Most of the existing studies on the freeway hard-shoulder-running (HSR) strategy only focus on the traffic flow with human-driven vehicles (HDVs) but neglect the potential impacts of connected automated vehicles (CAVs) with various penetration rates on the macroscopic traffic flow. The purpose of this paper is to ascertain whether the effects of mixed traffic flow for the freeway HSR strategy differ from those of a traditional traffic flow or not. In this research, a sophisticated methodology is employed, integrating microscopic simulation with factorial experimental design. By meticulously examining and statistically analyzing the simulation results, significant and novel insights are obtained. We find a complex interplay between the penetration rate of CAVs and HSR strategy on traffic flow efficiency and safety metrics. Without HSR, the traffic flow efficiency does not exhibit an immediate enhancement as the penetration rate increases. Instead, a notable improvement in overall traffic flow efficiency is observed only when the penetration rate attains a specific threshold. After implementing the HSR, the penetration rate of CAVs has a pronounced positive influence both on the traffic efficiency and safety, regardless of the prevailing traffic flow conditions. However, different speed limits for the HSR can lead to varying degrees of efficiency improvement under different traffic demands. Within a penetration rate range of 0.4 to 0.7 during the congested traffic flow, the traffic efficiency achieved at a speed limit of 120 km/h does not necessarily exceed that achieved at a speed limit of 80 km/h. Finally, we observe that the opening of hard shoulders would result in a decrease in traffic safety under congested traffic conditions. This finding provides important guidance for determining the appropriate timing of hard shoulder utilization in mixed traffic flow scenarios in the future.

For subpixel mapping (SPM), ensuring the operational efficiency of the algorithm and mitigating the effect of abundance errors often cannot be achieved simultaneously. To solve the problem, we propose a new SPM method based on the spatial adaptive attraction model (SAAM) and conditional random fields (CRFs). Firstly, the proposed SAAM obtains the spatial adaptive attraction value by adaptively adjusting the spatial attraction value obtained using the traditional spatial attraction model, thereby turning the display form of the abundance constraints in the SPM into an implicit form for expression, to perform the physical significance of the abundance constraints with the relative size of the attraction value of each subpixel. Secondly, the spatial adaptive attraction value of the implicitly represented abundance constraints and the local spatial smoothing prior are modelled in the CRFs, and the model makes full use of the spatial information in the label field while considering the abundance constraint. Thirdly, Graph-cut is used to optimize the model, make the proposed SPM can not only guarantee the operational efficiency, but also extinguish the influence of abundance error and decrease the noise artefact on the results of SPM. Experiments on three remote sensing images show that the proposed SPM accuracy is considerably better than the previously available SPM methods and is the least time-consuming. This study provides a new solution for the SPM of remote-sensing images.

Since the proposal of the "2030 Agenda", the United Nations Sustainable Development Goal indicator 11.7.1 aims to calculate the accessibility and quality of urban open spaces(UOS). An accurate and rapid assessment framework of UOS is of great significance for urban sustainable development. Previous research on UOS has mainly focused on the evolution patterns of UOS, with little research on assessments of their accessibility for different population structures (i.e., men vs. women, young vs. older). In this study, a U-Net deep learning network was used for training from 3072 annotated samples of urban green spaces(UGS) which was created based on Gaofen-2 remote sensing images. The trained model was used to identify UGS within five districts of Beijing at sub-meter level, incorporated with Open Street Map and area of interest data. A spatial analysis was conducted for accessibility of UOS, finding that most of the UOS in the central urban area of Beijing can be reached within 10 minutes, but access to the eastern and western edges is poorer (more than 30 minutes). Finally, using Worldpop data, the accessibility of UOS was statistically analyzed for different ages and genders. The results show that UOS accessibility rate for the elderly and children reaches over 90% (10 minutes accessibility).

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.

Cultivated land is fundamental to human survival and development. China attaches great importance to the utilization and protection of cultivated land. In the field of administrative law enforcement, the Ministry of Natural Resources, as the competent authority, has regularly released text of cases involving illegal occupation of cultivated land for a long time. Such unstructured data is of low value density and limited usability. In this paper, the structured information such as location, time, event type, offence and occupied area can be quickly and efficiently extracted from the administrative cases through event extraction technology, so as to promote the use of historical case information and the study of intelligent law enforcement and case handling for administrative agencies. Firstly, the real-case data is collected from the official website of the Ministry of Natural Resources using a thematic web crawler. Subsequently, it constructs an entity argument recognition framework, defining the types of illegal cultivated land use events and their argument compositions. A training dataset is built for illegal cultivated land use cases through manual annotation. Finally, two sets of models based on statistical-based machine learning and deep learning are employed to optimize the extraction results of event arguments. Experiments show that the F1 score for the event argument extraction reaches 90.81% with BiLSTM-CRF model. The research in this paper achieves effective extraction of case information, verifies the effectiveness of existing models in event extraction from administrative law enforcement case texts, and provides effective support for deeper research in this field in the future.

Most existing the freeway hard shoulder running (HSR) strategy literature only focuses on the traffic flow with human-driven vehicles (HDVs) but neglects the possible impacts of connected automated vehicles (CAVs) with various penetration rates on the macroscopic traffic flow. The purpose of this paper is to detect whether the effects of mixed traffic flow for the freeway HSR strategy differ the traditional traffic flow or not. An approach based on microscopic simulation with factorial experimental design is adopted in this study, and novel results are obtained from the discussion and statistical analyses of simulation results. The results showed that the penetration rates of CAVs have a positive influence both on the traffic efficiency and safety on freeway HSR regardless of the prevailing traffic flow conditions. Furthermore, the implementation of the HSR strategy has the potential to markedly enhance traffic efficiency across a range of varying traffic demands. However, different speed limits for the HSR can lead to varying degrees of efficiency improvement under different traffic demands. Finally, we find a complex interplay between traffic flow conditions and the impact of HSR on TTC values and overall traffic safety.