron , Philip Tauxe Staudigel , Sven Hofmann , Jens Fiebig","doi":"10.1016/j.softx.2025.102492","DOIUrl":"10.1016/j.softx.2025.102492","url":null,"abstract":"<div><div>Accurate and precise mass spectrometric determination of ppm-ppb quantities of mass 47–49 <em>clumped</em> isotopologues of carbonate-derived CO<sub>2</sub>, expressed as <span><math><msub><mi>Δ</mi><mrow><mn>47</mn></mrow></msub></math></span>–<span><math><msub><mi>Δ</mi><mrow><mn>49</mn></mrow></msub></math></span> values, requires advanced processing schemes. Here, we introduce D4Xgui, a user-friendly processing tool that allows correction of mass-spectrometric raw intensities for a pressure baseline artifact, before standardization is carried out using D47crunch. D4Xgui enables rapid processing of multi-session data under consideration of full error-propagation, interactive visualization of results including tools for data quality assurance, calculation of carbonate crystallization temperature from finally processed data, and rapid re-evaluation of datasets with revised processing parameters. Though the primary focus of D4Xgui is on carbonates it can also be applied to the correction of mass spectrometric raw data obtained on CO<sub>2</sub> from other sources.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102492"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLIMB: Framework for CLIMate data bias-adjustment and downscaling CLIMB：气候数据偏差调整和降尺度框架

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.softx.2025.102479

Jakub Śledziowski , Paweł Terefenko , Andrzej Giza , Kamran Tanwari , Dominik Paprotny

引用次数: 0

AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns AssociationExplorer：一个用户友好的应用程序，用于探索关联和可视化模式

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.softx.2025.102483

Antoine Soetewey , Cédric Heuchenne , Arnaud Claes , Antonin Descampe

{"title":"AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns","authors":"Antoine Soetewey , Cédric Heuchenne , Arnaud Claes , Antonin Descampe","doi":"10.1016/j.softx.2025.102483","DOIUrl":"10.1016/j.softx.2025.102483","url":null,"abstract":"<div><div>AssociationExplorer is an open-source interactive R Shiny application designed to help non-technical users explore statistical associations within multivariate datasets. Aimed particularly at journalists, educators, and engaged citizens, the tool facilitates the discovery and interpretation of meaningful patterns between variables without requiring programming or statistical expertise. Users can upload structured data (e.g., from surveys or open government datasets), select relevant variables, and dynamically visualize relationships via a correlation network and contextual bivariate plots. To illustrate its capabilities, we present a case study based on the European Social Survey (ESS), showcasing how users can investigate links between attitudes, behaviors, and socio-demographic indicators across countries. The app supports a range of association measures adapted to variable types (Pearson’s <span><math><mi>r</mi></math></span>, Eta, and Cramer’s V), ensuring both flexibility and statistical rigor. The visual interface enables users to adjust thresholds for association strength and examine results through interactive graphs and summary tables, making the app particularly well-suited for data storytelling, exploratory research, and public communication. AssociationExplorer demonstrates how open-source statistical tools can enhance transparency, accessibility, and insight in the interpretation of complex social data.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102483"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PermXCT: A novel framework for imaging-based virtual permeability prediction PermXCT：一种基于成像的虚拟渗透率预测框架

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-29 DOI: 10.1016/j.softx.2026.102529

Debabrata Adhikari, Jesper John Lisegaard, Jesper Henri Hattel, Sankhya Mohanty

{"title":"PermXCT: A novel framework for imaging-based virtual permeability prediction","authors":"Debabrata Adhikari, Jesper John Lisegaard, Jesper Henri Hattel, Sankhya Mohanty","doi":"10.1016/j.softx.2026.102529","DOIUrl":"10.1016/j.softx.2026.102529","url":null,"abstract":"<div><div>PermXCT is an open-source computational framework designed to predict virtual permeability in fiber-reinforced polymer composites based on data extracted from X-ray computed tomography (XCT). It provides an automated and reproducible workflow that connects imaging based geometry extraction, mesh generation, and numerical flow simulation for permeability estimation. The framework integrates both mesoscale and microscale morphological characteristics, such as intra and inter-yarn porosity and fiber orientation, to capture realistic flow pathways within complex composite geometries. PermXCT utilises a combination of established open-source tools, including DREAM3D for mesh creation, OpenFOAM for fluid flow simulation, and Python and MATLAB for data processing and automation. Computational efficiency is achieved through optimized meshing strategies and domain scaling, enabling large XCT datasets to be analyzed with reduced computational cost. Validation against experimental permeability measurements demonstrates strong agreement, confirming the reliability and physical accuracy of the imaging based predictions. By minimizing uncertainties and repeatability issues associated with experimental permeability testing, PermXCT provides a robust foundation for XCT-informed virtual permeability characterization.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102529"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TorchCor: High-performance cardiac electrophysiology simulations with the finite element method on GPUs TorchCor：基于gpu的有限元方法的高性能心脏电生理模拟

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 10.1016/j.softx.2026.102521

Bei Zhou , Maximilian Balmus , Cesare Corrado , Ludovica Cicci , Shuang Qian , Steven A. Niederer

引用次数: 0