Construction of Ecological Security Patterns and Priority Identification for Restoration in High-Risk Geological Disaster Areas Coupled with InSAR Technology and Neural Network Models
Abstract
Geological disasters threaten ecological stability, yet restoration planning in high-risk areas remains difficult because deformation, environmental conditions, and hazard drivers vary across space and time. We developed a multimodal dataset, HGR-InSAR-2022, covering 12[Formula: see text]640[Formula: see text]km 2 and comprising 312 Sentinel-1 scenes, 1486 Sentinel-2 images, a 30-m digital elevation model, rainfall and geological layers, and 1276 documented landslide locations collected from 2018 to 2022. The dataset provides comprehensive spatial and temporal observations for characterizing terrain instability, ecological degradation, and heterogeneous environmental responses across disaster-prone landscapes. Based on this dataset, we propose AGRNet, an adaptive geospatial restoration network that integrates a constraint-aware restoration optimizer, an agent-based risk mapping unit, and an uncertainty-driven InSAR fusion module. The network combines InSAR-derived deformation, multispectral ecological indicators, topographic variables, and hazard factors to generate ecological security patterns and restoration-priority maps while quantifying prediction uncertainty. This multimodal integration enables the model to capture complex interactions between surface deformation, ecological conditions, and geological hazard processes at different spatial scales. On an independent 20% test set, AGRNet achieved an [Formula: see text]1-score of 0.91 and an intersection-over-union of 0.84 for ecological security classification, outperforming random forest and conventional convolutional neural network baselines by 8.7 and 5.1 percentage points, respectively. Its restoration-priority map achieved an area under the receiver operating characteristic curve of 0.94, while uncertainty modeling reduced deformation prediction RMSE by 16.3%. These results demonstrate that the proposed framework provides accurate, uncertainty-aware support for ecological restoration and geological-disaster mitigation.