The food and agriculture sector is a cornerstone of critical infrastructure (CI), underpinning global food security, public health, and economic stability. However, the increasing digitalization and connectivity of operational technologies (OTs) in this sector expose it to significant cybersecurity risks. Blockchain technology (BT) has emerged as a transformative solution for addressing these challenges by enhancing network security, traceability, and system resilience. This study presents a comprehensive review of BT applications in OT security for food and agriculture CI, employing bibliometric and content analysis methods. A total of 124 relevant articles were identified from six databases, including the Web of Science Core Collection and MEDLINE^®. Bibliometric analysis was conducted across five dimensions: publication year, literature type, journal distribution, country contributions, and keyword trends. The findings are meticulously organized through tables, charts, and graphs. The year 2018 marked a surge in research within this domain, with the IEEE Internet of Things Journal and IEEE ACESS emerging as the most prolific journals, each boasting nine publications. The United States, China, and India are at the forefront in terms of journal citation counts. Our analysis determined that a reference count of 37 serves as an appropriate threshold. Otoum Safa stands out as the author with the highest number of published articles, totaling four. Keywords such as “blockchain”, “internet of things”, “smart contract”, “security”, and “critical infrastructure” appear with significant frequency. The statistics, trends, and insights gleaned from this bibliometric analysis can guide researchers in the OTCI field to forge a coherent and logical research trajectory. Content analysis further identified six key research areas within this domain: identity authentication and data verification, secure access control, attack detection and perception, data security and protection, data backup and recovery, and attack assessment and attribution. Based on these insights, a general framework is proposed to guide future research and practical applications of BT in securing OT within food and agriculture CI. This study systematically analyzes the current research landscape, challenges, and opportunities for BT in securing the OT critical to food and agriculture CI. By bridging the gap between blockchain innovations and the operational needs of the food and agriculture sector, this work contributes to advancing strategic implementation and improving the security of CI systems.

In recent years, there has been a growing interest in remote sensing image–text cross-modal retrieval due to the rapid development of space information technology and the significant increase in the volume of remote sensing image data. Remote sensing images have unique characteristics that make the cross-modal retrieval task challenging. Firstly, the semantics of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Different combinations of basic units of semantic expression can generate diverse text descriptions. Additionally, these images exhibit variations in resolution, color, and perspective. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method, the progressiveness of which has been proved in the cross-modal retrieval of natural images. By jointly training the model with three tasks: image–text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC), we enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in joint representation expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method, which achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness.

Due to the swift growth in the scale of remote sensing imagery, scholars have progressively directed their attention towards achieving efficient and adaptable cross-modal retrieval for remote sensing images. They have also steadily tackled the distinctive challenge posed by the multi-scale attributes of these images. However, existing studies primarily concentrate on the characterization of these features, neglecting the comprehensive investigation of the complex relationship between multi-scale targets and the semantic alignment of these targets with text. To address this issue, this study introduces a fine-grained semantic alignment method that adequately aggregates multi-scale information (referred to as FAAMI). The proposed approach comprises multiple stages. Initially, we employ a computing-friendly cross-layer feature connection method to construct a multi-scale feature representation of an image. Subsequently, we devise an efficient feature consistency enhancement module to rectify the incongruous semantic discrimination observed in cross-layer features. Finally, a shallow cross-attention network is employed to capture the fine-grained semantic relationship between multiple-scale image regions and the corresponding words in the text. Extensive experiments were conducted using two datasets: RSICD and RSITMD. The results demonstrate that the performance of FAAMI surpasses that of recently proposed advanced models in the same domain, with significant improvements observed in R@K and other evaluation metrics. Specifically, the mR values achieved by FAAMI are 23.18% and 35.99% for the two datasets, respectively.

In recent years, there has been a growing interest in remote sensing image-text cross-modal retrieval due to the rapid development of space information technology and the significant increase in remote sensing image data volume. One approach that has shown promising results in cross-modal retrieval of natural images is the multimodal fusion encoding method. However, remote sensing images have unique characteristics that make the retrieval task challenging. Firstly, the semantic features of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Additionally, these images exhibit variations in resolution, color, and perspective. Different combinations of basic units of semantic expression can generate diverse text descriptions. These characteristics pose considerable challenges for cross-modal retrieval. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method. The model incorporates three tasks: image-text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC) task. By jointly training the model with these tasks, we aim to enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in feature expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method. This method achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness. Overall, this study focuses on remote sensing image-text cross-modal retrieval and introduces the MTGFE model, which combines multimodal fusion encoding with multiple tasks to enhance the model’s ability to capture fine-grained correlations. Additionally, a retrieval filtering method is proposed to improve retrieval efficiency. Experimental results demonstrate the effectiveness of the proposed method.

Accurate road graph extraction from satellite imagery is essential for large-scale mapping and geospatial analysis. Recent one-shot graph extraction frameworks based on foundation models have achieved promising performance, but their effectiveness decreases in complex environments where road structures are partially obscured by vegetation, buildings, shadows, and other surface conditions. These occlusion-induced disturbances lead to incomplete connectivity and degraded topology reconstruction, particularly under out-of-domain scenarios. This study proposes an occlusion-aware refinement framework to improve the robustness of satellite image road graph extraction while maintaining the original backbone architecture. The proposed framework introduces three complementary strategies: Synthetic Occlusion Augmentation for explicit occlusion-aware representation learning, an Occlusion-Adaptive Extended-Line strategy with Hard-Mining Topology Optimization for improved connectivity reasoning, and an Occlusion-Adaptive Node-Guided Resampling mechanism for reliable graph node localization. Experiments conducted on the Global-Scale road graph extraction benchmark demonstrate that the proposed method consistently improves topology reconstruction performance. Compared with the reproduced SAM-Road++ baseline, the proposed framework improves TOPO F1 from 61.81 to 62.56 on the in-domain split and from 46.93 to 51.51 on the out-of-domain split. Furthermore, the ID-OOD performance gap is reduced from 14.88 to 11.05, indicating enhanced robustness under unseen geographic conditions. The results demonstrate that explicitly modeling occlusion as a structured factor can effectively improve the generalization capability of satellite road graph extraction systems.

Remote sensing captions typically interleave domain-specific terminology, object attributes, spatial relations, and scene context within a single sentence, while the corresponding imagery contains targets spanning markedly different spatial extents—from individual vehicles to entire airport layouts. A single global text vector and fixed-scale visual pooling therefore tend to discard the local and hierarchical evidence needed for reliable image–text alignment. Moreover, standard contrastive objectives treat all unmatched pairs as equally unrelated, ignoring the fact that semantically adjacent scenes (e.g., airports and industrial zones) share substantial visual content. To address these issues, we develop a CLIP-style dual encoder with three targeted additions: a text encoder that preserves word-level, phrase-level, and sentence-level representations before fusing them through cross-granularity attention; a visual encoder that associates four hierarchical feature levels with scale-aware receptive-field configurations and incorporates SAM-guided local views during training; and a semantic membership-aware contrastive loss that applies detached, bounded discount weights to semantically close negatives without inverting their penalty sign. On the cleaned RSICD benchmark, the model achieves 87.5% mAP for in-domain prompt-based scene recognition and 34.37% mean recall for scene-constrained retrieval, outperforming the quoted RemoteCLIP and fully fine-tuned CLIP results in the scene-constrained retrieval comparison. Ablation confirms that each of the three components contributes meaningfully to the final performance. The deployable dual encoder contains approximately 59.7 million trainable parameters, and SAM is not required at inference.