Remote sensing change detection (CD) involves identifying differences between two satellite images of the same geographic area taken at different times. It plays a critical role in applications such as urban planning and disaster management. Traditional CD methods rely on manually extracted features, which often lack robustness and accuracy in capturing the details of objects. Recently, deep learning-based methods have expanded the applications of CD in high-resolution remote sensing images, yet they struggle to fully utilize the multi-level features extracted by backbone networks, limiting their performance. To address this challenge, we propose a Semantic-Guided Cross-Attention Network (SCANet). It introduces a Hierarchical Semantic-Guided Fusion (HSF) module, which leverages high-level semantic information to guide low-level spatial details through an attention mechanism. Additionally, we design a Cross-Attention Feature Fusion (CAFF) module to establish global correlations between bitemporal images, thereby improving feature interaction. Extensive experiments on the IWHR-data and LEVIR-CD datasets demonstrate that SCANet significantly outperforms existing State-of-the-Art (SOTA) methods. Specifically, the F1-score and the Intersection over Union (IoU) score are improved by 2.002% and 3.297% on the IWHR-data dataset and by 0.761% and 1.276% on the LEVIR-CD dataset, respectively. These results validate the effectiveness of semantic-guided fusion and cross-attention feature interaction, providing new insights for advancing change detection research in high-resolution remote sensing imagery.

Hydrological signatures (HS) have proven to be highly effective in calibrating physically‐based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)‐based hydrological models has been limited. To address this gap, we propose a novel HS‐informed framework that dynamically integrates HS into DL parameterization through a multi‐task learning approach. This study evaluates the impact of HS integration on model performance using a large‐scale, global hydrological data set. The HS‐informed model achieved a significant performance improvement, with a median Nash‐Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow‐dominated (+0.135), drought‐prone (+0.148), and flood‐prone basins (+0.159). Sensitivity analysis further revealed that the HS‐informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30‐day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long‐term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non‐climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point‐error‐based calibration in AI‐driven hydrological modeling.

China’s Ecological Protection Red Lines (ERLs) policy has proven effective in constructing regional ecological security patterns and protecting ecological space. However, the existing methods for the identification of high conservation value areas (HCVAs) usually use physical models, whose parameters and processes are complex and only for a single service, affecting the ERL delineation. In this study, the data-driven machine learning (ML) models were innovatively applied to construct a framework for ERL identification. First, the One-Class Support Vector Machine (OC-SVM) was used to generate negative samples from natural reserves and ecological factors. Second, the supervised ML models were applied to predict the HCVAs by using samples. Third, by applying the same ecological factors, the traditional physical models were used to assess the ecological services of the study area for reference and comparison. Take Chengdu–Chongqing Urban Agglomeration (CY) as a case study, wherein data from 11 factors and 1822 nature reserve samples were prepared for feasibility verification of the proposed framework. The results showed that the area under the receiver operating characteristic curve (AUC) of all ML models was more than 97%, and random forest (RF) achieved the best performance at 99.57%. Furthermore, the land cover had great contributions to the HCVAs prediction, which is consistent with the land use pattern of CY. High-value areas are distributed in the surrounding mountains of CY, with lush vegetation. All of the above results indicated that the proposed framework can accurately identify HCVAs, and that it is more suitable and simpler than the traditional physical model. It can help improve the effectiveness of ERL delimitation and promote the implementation of ERL policies.

Accurate landslide segmentation from multitemporal remote sensing remains challenging due to strong class imbalance, heterogeneous background disturbances, and ambiguous boundaries induced by shadows, vegetation dynamics, and sensor noise. We propose Change-driven Hysteresis and Inference-ensemble Pseudo-labeling for Landslide Segmentation (CHIPS), a semi-supervised framework that leverages change cues to scale high-quality supervision while controlling error propagation in pseudo-label learning. CHIPS uses LandTrendr-derived spectral change descriptors as the primary representation and couples them with a hysteresis-based selection strategy that assigns pseudo-labels via dual thresholds, enabling confident positives and confident negatives while deferring uncertain pixels to an ignored set. This design explicitly balances precision–recall trade-offs and mitigates confirmation bias under severe foreground sparsity. To further stabilize learning, we integrate an inference-ensemble mechanism that aggregates multiple stochastic predictions (e.g., perturbation- or dropout-based) to estimate pixel-wise confidence and improve pseudo-label reliability. A teacher–student training scheme with exponential moving average supervision combines supervised segmentation loss with pseudo-label and consistency objectives under a scheduled ramp-up. Experiments on a large-scale landslide dataset constructed from change-map patches demonstrate that CHIPS consistently improves intersection-over-union and boundary delineation over fully supervised baselines and common semi-supervised alternatives, particularly in challenging terrain and low-label regimes. The proposed framework offers a practical and scalable solution for regional landslide mapping using change-driven priors and robust pseudo-labeling.