The paper introduces a control‑free method for measuring large‑gradient deformation using spaceborne optical image correlation. By treating repeat‑pass correlation as a differential estimation problem, it jointly estimates surface displacement and revisit‑bias coefficients without surveyed ground control, providing a predictive accuracy envelope and per‑point uncertainty. Experiments on satellite data from recent earthquakes and glacier movement confirm the method’s low noise floor and robustness to stable‑terrain assumptions.
arXiv:2608.29680v1 Announce Type: new Abstract: Feed-forward 3D foundation models reconstruct perspective scenes in one pass. Satellite photogrammetry needs a different product, one that domain adapt...
arXiv:2608. 09325v1 Announce Type: new Abstract: Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment.
Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data sources they still generate high-confidence false alarms.
arXiv:2608.22300v1 Announce Type: new Abstract: Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery, and operational products still carry documente...
The paper introduces PRISMA, a generative framework that separates precipitation prior training from sensor-specific constraints, allowing flexible composition of heterogeneous satellite observations without retraining the core model. By integrating FY‑4B/AGRI, GPM/GMI, F16‑F18 SSMIS, and GPM/DPR‑Ka data, PRISMA consistently improves precipitation‑estimation accuracy and outperforms IMERG Final in CRPS and RMSE while maintaining positive Brier skill across thresholds. The framework supports rapid, accurate ensemble precipitation estimates, enhancing satellite‑based monitoring for hydrometeorological hazards.
arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.
arXiv:2609.21095v1 Announce Type: new Abstract: We present MarsFM, an image-conditioned latent flow-matching model for local Martian relief estimation from single-band HiRISE RED orthoimagery. The me...
arXiv:2608.29384v1 Announce Type: cross Abstract: The growing availability of dense commercial Synthetic Aperture Radar (SAR) time series enables temporal Interferometric SAR (InSAR) analysis, but fi...
arXiv:2607. 03663v1 Announce Type: cross Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery.
arXiv:2609.16772v1 Announce Type: new Abstract: A Digital Surface Model (DSM) is a fundamental geospatial data product for representing the elevation of the Earth's surface. Recently, 3D Gaussian Spl...
FORMSpoT presents a decade-long, 1.5 m resolution mapping of forest canopy height across France using SPOT‑6/7 data and a transformer model trained on airborne laser scanning. The derived FORMSpoT‑Δ disturbance polygons reveal that most French forest disturbances are small (<0.1 ha), a scale largely missed by coarser Sentinel‑1/2 and Landsat products, and that the method achieves high detection accuracy (F1 > 0.8) for events larger than 100 m² while still capturing finer events. Nationally, the approach distinguishes contrasting disturbance regimes—clear‑cut dominated in maritime pine plantations versus diffuse events in mountain forests—and tracks temporal dynamics such as the 2017‑2022 bark beetle crisis.
The paper investigates how processing parameters affect the accuracy of UAV photogrammetry. By testing 768 processing variants on ten datasets over 1.5 years, the study finds that the best configuration yields an RMSE of 16 mm, while the worst reaches 303 mm. Key factors include the number of ground control points, camera calibration corrections, and the use of Post‑Processing Kinematic GNSS for camera center determination, which together reduce systematic errors by more than half.