In the era of big data, prediction has become a fundamental capability. Current prediction methods primarily focus on sequence elements; however, in multivariate time series forecasting, time is a critical factor that must not be overlooked. While some methods consider time, they often neglect the temporal distance between sequence elements and the predicted target time, a relationship essential for identifying patterns such as periodicity, trends, and other temporal dynamics. Moreover, the extraction of temporal features is often inadequate, and discussions on how to comprehensively leverage temporal data are limited. As a result, model performance can suffer, particularly in prediction tasks with specific time requirements. To address these challenges, we propose a new model, TE-LSTM, based on LSTM, which employs a temporal encoding method to fully extract temporal features. A temporal weighting strategy is also used to optimize the integration of temporal information, capturing the temporal relationship of each element relative to the target element, and integrating it into the LSTM. Additionally, this study examines the impact of different time granularities on the model. Using the Beijing International Airport station as the study area, we applied our method to temperature prediction. Compared to the baseline model, our model showed an improvement of 0.7552% without time granularity, 1.2047% with a time granularity of 3, and 0.0953% when addressing prediction tasks with specific time requirements. The final results demonstrate the superiority of the proposed method and highlight its effectiveness in overcoming the limitations of existing approaches.

Landslide disasters are complex spatiotemporal phenomena. Existing deep learning (DL) models for remote sensing (RS) image analysis primarily exploit shallow visual features, inadequately incorporating critical geological, geographical, and environmental knowledge. This limitation impairs detection accuracy and generalization, especially in complex terrains and diverse vegetation conditions. We propose Knowledge Graph Embedding in Swin Feature Pyramid Networks (KGE–SwinFpn), a novel RS landslide segmentation framework that integrates explicit domain knowledge with deep features. First, a comprehensive landslide knowledge graph is constructed, organizing multi-source factors (e.g., lithology, topography, hydrology, rainfall, land cover, etc.) into entities and relations that characterize controlling, inducing, and indicative patterns. A dedicated KGE Block learns embeddings for these entities and discretized factor levels from the landslide knowledge graph, enabling their fusion with multi-scale RS features in SwinFpn. This approach preserves the efficiency of automatic feature learning while embedding prior knowledge guidance, enhancing data–knowledge–model coupling. Experiments demonstrate significant outperformance over classic segmentation networks: on the Yuan-yang dataset, KGE–SwinFpn achieved 96.85% pixel accuracy (PA), 88.46% mean pixel accuracy (MPA), and 82.01% mean intersection over union (MIoU); on the Bijie dataset, it attained 96.28% PA, 90.72% MPA, and 84.47% MIoU. Ablation studies confirm the complementary roles of different knowledge features and the KGE Block’s contribution to robustness in complex terrains. Notably, the KGE Block is architecture-agnostic, suggesting broad applicability for knowledge-guided RS landslide detection and promising enhanced technical support for disaster monitoring and risk assessment.

To address the challenges of complex task matching, limited semantic representation, and low recommendation efficiency in remote sensing data acquisition for natural disasters, this study proposes a semantic path-guided recommendation method based on a knowledge graph framework. A disaster-oriented remote sensing knowledge graph is constructed by integrating entities such as disaster types, remote sensing tasks, observation requirements, sensors, and satellite platforms. High-order meta-paths with semantic closure are designed to model task–resource relationships structurally. A Meta-Path2Vec embedding mechanism is employed to learn vector representations of nodes through path-constrained random walks and Skip-Gram training, capturing implicit semantic correlations between tasks and sensors. Cosine similarity and a Top-K ranking strategy are then applied to perform intelligent task-driven sensor recommendation. Experiments on multiple disaster scenarios—such as floods, landslides, and wildfires—demonstrate the model’s high accuracy and robust stability. An interactive recommendation system is also developed, integrating data querying, model inference, and visual feedback, validating the method’s practicality and effectiveness in real-world applications. This work provides a theoretical foundation and practical solution for intelligent remote sensing data matching in disaster contexts.