Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine by Shiyu Li, Hong Wan, Qun Yu, Xinyuan Wang

“…In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. Finally, the downscaling accuracy of the network was evaluated through simulated data experiments and real data experiments and compared with the Random Forest (RF) method. …”
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Weed Types and Dynamics Associations with Catena Landscape Positions: Smallholder Farmers’ Knowledge and Perception in Zimbabwe by Justin Chipomho, Simbarashe Tatsvarei, Cosmas Parwada, Arnold Bray Mashingaidze, Joyful T. Rugare, Stanford Mabasa, Regis Chikowo

“…The data were coded and processed using the CSPro software package, and then analysed using the SPSS program. Factors that predicted the spatial distribution of weeds were determined using a binary logistic model. …”
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An Upscaling-Based Strategy to Improve the Ephemeral Gully Mapping Accuracy by Solmaz Fathololoumi, Daniel D. Saurette, Harnoordeep Singh Mann, Naoya Kadota, Hiteshkumar B. Vasava, Mojtaba Naeimi, Prasad Daggupati, Asim Biswas

“…The overall classification error of the upscaling strategy at spatial resolutions of 0.6, 2, 4, 6, 8, and 10 m (Upscaled), as well as that of the direct Sentinel-2 model, were 7.9%, 8.2%, 9.1%, 10.3%, 11.2%, 12.5%, and 14.5%, respectively. …”
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Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria. by Freya M Shearer, Zhi Huang, Daniel J Weiss, Antoinette Wiebe, Harry S Gibson, Katherine E Battle, David M Pigott, Oliver J Brady, Chaturong Putaporntip, Somchai Jongwutiwes, Yee Ling Lau, Magnus Manske, Roberto Amato, Iqbal R F Elyazar, Indra Vythilingam, Samir Bhatt, Peter W Gething, Balbir Singh, Nick Golding, Simon I Hay, Catherine L Moyes

“…<h4>Methodology/principal findings</h4>A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. …”
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Mapping global leaf inclination angle (LIA) based on field measurement data by S. Li, S. Li, S. Li, H. Fang, H. Fang

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Estimation of nitrogen uptake, biomass, and nitrogen concentration, in cover crop monocultures and mixtures from optical UAV images by Paolo Dal Lago, Nikolaos Vavlas, Lammert Kooistra, Gerlinde B. De Deyn

“…This study provides a basis for spatially predicting key CC traits using UAV optical data.…”
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HMGB1 Inhibition Alleviates Chronic Nonbacterial Prostatitis by Suppressing M1 Polarization of Macrophages by Zhou J, Ding L, Chen J, Chen C, Jiang P, Mei Z, Jiang Q, Hua X

“…Co-immunofluorescence was used to analyze the functional phenotype of macrophages and spatial localization of HMGB1 in prostate of EAP mice. …”
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Defending Data Poisoning Attack Through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease by M. Shyamala Devi, S. Priya

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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…The analysis has been conducted for a coastal region in China, where derived features, such as normalized difference vegetation index (NDVI), salinity indices, and SAR-based soil moisture proxies, have been used as inputs to the CNN model. The model achieved an overall accuracy of 87% and a kappa coefficient of 0.82, outperforming traditional classification methods by leveraging spatial feature learning and data augmentation. …”
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Analysing factors underlying the reporting of established non-native species by Phillip J. Haubrock, Ismael Soto, Ross N. Cuthbert, Irmak Kurtul, Elizabeta Briski

“…We employ a series of broadscale national predictors classified into ‘research’, ‘economy’, ‘environment & culture’, and ‘land-use’ to predict successful establishment. Our null models, which assume the distribution of non-native species mirrors that of each predictor, accurately predicted non-native species numbers across European countries. …”
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Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. …”
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Dynamic bone recognition for robotic vertebral plate cutting via unit energy consumption and SVM optimized by PSO by Heqiang Tian, Jing Zhao, Ying Sun, Jinchang An

“…Principal component analysis (PCA) was used for further feature extraction and dimensionality reduction. The data was normalized, and an SVM-based bone identification model was developed, optimized by PSO. …”
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Analysis of Written Material-based, Multi-perspective Argumentation in Geography Lessons: A Comparison Between Two Samples From Ecuador and Germany by Kimberley Hindmarsh, Alexandra Budke

“…The skills and difficulties students had seem to be due to the cognitive demands of the task rather than on the students' respective cultural and educational context as the results show comparable trends in most categories of the model in both countries.  …”
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Prioritization of Urban Regions from the Perspective of Physical and Environmental Indicators of Livability (Case Study: 5 Regions of Urmia) by Nima Bayramzadeh, Amin Shahsavar

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Assessment of Hydrologic Data Estimates From ERA5 Reanalyses in Benin, West Africa by René Bodjrènou, Luc Ollivier Sintondji, Yekambessoun M' Po N'Tcha, Diane Germain, Francis Esse Azonwade, Fernand Sohindji, Gilbert Hounnou, Edid Amouzouvi, Arthur Freud Segnon Kpognin, Françoise Comandan

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Aboveground biomass relationship with canopy cover and vegetation to improve carbon change monitoring in rangelands by Chiara Pasut, Jacqueline R. England, Melissa Piper, Stephen H. Roxburgh, Keryn I. Paul

“…A boosted‐regression‐tree model was trained to identify the relative importance of predictor variables. …”
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Time-series high spatio-temporal resolution vegetation leaf area index estimation based on NDVI trends by Chen Li, Hongmin Zhou, Jia Tang, Changjing Wang, Ziyu Wang, Jinlin Qi, Bihong Yang, Ruojing Fang

“…To overcome these challenges, a novel data assimilation approach, the ENKF-NDVI (Ensemble Kalman Filter-NDVI) model, was developed to estimate LAI data with 10-m spatial and 5-day temporal resolution. …”
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Assessing flash flood occurrences and hazard zones along the mediterranean coasts: a GIS and mapping techniques approach in the Wadi Saf Saf Basin, Northeast Algeria by Amal Maou, Amal Maou, Sabrina Souadnia, Sabrina Souadnia, Khadeijah Faqeih, Afaf AlAmri, Riheb Hadji, Riheb Hadji, Somayah moshrif Alamri, Khaled Harizi, Eman Alamery, Griche Sonia, Griche Sonia, Boubazine Leila, Boubazine Leila, Jamilah Altwaiher

“…This integrated approach highlights the spatial variability of flood vulnerability across the Saf Saf watershed and offers essential support for disaster risk reduction, structural mitigation planning, and informed land-use management in Mediterranean catchments.…”
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Recommendations for planning UAV flight missions for geodata collection by Hutsul Taras, Lysko Bohdan, Zhezhera Ivan, Tkach Vladyslav, Tsvyk Tetyana

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