Assessing Coincidence of Satellite Acquisitions and Flood Events to Predict Suitability for Flood Map Synthesis by Lyle Prince, Riley C. Hales, Kel N. Markert, E. James Nelson, Gustavious P. Williams, Daniel P. Ames, Hyongki Lee, Amirhossein Rostami

“…For our processes, we used Forecasting Inundation Extents using REOF analysis (FIER), a data-driven method of synthesizing flood maps by correlating extracted spatial and temporal patterns from satellite imagery with historical hydrological variables. …”
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Relating satellite NO2 tropospheric columns to near-surface concentrations: implications from ground-based MAX-DOAS NO2 vertical profile observations by Bowen Chang, Haoran Liu, Chengxin Zhang, Chengzhi Xing, Wei Tan, Cheng Liu

“…This study enhances understanding of the spatial and temporal dynamics and influencing mechanisms of CNO2 and SNO2, supporting improved air quality assessments and pollution exposure evaluations.…”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. …”
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Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks by Weixuan Wang, Yating Wei, Longfei Hao, Zushuai Wei, Tianjie Zhao

“…However, due to the combined effects of internal factors like soil types, terrain, and vegetation cover, as well as external factors such as precipitation and temperature, soil moisture data in wireless sensor networks exhibit complex spatial and temporal interdependencies. Consequently, developing predictive models that can accurately capture these dependencies and enable precise forecasts of soil moisture within these networks poses a significant challenge. …”
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Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. The models are trained on a comprehensive dataset encompassing environmental indicators, fire-prone area characteristics, and real-time meteorological data. …”
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Global biome changes over the last 21 000 years inferred from model–data comparisons by C. Li, C. Li, A. Dallmeyer, J. Ni, M. Chevalier, M. Willeit, A. A. Andreev, X. Cao, L. Schild, L. Schild, B. Heim, M. Wieczorek, U. Herzschuh, U. Herzschuh, U. Herzschuh

“…</p> <p>Overall, our reconstruction, with its relatively high temporal and spatial resolution, serves as a robust dataset for evaluating ESM-based paleo-megabiome simulations and provides potential clues for improving systematic model biases.…”
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Monthly methane emissions in Chinese mainland provinces from 2013–2022 by Duo Cui, Siyao Yang, Xuanren Song, Xiaoting Huang, Cuncun Duan, Mingrui Ji, Zhongyan Li, Siqi Yu, Zhu Deng, Piyu Ke, Xinyu Dou, Taochun Sun, Zhu Liu

“…Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. …”
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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…The Multi-Scale Feature Extraction Module (MFEM) employs convolutional layers with varying dilation rates to capture spatial patterns from fine to coarse granularity. …”
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Discriminating spatiotemporal heterogeneity and environmental drivers of fish assemblages using environmental DNA metabarcoding in mosaic habitat ecosystems by Fenfen Ji, Jianzhong Shen, Wenqiang Zhang, Saihong Yan, Baoqing Shan, Jinmiao Zha

“…Although environmental DNA (eDNA) metabarcoding has been effectively used to evaluate fish diversity, studies exploring the spatial and temporal variability of fish communities in mosaic habitats and their connection to water quality after ecological project implementation are still scarce. …”
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Identifying gaps in protection from malaria vector biting in rural Cambodia using an entomological assessment and human behaviour observations by David J. McIver, Elodie A. Vajda, Dyna Doum, Nicholas W. Daniel, Molly Quan, Diane D. Lovin, Joanne M. Cunningham, Siv Sovannaroth, Allison Tatarsky, Neil F. Lobo

“…To help achieve malaria elimination, human behaviour data on intervention use and behaviour patterns should be evaluated and integrated with entomological data towards identifying and quantifying protection conferred by current interventions, as well as remaining gaps in protection. …”
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Trends in Heavy Metal Pollution in Agricultural Land Soils of Tropical Islands in China (2000–2024): A Case Study on Hainan Island by Erping Shang, Yong Ma, Wutao Yao, Shuyan Zhang

“…Pb and As show similar spatial patterns, with higher concentrations in the west and lower concentrations in the east. …”
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Spatiotemporal analysis of thermal islands in a semi-arid city: A case study of Kermanshah, Iran using machine learning and remote sensing by Peyman Karami, Seyed-Mohsen Mousavi

“…Cold Islands (CIs) and Hot Islands (HIs) were identified for each image using LST and Getis-Ord Gi analysis, and their spatio-temporal changes were evaluated with the Kappa index and landscape metrics. …”
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Exploring habitat‐density relationships and model transferability for an alpine bird using abundance models by Håkon Brandt Fjeld, Jan Eivind Østnes, Erlend B. Nilsen

“…While several previous studies have evaluated the transferability of species distribution models, much less is known about how well models that predict spatially explicit population density transfer across contexts. …”
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Urban Heat Island Effect: Remote Sensing Monitoring and Assessment—Methods, Applications, and Future Directions by Lili Zhao, Xuncheng Fan, Tao Hong

“…The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. …”
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Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues by Yihan Zhou, Xiaokang Yu, Zixi Huang, Feierdun Palati, Zeyu Zhao, Zihan He, Yuan Feng, Yuxi Luo

“…The FIE block utilizes ResNet-18 to enhance the feature representation of video frames and integrates two types of attention mechanisms to capture spatial-temporal patterns. Meanwhile, the VIE block processes the Mel spectrogram of the audio signal, followed by an optimized Swin transformer block to extract auditory features. …”
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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

“…Additionally, uncertainty estimates using the p-factor indicated that the extreme gradient boosting model exhibited lower uncertainty in reproducing the observed spatio-temporal patterns of climate variables. Overall, the proposed framework enhances the reliability of global climate model simulations, supporting robust regional-scale hydrological modeling and climate change impact assessments.…”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…This study proposes a novel deep learning approach for surgical tool classification based on combining convolutional neural networks (CNNs), Feature Fusion Modules (FFMs), Squeeze-and-Excitation (SE) networks, and Bidirectional long-short term memory (BiLSTM) networks to capture both spatial and temporal features in laparoscopic surgical videos. …”
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SmartTrust: a hybrid deep learning framework for real-time threat detection in cloud environments using Zero-Trust Architecture by Umesh Kumar Lilhore, Sarita Simaiya, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi, Md Monish Khan

“…SmartTrust integrates CNN, LSTM, and Transformer models to analyze spatial and temporal patterns in network traffic and user behaviours. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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