arXiv:2609.12744v1 Announce Type: new Abstract: Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground p...
Pretrained Earth observation models may reduce the effort needed to map surface water, but their value depends on the alternative against which they are compared. We evaluated Prithvi-EO-2.0-Tiny usin...
Cryo-Bench is a new benchmark that evaluates foundation models for cryosphere mapping, comprising six semantic‑segmentation datasets across five cryospheric components (supraglacial debris, glacial lakes, sea ice, calving fronts, and Antarctic ice‑shelf extent). The benchmark includes multispectral, RGB, and SAR observations from under‑represented regions and tests thirteen geo‑foundation models alongside U‑Net and Vision Transformer baselines. Results show that with frozen encoders U‑Net slightly outperforms TerraMind, but the difference is not statistically significant; fine‑tuning with learning‑rate optimization can dramatically improve performance for some models, while in few‑shot scenarios several foundation models retain over 90 % of their full‑label accuracy.
arXiv:2610.34904v1 Announce Type: cross Abstract: Pretrained Earth observation models may reduce the effort needed to map surface water, but their value depends on the alternative against which they...
arXiv:2610.19763v1 Announce Type: new Abstract: Physics-guided networks for underwater image restoration predict transmission and backscatter and invert the image-formation model, yet rarely test whe...
arXiv:2608. 14935v1 Announce Type: cross Abstract: Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships.
arXiv:2609.37565v1 Announce Type: new Abstract: Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet tran...
The Digital Typhoon Dataset V3 extends the longest typhoon satellite image collection to nearly 50 years, now including multispectral satellite data for multi‑channel modeling. The study benchmarks machine learning models across the Northern and Southern Hemispheres, finding that multi‑channel inputs do not consistently outperform single‑channel ones for pressure regression, with ResNet‑18, full‑resolution images, and Channel 1 delivering the best single‑channel results. Systematic bias analysis reveals that intensity regression underestimates strong typhoons, and imbalance‑aware weighting strategies fail to fully correct this bias.
Automated detection of subsurface cavities from Ground Penetrating Radar (GPR) is most difficult in soft, high-water-content ground, where conductive, water-saturated soil attenuates the signal and degrades cavity reflections, yet this is also the condition under which cavities most readily form. This paper proposes TriView-YOLO, a multi-view YOLOv12 detector for road cavity screening in such ground.
The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.
The study trains a convolutional LSTM on ten annual Earth Observation variables to forecast desertification dynamics in Ceará, Brazil, achieving modest improvements over persistence and capturing 53‑69% of the regional degradation trend. While the model slightly outperforms simple baselines one to four years ahead, it explains only a small fraction of cell‑level variance, indicating that EO data are more reliable for regional trends than for individual pixel predictions. The authors also demonstrate that elevation and EVI channels degrade forecast skill, and use Monte Carlo dropout to quantify uncertainty in future degradation maps.
The paper performs a systematic hyperparameter search across five deep learning architectures (DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2) and nine context lengths for forecasting significant wave height (Hs) at a single buoy, then re‑evaluates the best configurations on a 47‑buoy, 37‑year dataset. Across all models, performance converges to a very small spread (SD = 0.0014 m², 0.8% of the grand mean), yet all models still outperform persistence, though none consistently outperforms the others. The study finds that persistence already captures the dominant linear‑inertial signal in univariate Hs, and that model‑class differences are dwarfed by cross‑buoy variance, suggesting diminishing returns for further architecture engineering under univariate input settings.