Volcano activity classification from synergy of EO data and machine learning: an application to Mount Etna volcano (Italy) by C. Petrucci, G. Romoli, A. Pignatelli, E. Trasatti, F. Zuccarello, F. Greco, M. Dozzo, G. Bilotta, F. Spina, G. Ganci

“…The study addresses challenges like temporal and spatial disparities and class imbalances through data preprocessing, ensuring a reliable dataset for training and validation. …”
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Lower ambulatory care availability and greater hospital capacity are associated with higher hospital case volumes by Doreen Müller, Manas K. Akmatov, Dominik Graf von Stillfried

“…Global Moran’s I confirmed spatial clustering, and GWR revealed heterogeneous effects of primary-care access on hospital admissions, whereas bed capacity uniformly increased hospital cases and shorter GP distances consistently predicted more office visits across Germany. …”
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Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS by Erin Boettcher, Edmund Hodges-Kluck

“…Starburst wind models predict that metals and energy are primarily carried out of the disk by hot gas ( T > 10 ^6 K), but the low energy resolution of X-ray CCD observations results in large uncertainties on the mass and energy loading. …”
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Differentiating Cheatgrass and Medusahead Phenological Characteristics in Western United States Rangelands by Trenton D. Benedict, Stephen P. Boyte, Devendra Dahal

“…Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. …”
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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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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

“…Water dynamics at the subsurface was accurately predicted with an average error of ∼3%. Adding water content measurements to PINNs training improved the system outcomes, mainly at the ERT low sensitivity zones. …”
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HO 2 Generation Above Sprite‐Producing Thunderstorms Derived from Low‐Noise SMILES Observation Spectra by T. Yamada, T. O. Sato, T. Adachi, H. Winkler, K. Kuribayashi, R. Larsson, N. Yoshida, Y. Takahashi, M. Sato, A. B. Chen, R. R. Hsu, Y. Nakano, T. Fujinawa, S. Nara, Y. Uchiyama, Y. Kasai

“…A total of three areas was identified with enhanced HO 2 levels of approximately 10 25 molecules. A chemical sprite model indicates an increase in HO 2 in the considered altitude region; however, the predicted production due to a single sprite event is smaller than the observed enhancement. …”
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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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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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Shafranov-shift destabilization of ballooning-type micro-instabilities by X. Jian, V. Chan, Z. Qiu, S. Ding, C. Holland, E. Bass, A. Garofalo, X. Liu

“…The reduced transport model TGLF can capture the physics reasonably well.…”
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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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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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Analysing cold-climate urban heat islands using personal weather station data by Jonathon Taylor, Charles H. Simpson, Jaana Vanhatalo, Hasan Sohail, Oscar Brousse, Clare Heaviside

“…Urban intensification of extreme heat was greater than extreme cold reduction. Practice relevance Urban climate research typically relies on sparse networks of official or researcher-deployed weather stations, interpolations thereof, land-surface temperature observations or urban climate models. …”
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Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: a case study in Jiangsu province, China by Lizhi Miao, Jvmin Wang, Kaiwen Wu, Heng Xu, Xiying Sun, Gang Lu, Mei-Po Kwan

“…The model demonstrated excellent performance in predicting carbon (R2 = 0.91, 10-fold CV R2 = 0.80). …”
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Interface Thermal Conductance of Nonequilibrium Thermal Transport across Nanoscale Metal Multilayers by Donghao Li, Zhongyin Zhang, Ziyang Wang, Gen Li, Jing Zhou, Jie Zhu, Dawei Tang

“…These experiment results revealed that nonequilibrium ITC at the Au/Cr interface closely matched the predicted value of 6 GW m−2 K−1 by the electronic diffuse mismatch model, which captures ultrafast thermal behavior within the first few picoseconds following femtosecond laser heating. …”
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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

“…Notably, this study introduces the use of the attention mechanism in ViTs to elucidate model decision-making, providing interpretability by highlighting regions that influence predictions and revealing why the model succeeds or fails in specific cases. …”
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Future Dynamics of Drought in Areas at Risk: An Interpretation of RCP Projections on a Regional Scale by Pietro Monforte, Sebastiano Imposa

“…Data were aggregated on a 0.50° × 0.50° spatial grid and bias-corrected using linear scaling. …”
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The roles of surface processes in porphyry copper deposit preservation by B. Hadler Boggiani, T. Salles, C. Mallard, N. Atwood

“…While our landscape evolution model successfully predicts the known emplacement depths for the North and South Andean deposits younger than 20 <span class="inline-formula">Myr</span>, it also predicts depths exceeding 6 <span class="inline-formula">km</span> for Central Andean deposits older than 60 <span class="inline-formula">Myr</span>. …”
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Tree species classification using intensity patterns from individual tree point clouds by Andreas Tockner, Ralf Kraßnitzer, Christoph Gollob, Sarah Witzmann, Tim Ritter, Arne Nothdurft

“…However, recent studies assessing tree species labels on single tree point clouds have been insufficiently accurate in complex forest ecosystems; moreover, explainability of machine-learning methods used in published studies has been insufficient. Whether the predictions of black-box models suffer from over-fitting or whether they are based on characteristic species traits often remains unclear. …”
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Skin-Inspired Magnetoresistive Tactile Sensor for Force Characterization in Distributed Areas by Francisco Mêda, Fabian Näf, Tiago P. Fernandes, Alexandre Bernardino, Lorenzo Jamone, Gonçalo Tavares, Susana Cardoso

“…The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. …”
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