arXiv Computer Vision By Yueqiang Zhang, Chang Ma, Shuixin Pan, Haibo Liu

Spaceborne differential photogrammetry for control-free measurement of large-gradient deformation with structural immunity and a predictable accuracy envelope

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers
Aug 10

GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models

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 AI
Sep 16

Composable multi-satellite precipitation estimation for evolving observing systems

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.

By Yunfan Yang, Haofei Sun, Xiuyu Sun, Wei Han, Xiaoze Xu, Xingtao Song, Jun Li, Zhiqiu Gao, Wei Huang
arXiv AI
Jul 14

Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

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.

By Ritu Yadav, Andrea Nascetti, Yifang Ban