Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China by Jingjing Liu, Lei Xu, Nengcheng Chen, Zeqiang Chen

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Improving Land Cover Mapping in Riverine Environments with Machine Learning and Spectral Indices by R. Sharma, Pritipadmaja, Z. Ahmad, R. D. Garg

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Predicting Forest Evapotranspiration using Remote Sensing and Machine Learning by B. Yadav, L. K. Sharma, B. Bijarniya

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Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico by K. D. Rodríguez González, L. E. Arista Cázares, F. D. Yépez Rincón

“…Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. …”
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A Machine Learning-Driven Approach for Automated Landfill Site Selection: An Experimental Study on Marmara Region, Türkiye by A. Bilgili, T. Arda, B. Kilic, B. Kilic, M. Uzar

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Two Decades of Coastal Development across the Arabian Gulf: Insights from Remote Sensing and Machine Learning by B. Dahy, R. Sousa, M. Sultan, J. A. Burt

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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

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Comparative Analysis of Deep Learning CNN Models and Traditional Machine Learning Approaches for Land Use Land Cover Classification Using Imagery by A. Chaurasia, A. Chaurasia, A. K. Thakur, N. S. Rehengma, M. Yadav, Pritipadmaja

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VHR Multispectral Satellite Image Classification with Kolmogorov-Arnold Networks for Urban Applications by M. Fawzy, M. Fawzy, Á. Barsi

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Detection of honey bees (<em>Apis mellifera</em>) in hypertemporal LiDAR point cloud time series to extract bee activity zones and times by J. S. Meyer, R. Tabernig, R. Tabernig, B. Höfle, B. Höfle

“…Given the vital role bees play in our ecosystems and their increasing endangerment, it is highly important to develop new methods that assist in gaining a deeper understanding of the spatial dimension of insect behavior. Conventional methods for monitoring bees are subject to accuracy limitations, experimental setup complexity, and lack the explicit spatial dimension. …”
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EventFormer: a hierarchical neural point process framework for spatio-temporal clustering events prediction by Shanshan Yu, Danhuai Guo, Yanjie Fu, Wei Jin

“…Abstract In real-world scenarios, event data often exhibits inherent randomness, complex historical dependencies, and hierarchical spatio-temporal clustering. …”
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Study on the Catastrophic Evolution of Tianshan Road Slope under the Freeze-Thaw Cycles by Luqi Wang, Yibing Zhang, Jian Guo, Qiang Ou, Songlin Liu, Lin Wang

“…The dynamic collapse of the rocky slope has a certain degree of randomness caused by the spatial distribution of structural planes and the interaction between the rock fragments. …”
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Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond by Anas Alashqar, Ehsan Olyaei Torshizi, Raed Mesleh, Werner Henkel

“…Extensive simulation results demonstrate that the proposed 2D CNN&#x2013;AE&#x2013;SSA-based PSKG framework significantly reduces the key disagreement ratio (KDR), enhances key randomness, and maintains low computational complexity. …”
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Using Plant DNA Barcodes and Functional Traits to Assess Community Assembly of Quercus Forests at Different Scales in the Semiarid Loess Plateau of China by YongFu Chai, TingTing Tian, Luyao Wang, Junxin Wei, Yao Xu, Peiliang Liu, Chengcheng Xiang, Ming Yue

“…In contrast to the phylogenetic convergence observed at the genus level, using plant DNA barcodes, we found that the entire forest communities and the tree layer exhibited phylogenetic randomness across all three spatial scales; even the shrub layer showed phylogenetic overdispersion with increasing scale. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. …”
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A multi-objective deep reinforcement learning method for intelligent scheduling of wind-solar-hydro-battery complementary generation systems by Yuanyu Ge, Jun Xie, Jiaqi Chang, Shuo Feng

“…However, integrating these sources poses challenges due to their randomness, volatility, and spatial–temporal mismatch of energy-electricity demand. …”
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Study on the influence mechanism and correlation model of rockfall parameters on indentation morphology by LI Hao, WANG Dong-po, YAN Shuai-xing, LI Wei, CHEN Hui, HE Si-ming

“…Characterized by their high - degree of suddenness, marked randomness, and wide - spread spatial distribution, rockfalls have become critical disaster - inducing factors that pose a significant threat to the safety of transportation infrastructure in mountainous areas. …”
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Methodological Integration of Machine Learning and Geospatial Analysis for PM10 Pollution Mapping by Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

“…RF excelled in capturing spatial heterogeneity and complex variable interactions, while KNN and NB exhibited limitations in managing feature dependencies and localized variability. …”
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Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus by Ke Xie, Tao Wang, Pan Zhong, Zihao Zhao, Zixiang Wang

“…The graph embedding algorithm is used to calculate feature vector representations of nodes in the network, which can capture complex relationships among nodes through biased random walks. …”
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