Non-Intrusive Load Monitoring (NILM) is widely employed to disaggregate a building’s total electrical load and estimate the energy consumption of individual devices. Recently, image-based NILM has garnered interest for its ability to capture temporal patterns in time series data. However, existing methods often convert time series data into a single image, resulting in information loss and inferior disaggregation performance. To address this issue, we propose a novel tensor-based image encoding approach for NILM. Our method leverages a proposed diagonal projection method, which enables nearly 100% recovery of raw time series values through inverse normalization. It only performs linear scaling without altering inherent data characteristics. We also integrate three additional image conversion techniques-Recurrence Plot, Gramian Angular Field and Markov Transition Field-to construct a four-dimensional image set that preserves data integrity. This set is then encoded into a multi-dimensional tensor, providing rich geometric features for model training. Additionally, we introduce an involution model to expand the convolutional receptive field and reduce parameter redundancy. Experimental results on the REDD and AMPds datasets demonstrate that our approach outperforms existing NILM techniques, highlighting its significant potential for building energy disaggregation.

Precision navigation and positioning are essential for lunar landing exploration missions. Terrain-relative navigation based on crater matching provides an effective means for lander position estimation as craters are distinguishing features on lunar. However, challenges arise from the lack of a one-to-one correspondence between image-detected craters and the crater database, as well as the inconsistency of the coordinate system of craters in the image and those in the database, which complicates the matching process. This article has proposed a lunar crater matching method with triangle-based global second-order similarity for precision navigation. First, craters are constructed as triangles as the basic matching primitives, and the topological relationships between craters are transformed into a graph structure. Then, geometric constraints and triangle removal rules are designed to retain high-quality triangles that satisfy the first-order similarity. Next, a second-order similarity metric is introduced to evaluate the consistency of the topology of crater distributions from a global perspective. The global optimal crater matching is determined by constructing a second-order similarity score matrix. The proposed method is validated by comprehensive experiments using both simulation data and Chang’E-6 landing phase data. The experimental results show that the proposed method has achieved the highest accuracy and robustness among the comparison methods, and the average position estimation accuracies are 0.44% and 0.41% of flight altitude for orbiting and landing scenarios.

The accurate prediction of sea surface temperature (SST) is highly important for climate change research and the management of marine ecosystems. Traditional numerical models rely on complex physical processes and precise initial conditions, resulting in high computational costs and limited generalizability. Although deep learning methods can improve the physical consistency and interpretability of ocean processes by incorporating physical constraints, their performance may degrade under anomalous or extreme SST conditions, where rigid constraints limit the model’s adaptability to complex variations. In contrast, incorporating motion-aware mechanisms enables models to flexibly capture dynamic patterns from data, thus increasing their responsiveness to nonstationary processes. Therefore, we propose a novel SST prediction model that integrates optical flow guidance with spatiotemporal awareness, aiming to enhance the modeling of motion and spatiotemporal features in SST evolution. The proposed model consists of three key modulesa spatiotemporal information extraction module (SIEM), a motion trend extraction module (MTEM), and a spatiotemporal feature fusion module (SFFM). First, the SIEM, composed of multiple layers of SwinLSTM, captures spatial and temporal dependencies in the SST time series. The MTEM then estimates the optical flow to extract motion information from the SST data. Finally, the motion information is used to dynamically adjust the spatiotemporal features, which are then fused in the SFFM. To evaluate the performance of the proposed model, we conducted SST prediction experiments at both daily (1–10 days) and weekly (1–10 weeks) scales over the South China Sea (SCS) and the Pacific Ocean, and compared the results with several existing models. The experimental results demonstrate that our model outperforms existing methods across multiple evaluation metrics, with superior prediction accuracy and robustness.

Mapping of planetary surface using stereo orbital imagery is a basic task of planetary exploration. As the number of orbiter images increases, excessive images will lead to unnecessary time costs and accuracy loss during the photogrammetric mapping process. However, existing studies focus more on the factors that affect the mapping accuracy and often ignores the redundancy and distribution rationality of images in the mapping process. Therefore, optimal data selection from extremely redundant orbiter images has become a challenging and urgent issue for planetary mapping. This paper proposes an effective optimal selection method of off-track stereo pairs, which extends the selection operation from a local to a global perspective, and reduces the redundancy of image pair subset covering the region of interest (ROI) based on multi-constraints such as quality constraints and global distribution constraints. Specifically, the ROI is rasterized and gridded, and the optimal and candidate stereo pairs are selected for each grid based on the quantified quality constraints. By combining global distribution constraints and consistency constraints between adjacent stereo pairs, an optimization model is constructed and the optimal subset of stereo pairs is determined using belief propagation algorithm. Experiments conducted on the selected ROIs of Moon and Mars indicate that our method significantly reduces data redundancy while ensuring the quality and rational distribution of stereo image pairs. Qualitative and quantitative evaluations demonstrate that the stereo pairs obtained using the proposed method perform exceptionally well in terms of intersection angles, redundancy rates, and other factors, showing overall superior performance compared to other methods. Additionally, the stereo pairs obtained by the proposed method were employed for photogrammetric mapping within the ROI, and the results validate the mapping products also exhibit satisfactory accuracy.

When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation capability object field (SOCO-Field) with sensor observation capability particle (SOC-Particle) as its core. SOCO-Field integrates SOC-Objects and GeoField for the discovery and association of sensors. SOC-Particle objectively exists on every location point in the geospatial environment, and SOC-Particles in space-continuous areas can further aggregate into SOC-Particle cluster to represent single- or multi-sensor-associated observation capability information. SOCO-Field includes three basic association behaviours and four further association behaviours to solve associated observation capability, in which the dynamic GeoField is the influential factor. An experiment on flood monitoring in the lower reaches of Jinsha River Basin is conducted. The sensor planner can view any sensor combination’s associated observation capability under a specific association mode and can effectively dispatch a multi-sensor for collaborated observation due to the effective modelling of associated sensor observation capability information (SOCInfo).