An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

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Vegetation classification in a subtropical region with Sentinel-2 time series data and deep learning by Ming Zhang, Dengqiu Li, Guiying Li, Dengsheng Lu

“…However, how irregular time series data affect vegetation classification in deep learning models is poorly understood. …”
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ForestSemantic: a dataset for semantic learning of forest from close-range sensing by Xinlian Liang, Hanwen Qi, Xuejie Deng, Jianchang Chen, Shangshu Cai, Qingjun Zhang, Yunsheng Wang, Antero Kukko, Juha Hyyppä

“…However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. …”
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A Progressive Clustering Approach for Buildings Using MST and SOM with Feature Factors by Tianliang Zhang, Xiaoji Lan, Jianhua Feng

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SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks by Chenghao Zheng, Yunfei Qiu, Jian Yang, Bianying Zhang, Zeyuan Li, Zhangxiang Lin, Xianglin Zhang, Yang Hou, Li Fang

“…Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. …”
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Fuel detection in forest environments training deep learners with smartphone imagery by F. Pirotti, F. Pirotti, A. Carmelo, E. Kutchartt, E. Kutchartt, E. Kutchartt

“…Unmixing mixtures in images is one of the challenges for extracting information from data. Forest environments are particularly complex due to the relatively irregular structure of trees, shrubs and low vegetation. …”
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GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images by Xiaokai Jiang, Xuewen Ding, Jinying Ma, Chunyu Liu, Xinyi Li

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LiDAR-Based Detection of Urban Trees Using a Backpack System by M. F. da Silva, L. F. Castanheiro, A. M. G. Tommaselli, A. M. G. Tommaselli, R. C. dos Santos, R. C. dos Santos, M. Galo, M. Galo

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Simulation of Delamination Evolution of Slab Ballastless Track under Vertical Impact by Yu Liu, Qianqi Xu, Xiaodan Sun, Guotao Yang, Guotang Zhao

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Exploring State Space Models in LiDAR Point Cloud Segmentation by D. Lu, L. Xu, R. Wang, J. Li

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Методика оценки информативности маршрута движения by Valentin Vladimirovich Vasil’Ev, Maxim Yur’evich Manzin

“…The article develops the estimation technique of informative content of an isolated irregularity and a track section as a whole. …”
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Modeling the Impact of Landscape Dynamics on Soil Erosion in Eastern DR Congo: Implications for Sustainable Land Management by Jean Nacishali Nteranya, Andrew Kiplagat, Elias K. Ucakuwun, Chantal Kabonyi Nzabandora

“…In addition, the impact of landscape structure on soil erosion dynamics has not been assessed yet in this region despite that this information is crucial for sustainable land management. …”
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Point Cloud Adversarial Perturbation Generation for Adversarial Attacks by Fengmei He, Yihuai Chen, Ruidong Chen, Weizhi Nie

“…Point cloud contains rich 3D object geometry information, which is an important 3D object data format widely used in many applications. …”
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ENSO Forecasts With Spatiotemporal Fusion Transformer Network by Anming Zhao, Mengjiao Qin, Sensen Wu, Renyi Liu, Zhenhong Du

“…However, the long-term prediction of ENSO persists as a challenge because of its diversity, irregularity, and asymmetry. Here, we develop a spatiotemporal fusion transformer network (STFTN), which designed a parallel encoder structure to effectively extract the spatiotemporal information from sea surface temperature anomaly and Niño3.4 index simultaneously, thereby enhancing the precision of Niño3.4 index forecasts. …”
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GD: Location-Aware Key Management for General Deployment of Wireless Sensor Networks by JongHyup Lee, Taekyoung Kwon

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Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data by Shihua Li, Shunda Zhao, Zhilin Tian, Hao Tang, Zhonghua Su

“…Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. …”
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An Analysis of Capital Market Using Network Approach by Reza Taghizadeh, Mohammad Abdzadeh Kanafi

“…They may have higher connections, faster access to information, and greater influence over other units' price changes. …”
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Assessing climate change risk and vulnerability among Bhil and Bhilala tribal communities in Madhya Pradesh, India: a multidimensional approach by Amit Kumar, T. Mohanasundari

“…However, household perceptions reveal a high awareness of climatic changes, with 97% of respondents reporting irregularity in rainfall and 98% documenting increased summer hot days. …”
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Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection by Jingwen Zhao, Jianchao Li, Wei Zhou, Haohao Ren, Yunliang Long, Haifeng Hu

“…LiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. …”
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MAIN REGULARITIES OF FAULTING IN LITHOSPHERE AND THEIR APPLICATION (BASED ON PHYSICAL MODELLING RESULTS) by S. A. Bornyakov, K. Zh. Seminsky, V. Yu. Buddo, A. I. Miroshnichenko, A. V. Cheremnykh, A. S. Cheremnykh, A. A. Tarasova

“…Multiple correlation equations are proposed to show relationships between AADIF's width (М), H, η and V for faults of various morphological and genetic types. The irregularity of AADIF in time and space is characterised in view of staged formation of the internal fault structure of such areas and geometric and dynamic parameters of AADIF which are changeable along the fault strike. …”
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