An accurate estimation of missing data in traffic flow is crucial in urban planning, intelligent transportation, economic geography, and other fields. Thus, improving the data quality of traffic flow is a necessary step in data modeling. Most existing studies use data-driven models to determine spatiotemporal patterns in traffic flow data and fill in the missing information automatically. However, simple data-driven models have unsatisfactory results for describing complex patterns in traffic flow and filling in missing data. This study treated the complex patterns in traffic flow as integrating multiple simple patterns and proposed a hybrid missing data imputation framework called ST-PTD. We used a specific time-series analysis to mine periodic patterns and proposed a novel matrix decomposition method to describe the trend of the traffic flow data. Furthermore, we fused the periodic and trend characteristics of the missing data using a novel dendritic neural network. We applied the framework in actual traffic flow data sets collected in Wuhan, China. The results showed that the ST-PTD framework outperformed the eight existing baselines in terms of imputation accuracy.

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.

As indoor space is the primary place for pedestrian activities, obtaining intelligent monitoring of indoor pedestrians is crucial for intelligent video surveillance. Previous studies have verified the effectiveness of spatiotemporal constraints in multitarget multicamera tracking (MTMCT). Pedestrians are generally subjected to fine spatiotemporal constraints within buildings, based on which the indoor geographic information system (GIS) technology can obtain automatic spatiotemporal modeling. Combined with artificial intelligence (AI) technology, we established a research framework of “GIS+AI+IMPMCT. ” Specifically, we proposed indoor multipedestrian multicamera tracking (IMPMCT) based on fine spatiotemporal constraints. First, we used GIS to map the indoor monitoring images of buildings and automatically model the fine spatiotemporal relationship among the semantics of the entrance of the surveillance zone. Subsequently, we used the machine learning model of pedestrian localization and tracking to obtain local trajectories of pedestrians and combined them with map information to extract entrance semantics of trajectories. Finally, we used the local trajectory semantics and surveillance entrance semantic constraints to obtain a fine spatiotemporal constraint weight matrix between trajectories and fused pedestrian’s apparent features to obtain trajectory matching results. To verify our method, we established an IMPMCT data set containing fine indoor spatiotemporal information. Our method obtained an IDF1 of 0.805, which is better than those of other methods. Furthermore, the tracking results obtained by the proposed method contained both image space and geospatial trajectories.

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature discrimination. Moreover, direct difference computation after feature extraction tends to retain task-irrelevant noise, limiting the model’s ability to capture meaningful changes. This study proposes a novel cross-spatial Transformer and symmetric difference localization network (CTSD-Net) for RSICC to address these limitations. The proposed Cross-Spatial Transformer adaptively enhances spatial-aware feature representations by guiding the model to focus on key regions across temporal images. Additionally, a hierarchical difference feature integration strategy is introduced to suppress noise by fusing multi-level differential features, while residual-connected high-level features serve as query vectors to facilitate bidirectional change representation learning. Finally, a causal Transformer decoder creates accurate descriptions by linking visual information with text. CTSD-Net achieved BLEU-4 scores of 66.32 and 73.84 on the LEVIR-CC and WHU-CDC datasets, respectively, outperforming existing methods in accurately locating change areas and describing them semantically. This study provides a promising solution for enhancing interpretability in remote sensing change analysis.

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.