DMS-OGSTV: A Novel Infrared Small Target Detection Based on Spatial-Temporal Tensor Model by Xiaoling Ge, Weixian Qian, Zipeng Fu

“…First, we construct the infrared sequence into a holistic spatiotemporal tensor model (STTM) to utilize both spatial and temporal information. Then, based on the sparse-enhanced Tucker decomposition framework, we design a multi-scale energy movement saliency map (MESM) from target motion characteristics. …”
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Deflection of screen surface and spatial deformation law of screen apertures during 1 mm elastic screening by Haishen JIANG, Ran YANG, Runyu LIU, Shijie YU, Peng GUO, Miao PAN, Long HUANG, Chenlong DUAN

“…Under the action of water and clay minerals, the process of wet material screening has some problems such as strong adhesion between fine particles, significant agglomeration effect, difficulty in particle separation and layering, and severe clogging on the screen surface, leading to difficulties in material sieving and poor screening performance, which can severely restrict the large-scale upgrading and processing of coal. To overcome the problems above, combining vibration testing and multi-flexible body dynamics simulation methods, the kinematic characteristics of elastic screen surface and 1 mm elastic screening test were carried out. …”
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Hyperspectral and Multispectral Remote Sensing Image Fusion Based on a Retractable Spatial–Spectral Transformer Network by Yilin He, Heng Li, Miaosen Zhang, Shuangqi Liu, Chunyu Zhu, Bingxia Xin, Jun Wang, Qiong Wu

“…Fusion that aims to emphasize spatial–spectral heterogeneous characteristics may significantly enhance the robustness of joint representation for multi-source data. …”
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Multibranch 3D-Dense Attention Network for Hyperspectral Image Classification by Junru Yin, Changsheng Qi, Wei Huang, Qiqiang Chen, Jiantao Qu

“…This network is able to reuse features to fully exploit the shallow spatial-spectral information of HSI. Meanwhile, the convolutional kernels of different sizes are used to extract multi-scale spatial-spectral features. …”
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Integrated outcomes of large-scale ecological restoration projects on biodiversity–eco-environment–society in China by Yi Wang, Changning Liu, Biying Liu, Tien Ming Lee

“…Yet, the integrated outcomes of these projects on the biodiversity–eco-environment–society dimensions remain unclear, but when available could be insightful for adaptation and adjustments, particularly in this United Nations Decade on Ecosystem Restoration (2021–2030). Based on multi-source data, we identified the categories and quantities of large-scale ERP implementation in the county-level. …”
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Integrating bottom-up energy model with urban parameterization for fine-scale anthropogenic heat estimation by Yichuan Chen, Chengliang Fan, Yuanyue Nie, Huixian Wu, Yingfen Zhu, Yundan Liao, Haoran Li, Liuyang Wu, Minjing Lao

“…This study aims to develop an anthropogenic heat flux (AHF) calculation method by integrating bottom-up energy model with urban parameterization. First, multi-source data were collected and processed, and the characteristics of AHE in buildings, industry and transportation were obtained respectively. …”
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Temporal and Spatial Changes of Dust Aerosol over Qinghai-Xizang （Tibet） Plateau and Analysis on Its Source Regions by Bentao LI, Lei ZHANG, Yunshu ZHANG, Haotian ZHANG, Gefei LU

“…Dust aerosol is an important component of the Earth's atmospheric aerosols， and has essential impacts on global climate， ecological environment and human body health.The spatial and temporal distributions of dust aerosol and their variations over Qinghai-Xizang （Tibet） Plateau are of great significance to be further investigated and estimated for evaluating the climatological and environmental effects of dust aerosol loading over Qinghai-Xizang （Tibet） Plateau.In this study， based on the data from different sources， including FengYun （FY） geostationary satellite remote sensing data and re-analysis data， both spatial and temporal characteristics of dust aerosol over Qinghai-Xizang （Tibet） Plateau are counted and analyzed within the period from 1999 to 2020.The intensity of dust activity over Qinghai-Xizang （Tibet） Plateau during monsoon period is stronger than that during non-monsoon period.More specifically， multi-year averaged seasonal aerosol optical depths （AODs） are 0.176， 0.064， 0.032 and 0.060， while dust optical depths （DODs） achieve 0.223， 0.118， 0.069 and 0.117 for spring， summer， autumn and winter.The dust activity is most active over Qaidam Basin， and small scale and isolated dust occurrences are also observed over the southern and western Qinghai-Xizang （Tibet） Plateau.The number of days， in which it is occurred with dust event， is 192， 218 and 212 from 2018 to 2020， respectively.The dust sources in East Asia （about 62%）， and Middle East and Central Asia （about 30%） are the main sources of dust aerosols for the northern and southern TP， separately.The dust originating from North Africa， mainly affects high-elevation regions in the southern Qinghai-Xizang （Tibet） Plateau.The intensity of dust activity over Qinghai-Xizang （Tibet） Plateau is relatively strong from 2000 to 2012， but its trend turns to be fluctuating from 2012 to 2020.The intensity and influencing area of dust activity over Qinghai-Xizang （Tibet） Plateau are both affected by variations of precipitation and wind over Taklamakan Desert， whose correlation coefficients with dust activity over Qinghai-Xizang （Tibet） Plateau are -0.56 and -0.32.…”
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Dynamic Assessment of Population Exposure to Urban Flooding Considering Building Characteristics by Shaonan Zhu, Xin Yang, Jiabao Yang, Jun Zhang, Qiang Dai, Zhenzhen Liu

