LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism by Yuliang Zhao, Yang Du, Qiutong Wang, Changhe Li, Yan Miao, Tengfei Wang, Xiangyu Song

“…Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. …”
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Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection by Shiva Agrawal, Savankumar Bhanderi, Gordon Elger

“…These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. …”
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Physics‐Informed Neural Networks Trained With Time‐Lapse Geo‐Electrical Tomograms to Estimate Water Saturation, Permeability and Petrophysical Relations at Heterogeneous Soils... by C. Sakar, N. Schwartz, Z. Moreno

“…Synthetic ERT surveys with electrode spacing 10 times larger than the numerical model resolution were conducted to provide 2D electrical tomograms. …”
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Enhancing seabed sediment classification with multibeam echo-sounding and self-training: a case study from the East Sea of South Korea by Changhoon Lee, Sujung Park, Daeung Yoon, Bo-Yeon Yi, Moonsoo Lim

“…To mitigate sample scarcity and class imbalance, a semi-supervised self-training loop iteratively added high-confidence pseudo-labels to the training set.ResultsField validation in the East Sea (Republic of Korea) showed that the Extreme Gradient Boosting model achieved the highest accuracy. Overall prediction accuracy increased from 60.81 % with the baseline workflow to 72.73 % after applying data interpolation, enhanced feature extraction, and self-training.DiscussionThe proposed combination of U-Net interpolation, multi-scale texture features, and semi-supervised learning significantly improves sediment classification where MBES data are incomplete and sediment samples are sparse. …”
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A long neglected world malaria map: Plasmodium vivax endemicity in 2010. by Peter W Gething, Iqbal R F Elyazar, Catherine L Moyes, David L Smith, Katherine E Battle, Carlos A Guerra, Anand P Patil, Andrew J Tatem, Rosalind E Howes, Monica F Myers, Dylan B George, Peter Horby, Heiman F L Wertheim, Ric N Price, Ivo Müeller, J Kevin Baird, Simon I Hay

“…The model incorporated data on Duffy negative phenotype frequency to suppress endemicity predictions, particularly in Africa. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. …”
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Depth Perception Based on the Interaction of Binocular Disparity and Motion Parallax Cues in Three-Dimensional Space by Shuai Li, Shufang He, Yuanrui Dong, Caihong Dai, Jinyuan Liu, Yanfei Wang, Hiroaki Shigemasu

“…In the future, it is necessary to explore methods for easier manipulating of depth cue signals in stereoscopic images and adopting deep learning-related methods to construct models and predict depths, to meet the increasing demand of human–computer interaction in complex 3D scenarios.…”
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Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

“…In addition, our study is the first to apply a pretrained ViT model to a dataset with different polarizations and spatial resolutions—the AI4Arctic Sea Ice Challenge dataset—to rigorously assess model adaptability. …”
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Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

“…Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. …”
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Microscopic pore combination type identification of tight sandstone reservoir based on improved swin transformer architecture by Zhenyu Pang, Zhicong Chen, Sijie Lu, Zhenbo Cai, Yuqing Lu, Mengting Peng

“…Experimental results demonstrate that SwinLSC achieves a prediction accuracy of 93.3 %, significantly outperforming the comparative models. …”
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Delimitation of Landslide Areas in Optical Remote Sensing Images Across Regions via Deep Transfer Learning by Zan Wang, Shengwen Qi, Yu Han, Bowen Zheng, Yu Zou, Yue Yang

“…A post-processing module is integrated into the Mask R-CNN architecture to address the challenge of overlapping mask predictions for individual landslide objects. The results indicate that the Mask R-CNN model exhibits superior overall performance in comparison with the U-Net model and is more suitable for tasks requiring detailed delineation of the object outlines in images. …”
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Evaluating Radio Occultation (RO) Constellation Designs Using Observing System Simulation Experiments (OSSEs) for Ionospheric Specification by Nicholas Dietrich, Tomoko Matsuo, Chi‐Yen Lin, Brandon diLorenzo, Charles Chien‐Hung Lin, Tzu‐Wei Fang

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Downhole Pressure Pulse Signal Recognition Based on SSA-CNN-LSTM by JIANG Panqin, LIU Xingbin, JIANG Zhicheng, LI Shanwen, HE Zhuang

“…It is found that the SSA-CNN-LSTM algorithm model outperforms traditional LSTM, CNN-LSTM, and PSO (particle swarm optimization) -CNN-LSTM models in terms of both fitting ability and prediction accuracy. …”
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Geomorphological and Geological Characteristics Slope Unit: Advancing Township-Scale Landslide Susceptibility Assessment Strategies by Gang Chen, Taorui Zeng, Dongsheng Liu, Hao Chen, Linfeng Wang, Liping Wang, Kaiqiang Zhang, Thomas Glade

“…A landslide susceptibility index system is developed using multi-source data, with susceptibility prediction conducted via the XGBoost model optimized by Bayesian methods. …”
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Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida by Inacio T. Bueno, Carlos A. Silva, Caio Hamamura, Victoria M. Donovan, Ajay Sharma, Jiangxiao Qiu, Jinyi Xia, Kody M. Brock, Monique B. Schlickmann, Jeff W. Atkins, Denis R. Valle, Jason Vogel, Andres Susaeta, Mauro A. Karasinski, Carine Klauberg

“…We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. …”
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YO-AFD: an improved YOLOv8-based deep learning approach for rapid and accurate apple flower detection by Dandan Wang, Dandan Wang, Huaibo Song, Huaibo Song, Huaibo Song, Bo Wang

“…The timely and accurate detection of apple flowers is crucial for assessing the growth status of fruit trees, predicting peak blooming dates, and early estimating apple yields. …”
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Enhancing Deep Learning Sustainability by Synchronized Multi Augmentation with Rotations and Multi-Backbone Architectures by Nikita Gordienko, Yuri Gordienko, Sergii Stirenko

“…Deep learning applications for Edge Intelligence (EI) face challenges in achieving high model performance while maintaining computational efficiency, particularly under varying image orientations and perspectives. …”
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Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong

“…Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutional neural networks. …”
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Detection of human activities using multi-layer convolutional neural network by Essam Abdellatef, Rasha M. Al-Makhlasawy, Wafaa A. Shalaby

“…This comparison underscores the model’s robustness, highlighting improvements in minimizing false positives and false negatives, which are crucial for real-world applications where reliable predictions are essential. …”
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Rain-Induced Shallow Landslide Susceptibility Under Multiple Scenarios Based on Effective Antecedent Precipitation by Chuanmei Cheng, Ying Li, Dong Zhu, Yu Liu, Yongqiu Wu, Degen Lin, Hao Guo

“…Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. …”
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