SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

“…Existing deep learning-based methods, particularly those relying on triple-branch architectures, often struggle to accurately localize and predict changes in complex spatial environments characterized by diverse land-cover types. …”
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Efficient room-level heat load prediction in buildings using spatiotemporal distribution characteristics by Xin Tan, Kaixuan Xu, Yahui Wang, Qihui Yu, Yongheng Yu, Guoxin Sun

“…A thermodynamic model built with DesignBuilder and a ResGRU neural network enables overall heat load prediction, with spatiotemporal matrix decomposition ensuring rapid room-level estimations. …”
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The spatial–temporal changes and driving factors of desertification in the Wuliangsuhai watershed based on remote sensing by Wenwen Gao, Zuoyong Huang, Xiuzhong Li, Baocun Ji, Na Li, Senyang Li, Xingyu Liu, Qingwei Zeng, Guangnian Sun, Dan Zhao

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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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Measuring and modelling functional moat area in perennially ice-covered Lake Fryxell, Antarctica by Michael S. Stone, Mark R. Salvatore, Hilary A. Dugan, Madeline E. Myers, Peter T. Doran

“…Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. …”
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Modeling the effects of land use change on agricultural carrying capacity and food security by R. Harini, R. Rijanta, E.H. Pangaribowo, R.F. Putri, I. Sukri

“…Predictions of spatial land changes will reveal changes in land function, carrying capacity and food security between regions.METHODS: Land changes were studied using remote sensing imagery-based mapping methods and spatial simulations using the cellular automata approach. …”
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Numerical simulation study on the evolution of wrinkling defects in carbon fiber laminates based on spatial decomposition damage variable method by ZHENG Haocheng, ZHOU Bo, LI Hui, WANG Yajie, SUN Ning, ZHANG Xueyan

“…In order to investigate the compression damage evolution of carbon fiber laminates with wrinkles and accurately predict the mechanical behavior of damage initiation and propagation, a progressive damage finite element model was proposed based on three-dimensional elastic theory by employing a spatial decomposition of damage variables method to establish the damage constitutive relation. …”
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Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions by Vincenzo Giannico, Simone Pietro Garofalo, Luca Brillante, Pietro Sciusco, Mario Elia, Giuseppe Lopriore, Salvatore Camposeo, Raffaele Lafortezza, Giovanni Sanesi, Gaetano Alessandro Vivaldi

“…In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. …”
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Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data by Yang Chen, Xin Chen, Bin Bai, Linjiang Zheng

“…Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. …”
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A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. …”
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High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data by Mehdi Mikou, Améline Vallet, Céline Guivarch, David Makowski

“…It also yielded better results for the estimation of spatial inequality within administrative units. Using SHAP values, we explored the contribution of the model predictors to income predictions and found that, in addition to geographic predictors, distance to public transport or nighttime light intensity were key drivers of income predictions. …”
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A Moroccan soil spectral library use framework for improving soil property prediction: Evaluating a geostatistical approach by Tadesse Gashaw Asrat, Timo Breure, Ruben Sakrabani, Ron Corstanje, Kirsty L. Hassall, Abdellah Hamma, Fassil Kebede, Stephan M. Haefele

“…A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. …”
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Short-term rainfall prediction based on radar echo using an efficient spatio-temporal recurrent unit by Dali Wu, Shunli Zhang, Guohong Zhao, Yongchao Feng, Yuan Ma, Yue Zhang

“…The combined effect of the Self-Attention (SA) mechanism and convolution allows the model to focus on both global and local dependencies in spatial information, improving the clarity of the generated images. …”
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Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach by Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Y. Rebouh

“…The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+-based deep learning framework coupled with geographic information systems and multicriteria decision making methods for spatial prediction of landslide risk, over the Dubai coastal and urban region (covering approximately 4000 km²). …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…Geographic differences in access to better water sources were found through spatial analysis, with rural areas being the most impacted. …”
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Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event by Wenjia Cao, Robert V Rohli, Paul W Miller

“…National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) final (FNL) data were used as input to the Weather Research and Forecasting (WRF) model, to simulate the case study of the catastrophic 2016 flood in Louisiana, USA, for three aerosol loading scenarios: virtually clean, average, and very dirty, corresponding to 0.1×, 1×, and 10× the climatological aerosol concentration. …”
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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. …”
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Thermal dose feedback control systems applied to magnetic nanoparticle hyperthermia by Yash Sharad Lad, Shreeniket Pawar, Nageshwar Arepally, Hayden Carlton, Constantinos Hadjipanayis, Robert Ivkov, Ma’Moun Abu-Ayyad, Anilchandra Attaluri

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Enhancing soil total nitrogen prediction in rice fields using advanced Geo-AI integration of remote sensing data and environmental covariates by Novandi Rizky Prasetya, Aditya Nugraha Putra, Mochtar Lutfi Rayes, Sri Rahayu Utami

“…Recently, advanced Geospatial-Artificial Intelligence (Geo-AI) techniques such as the random forest (RF) algorithm have been developed to increase the accuracy and spatial representativeness of STN prediction. However, critical challenges remain in using datasets from multiple locations. …”
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