Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories by Qiqi Li, Manudeo Singh, Sonia Silvestri

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MapReduce teaching learning based optimization algorithm for solving CEC-2013 LSGO benchmark Testsuit by A.J. Umbarkar, P.M. Sheth, Wei-Chiang Hong, S.M. Jagdeo

“…Teaching Learning Based Optimization (TLBO) algorithm, introduced in 2011 is widely used in optimization problems across various domains. …”
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Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

“…These findings underscore the robust performance of advanced ML algorithms, particularly ensemble methods with tree-based structures, in flood risk mapping, especially within complex environmental contexts.…”
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A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce regionZENODO by Rodrigo de Q. Miranda, Rodolfo L.B. Nóbrega, Anne Verhoef, Estevão L.R. da Silva, Jadson F. da Silva, José C. de Araújo Filho, Magna S.B. de Moura, Alexandre H.C. Barros, Alzira G.S.S. Souza, Wanhong Yang, Hui Shao, Raghavan Srinivasan, Feras Ziadat, Suzana M.G.L. Montenegro, Maria do S.B. Araújo, Josiclêda D. Galvíncio

“…Therefore, we recommend the use of region-specific PTFs for hydraulic properties based on multi-covariate soil property maps. This cost-effective framework accurately integrates diverse environmental covariates, adapts to varying soil data availability, and scales across spatial resolutions, making it highly transferable to other data-scarce regions.…”
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Deep learning-based mapping of total suspended solids in rivers across South Korea using high resolution satellite imagery by JunGi Moon, SungMin Suh, SangJin Jung, Sang-Soo Baek, JongCheol Pyo

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A Regret-Enhanced DEA Approach to Mapping Renewable Energy Efficiency in Asia’s Growth Economies by Chia-Nan Wang, Nhat-Luong Nhieu, Yu-Cin Ye

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A dereverberation beamforming algorithm for noise source localization in anechoic and semi-reverberant environments by R. Singh, A. Mimani, R. Kumar

“…This paper presents a dereverberation beamforming (DBF) technique based on windowing the cross-correlation matrix (CCM) to improve the localization accuracy of beamforming maps for imaging the noise sources generated by real-world applications. …”
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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

“…The study’s methodology involved optimizing the algorithms through grid search and cross-validation techniques, followed by validation using historical flood data. …”
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Block-Level Matching Recognition Algorithm for OpenStreetMap and Segments From High-Resolution Remote Sensing by Fan Shen, Jianyu Chen, Yu Shen, Zhao Gun, Qiankun Zhu

“…To address these issues, we propose a matching recognition algorithm that combines OpenStreetMap (OSM) data with high-resolution RS imagery. …”
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Localization of Acoustic Emission Source in Rock Using SMIGWO Algorithm by Jiong Wei, Fuqiang Gao, Jinfu Lou, Lei Yang, Xiaoqing Wang

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Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin by Chandni Thakur, Venkatesh Budamala, KS Kasiviswanathan, Claudia Teutschbein, Bankaru-Swamy Soundharajan

“…The effectiveness of the extreme gradient boosting framework in reproducing climate data was compared with the conventional quantile delta mapping approach across the basin. Additionally, both methods were evaluated across different seasons, including monsoon, pre-monsoon, and post-monsoon. …”
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Few-shot Metro Track-wheel Image Segmentation Algorithm Based on Cross-attention Network by CAO Jianxin, ZHANG Yueying, JIANG Weihao, GAO Yunhao

“…Then, the low-, mid-, and high-level features from the dual-branch mappings are fused across scales. A cross-attention network is used to mine the relational semantics between these fused features, enabling the capture of shared semantic information in the deep space across different metro track-wheel images belonging to the same class. …”
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Rendering algorithm for 3D model of goods in power warehouse based on linear interpolation and 2D texture mapping by Gui Luo, Xin Chen, Quan Liu, Anlin Liu, Xuan Zhang, Li Tian, Yueshi Gao

“…Thirdly, the designed mapping architecture maintains color accuracy below ΔE < 3.2 across illuminance levels of 50–1000 lx. …”
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Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem’s cross-sections by Xiaoying Zhu, Weiyu Zhou, Jianguo Li, Jianguo Li, Mingchong Yang, Mingchong Yang, Haiyu Zhou, Haiyu Zhou, Jiada Huang, Jiada Huang, Jiahua Shi, Jun Shen, Guangyao Pang, Lingqiang Wang, Lingqiang Wang, Lingqiang Wang

“…However, the detection of small vascular bundles from cross-sectional images is challenging due to their tiny size and the noisy background typically present in microscopy images.MethodsTo address these challenges, we propose Rice-SVBDete, a specialized deep learning-based detection algorithm for small vascular bundles in rice stem cross-sections. …”
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Mapping sugarcane plantations in Northeast Thailand using multi-temporal data from multi-sensors and machine-learning algorithms by Savittri Ratanopad Suwanlee, Surasak Keawsomsee, Emma Izquierdo-Verdiguier, Jaturong Som-Ard, Alvaro Moreno-Martinez, Vorraveerukorn Veerachit, Jantima Polpinij, Kanokporn Rattanasuteerakul

“…The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification. …”
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IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT by Sefri Imanuel Fallo, Lidia Paskalia Nipu

“…This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. …”
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Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables by Haoulata Touré, Cyril D. Boateng, Solomon S. R. Gidigasu, David D. Wemegah, Vera Mensah, Jeffrey N. A. Aryee, Marian A. Osei, Jesse Gilbert, Samuel K. Afful

“…Over the past ten years, machine learning has been increasingly and successfully used in groundwater availability studies across the world. This review paper explores the application of machine learning techniques in groundwater availability studies including groundwater level prediction and groundwater potential mapping studies by focusing on some of the studies conducted in Africa. …”
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Application of the Multi-Strategy Improved Walrus Optimization Algorithm in Mobile Robot Path Planning by Yongfu Ke, Limei Shi, Weinan Ji, Peng Luo, Lei Guo

“…Firstly, the Sine-Tent-Cosine chaotic mapping is used to initialize the walrus population, addressing the issue of insufficient population diversity in the later stages of the algorithm&#x2019;s iteration. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

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An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario by Jingrui Zhang, Zhenhong Xu, Houde Liu, Xiaojun Zhu, Bin Lan

“…Additionally, a grid-based, rectangular-area, obstacle avoidance strategy is incorporated to precisely evaluate the obstacle avoidance path of each individual across different obstacle maps. Finally, the feasibility and effectiveness of the proposed hybrid algorithm are validated through simulations involving both single and multiple mine disaster maps. …”
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