Current landslide displacement prediction models often suffer from insufficient integration between physical mechanisms and data-driven approaches, weak model generalizability, and limited operational applicability. To address these issues, this study develops a physics-informed online learning framework for landslide displacement prediction. The core of this framework is a Physics-informed Long Short-Term Memory network (Phys-LSTM). By embedding discretized forms of the stress balance, creep constitutive, and kinematic equations as hard constraints into the LSTM’s gating mechanisms and loss function, the model ensures physically consistent predictions and enhanced interpretability throughout the learning process. Leveraging real-time data streams from the Sichuan Provincial Geological Hazard Monitoring and Warning Platform, we developed an online processing pipeline for real-time multi-source data ingestion, automated quality control, spatiotemporal alignment, and physics-informed feature engineering. A progressive three-stage learning algorithm was designed to support model cold-start, incremental training, and rolling prediction. Validation across 45 model-development landslide sites and one independent application case demonstrated the framework’s significant superiority over traditional models in displacement prediction accuracy (RMSE ≤ 1.78 mm, R² ≥ 0.96), cross-site generalization stability, and its capability to capture accelerated deformation phases. This research indicates that deeply integrating geomechanical prior knowledge into an online learning framework can effectively improve the reliability, interpretability, and operational applicability of landslide displacement prediction models, thereby providing methodological support for subsequent landslide early warning applications.

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI.

Alpine gorge areas are typical regions where ecological fragility and geological hazard risk overlap strongly. Existing eco-geological assessments remain largely single- or dual-dimensional: remotely sensed indices describe surface status, ecosystem-service indices describe functional supply, and geological models are usually applied separately from ecological assessment, so each approach misses the information carried by the others. To close this gap, this study proposes a three-dimensional coupling framework for Eco-Geological Environment Carrying Capacity (EGECC) that integrates the Remote Sensing Ecological Index (RSEI), the Ecosystem Services Index (ESI) and the Geological Environmental Carrying Capacity (GECC), and applies it in Lixian County, Sichuan Province. The three indices were coupled at pixel level, and their coupling and vertical differentiation were analysed along an altitudinal gradient. Three findings are highlighted. (1) RSEI and ESI are only moderately correlated, and their altitudinal peaks are separated, so neither can substitute for the other. (2) GECC contributes the largest share of the variation in EGECC, indicating that the geological dimension carries information that is not a linear combination of the two ecological dimensions. (3) Consequently, the geological dimension cannot be reproduced from the ecological ones: degrading the framework to RSEI + ESI places part of the area in a lower class that the two-dimensional scheme would label as safe. A five-tier management strategy is outlined accordingly. The study provides a transferable basis for eco-geological zoning in alpine gorge counties.