“…Previous assessments relying on neighborhood block-scale population estimates derived from conventional census data have been constrained by significant spatial aggregation errors. …”
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Spatial patterns and key driving factors of wheat harvest index under irrigation and rainfed conditions in arid regions by Yongyu Chen, Yongyu Chen, Hengbati Wutanbieke, Dongdong Zhong, Dongdong Zhong, Jian Chen, Jian Chen, Zhen Huo, Zhen Huo, Hegan Dong, Hegan Dong

“…It reflects the "source-sink" relationship and biomass allocation strategies in crops. However, the spatial distribution patterns of wheat HI and their key driving factors in arid regions remain unclear.MethodsThis study was conducted in Xinjiang, a typical arid region of China, during 2022–2023, involving two years of large-scale systematic sampling. …”
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Segmentation and particle size analysis of coal particles based on ISUNet by Deqiang CHENG, Rui ZHANG, Tongxi XIE, Jingjing LIU, Lijuan ZHENG, Qiqi KOU, He JIANG

“…The ISUNet model introduces a compressed excitation atrous spatial pyramid pooling module and a Transformer-based multi-path iterative encoder. …”
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Multi-Modal Prototypes for Few-Shot Object Detection in Remote Sensing Images by Yanxing Liu, Zongxu Pan, Jianwei Yang, Peiling Zhou, Bingchen Zhang

“…In addition, the transferability of multi-modal few-shot detectors from natural scenarios to remote sensing scenarios remains unexplored, and previous training strategies for FSOD do not adequately consider the characteristics of text encoders. …”
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Multi-Model Ensemble Enhances the Spatiotemporal Comprehensive Performance of Regional Climate in China by Yan Wang, Yan-Jun Shen, Leibin Wang, Ying Guo, Yuanyuan Cheng, Xiaolong Zhang

“…The results indicate that CMIP6 models exhibit notable differences in simulating regional climate characteristics of China across different scales. Weighted multi-model ensemble schemes incorporating better-performing models consistently outperform equal-weight approaches, achieving an average 20.67% reduction in the DISO (distance between indices of simulation and observation) index, with temporal performance improvements being particularly pronounced. …”
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Spatiotemporal variation of spring phenology and the corresponding scale effects and uncertainties: A case study in southwestern China by Chongjing Zhu, Xiaojun She, Xiaojie Gao, Yajun Huang, Yelu Zeng, Chao Ding, Dongjie Fu, Jing Shao, Yao Li

“…Land surface phenology (LSP) derived from satellite observations has become a critical tool for tracking vegetation phenology across large spatial scales. However, LSP data from coarse spatial resolutions often mix phenological signals from multiple land cover types, a limitation that fine-resolution satellite data can help overcome. …”
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Impact of Urbanization on Surface Temperature in Morocco: A Multi-City Comparative Study by Mohamed Amine Lachkham, Lahouari Bounoua, Noura Ed-dahmany, Mohammed Yacoubi Khebiza

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Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection by Jianxun Zhao, Xin Wen, Yu He, Xiaowei Yang, Kechen Song

“…Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. …”
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Numerical Simulation and Improvement of Fiber Thrower Using Multi-Domain Coupling Schemes by Xu Qian, Su Yang, Chunbao Liu

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Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition by Peiwang Zhu, Chengwu Liang, Yalong Liu, Songqi Jiang

“…Secondly, an innovative multi-scale spatial–temporal feature interaction module is proposed to jointly learn sub-action dependencies and facilitate skeletal motion interactions, where long-range motion patterns are embedded to enhance correlation modeling. …”
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Evaluation of geological hazard susceptibility based on the multi-kernel density information method by Yang Li, Yutian Lei, Bo Chen, Jiale Chen

“…The MKDI method integrates information value with kernel density estimation, effectively capturing the spatial dependencies among samples. Furthermore, distinct bandwidths are prescribed for various scales of disasters to facilitate multi-kernel density estimation for geological hazards. …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). …”
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Study on the spatio-temporal evolution and multi-scale influencing factors of digital technology innovation: A case study of 41 cities in the Yangtze River Delta(数字技术创新的时空演进与多尺度影响因... by 李云霞(LI Yunxia), 高世杰(GAO Shijie), 李羚锐(LI Lingrui)

“…It is worth focusing on the development trend, spatial pattern, and multi-scale effects of digital technology innovation which is the core engine driving the development of the digital economy. …”
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