RiverBedDynamics v1.0: a Landlab component for computing two-dimensional sediment transport and river bed evolution by A. D. Monsalve, S. R. Anderson, N. M. Gasparini, E. M. Yager

“…<p>Computational landscape evolution models (LEMs) typically comprise at least two interacting components: a flow hydraulic solver that routes water across a landscape and a fluvial geomorphological model that modifies terrain properties, primarily bed surface elevation. …”
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Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4 by T. Rackow, T. Rackow, X. Pedruzo-Bagazgoitia, T. Becker, S. Milinski, I. Sandu, R. Aguridan, P. Bechtold, S. Beyer, J. Bidlot, S. Boussetta, W. Deconinck, M. Diamantakis, P. Dueben, E. Dutra, E. Dutra, R. Forbes, R. Ghosh, H. F. Goessling, I. Hadade, J. Hegewald, T. Jung, T. Jung, S. Keeley, L. Kluft, N. Koldunov, A. Koldunov, T. Kölling, J. Kousal, C. Kühnlein, P. Maciel, K. Mogensen, T. Quintino, I. Polichtchouk, B. Reuter, D. Sármány, P. Scholz, D. Sidorenko, J. Streffing, B. Sützl, D. Takasuka, D. Takasuka, S. Tietsche, M. Valentini, B. Vannière, N. Wedi, L. Zampieri, F. Ziemen

“…A number of shortcomings in the original numerical weather prediction (NWP)-focused model configurations were identified and mitigated over several cycles collaboratively by the modelling centres, academia, and the wider nextGEMS community. …”
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Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana) by Jinjin Chen, Xuejian Li, Zihao Huang, Jie Xuan, Chao Chen, Mengchen Hu, Cheng Tan, Yongxia Zhou, Yinyin Zhao, Jiacong Yu, Lei Huang, Meixuan Song, Huaqiang Du

“…The optimal model is used to predict the forest age and simulate the spatial age distribution. …”
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Spatiotemporal stability estimation of physicochemical parameters of rice based on dynamic process orientation and refined abundance modification by Shanjun Luo, Qian Li, Wu Zhaocong

“…The results showed that while not significant, the accuracy of predicting the LAI, SPAD, and CCC of rice using the NDREgreen with the soil background removed was better than that of the NDRE model. …”
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Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning by Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, JunJue Wang, Dingyuan Chen, Yu Su

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A novel fuzzy inference method for urban incomplete road weight assignment by Longhao Wang, Xiaoping Rui

“…Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.…”
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RB-TRNet: a regularity-guided and boundary-aware architecture for toponym recognition from Chinese text by Haigang Sui, Jindi Wang, Xining Zhang, Huihan Ning, Wentao Wang, Lieyun Hu

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EO aided comprehensive flood management for the Brahmaputra and Barak river basin in NE India by S. P. Aggarwal, D. Barman, S. S. Kundu, S. Kurbah, R. Das

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Integrating Geopolymer Pervious Concrete Pavement for Sustainable Stormwater Management: A Case Study in the UAE by A. Wagih, M. Hamouda, H. El-Hassan

“…Additionally, spatial analyses revealed substantial reductions in flooded areas, reinforcing the viability of PCP as an effective solution for managing urban stormwater. …”
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Integrating AI-Driven Wearable Metaverse Technologies into Ubiquitous Blended Learning: A Framework Based on Embodied Interaction and Multi-Agent Collaboration by Jiaqi Xu, Xuesong Zhai, Nian-Shing Chen, Usman Ghani, Andreja Istenic, Junyi Xin

“…Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. …”
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The Role of Open-Source Data in Disaster Preparedness and Response. A Case Study on Flood Impact in Local Communities by A. Oyetunde, G. Neziri

“…Future research should explore the integration of higher-resolution imagery and predictive flood modeling to further improve impact assessments and inform long-term resilience strategies.…”
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Integrated Geomatic Solutions for the Digital Twin of Florence: Protecting the Arno River and its Historic Urban Landscape from Climate Risks by G. Tucci, L. Fiorini, L. Fiorini, A. Meucci, A. Meucci, A. Conti

“…The results demonstrate the necessity of combining multiple surveying technologies to obtain a holistic representation of the study area. By integrating spatial and predictive modeling, DiC&amp;RT enables real-time risk assessment and scenario simulations for climate change adaptation. …”
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BREATHE: A GeoAI-Powered Air Quality Monitoring and Forecasting System for Urban Sustainability by S. D. Adduri, T. Ali

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Development of an early warning system to reduce the impact of floods related to glacial lake outburst floods (SAGAZ) by C. Rada, A. Rivera, S. Alfaro, S. Alfaro

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Stereo Matching of High-Resolution Satellite Images via Hierarchical ViT and Self-Supervised DINO by X. He, M. Yang, S. Jiang, S. Jiang, W. Jiang, Q. Li

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SARforMINE. A 2D/3D Web Service for the Underground Gas Storage Impact Monitoring by J. Blachowski, S. Biernat, D. Głąbicki, P. Grzempowski, A. Kaczmarek, K. Owczarz, B. Rosiak, A. Buczyńska

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Simulation of Land Use and Land Cover Using the MOLUSCE Plugin Integrated with QGIS for the Western Himalayan Region of India by S. Kumari, S. Kumari, A. Roy

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Monitoring a flooded open-cast mine with combining remote sensing techniques by P. Kujawa, J. Wajs, D. Kasza, F. Remondino

“…The proposed workflow involves data fusion, multi-temporal analysis, and 3D reconstruction to detect morphological changes, monitor hydrodynamic behaviour, and predict future flood scenarios. The results show that the combination of data from active and passive sensors significantly improves spatial and temporal resolution, enabling accurate and detailed modeling.…”
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Monitoring of Forest Changes in Mount Kenya National Park Based on Domestic High-Resolution Satellites by K. Liu, Z. Luo, L. Liu, Y. Gan, X. Mu, H. Wang, X. Du

“…The optimized semantic segmentation model DeepLabv3+, which is based on the Pytorch deep learning framework, was used to achieve fine segmentation of forest element boundaries via a spatial pyramid pooling module and an encoder-decoder architecture. …”
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Enhancing Post-Disaster Damage Detection and Recovery Monitoring by Addressing Class Imbalance in Satellite Imagery Using Enhanced Super-Resolution GANs (ESRGAN) by U. Lagap, S. Ghaffarian

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