International Conference on Geoinformatics : [proceedings]

Exploring the public's cognitive status, participation level, and emotional tendency towards intangible cultural heritage (ICH) is conducive to gaining insights into the public's attitudes and needs towards traditional culture, and provides a key basis for accurately formulating intangible cultural heritage protection and inheritance strategies. This paper crawls ICH text data from microblogs, adopts word frequency analysis, theme mining, emotion classification and other methods to excavate the deeper meaning of the text, and explores the public's real concerns, emotional attitudes and their formation mechanisms, and the results show that: 1) the public's perception of the ICH presents a multi-dimensional feature, and through theme analysis, it is found that the public discussion mainly focuses on three dimensions: cultural inheritance and development (52%), implementation and experience of the activities (32%), and introduction and promotion of ICH (16%). Among them, the proportion of topics related to the introduction and promotion of ICH is relatively low in microblogging, and it is suggested that the relevant departments increase the publicity and promotion of specific ICH. 2) The results of the sentiment analysis show that positive sentiments dominate the public's sentiment towards ICH. The average value of sentiment in each region exceeds 0.67 (out of 1), with Jiujiang City (0.8) and Ganzhou City (0.79) significantly leading the way. 3) Spatial econometric analysis reveals significant regional differences in the distribution of ICH sentiment. This difference was jointly influenced by multiple factors, of which the number of ICHs, GDP, and education expenditure level were the three most explanatory influences, with regression coefficients of 0.911,0.906, and 0.897 (p<0.01), respectively. This finding provides an important reference for the development of differentiated ICH protection policies.

Since 2021, the “low-altitude economy” has been successively incorporated into national plans and government work reports, emerging as a strategic emerging industry. As an economic form driven by low-altitude airspace, its development core lies in transportation, and the key to breakthroughs is traffic management. This paper sorts out the connotations of low-altitude economy, transportation, and traffic management, analyzes the elements of low-altitude economic development and traffic management, and explores the management forms of low-altitude manned aircraft, unmanned aerial vehicles (UAVs), and urban air mobility. The study finds that it is necessary to break through traditional management bottlenecks with a “low-altitude mindset”, and build a traffic management system adapted to the development of the low-altitude economy through mechanism innovation, regulation improvement, and talent cultivation, so as to promote the low-altitude economy to become a new growth pole of the national economy.

As the core growth pole of the Central Plains Urban Agglomeration, Zhengzhou's rapid urbanization has significantly disrupted regional ecological security patterns. This study integrates multi-temporal Landsat remote sensing data (2000-2020) to develop an Improved Remote Sensing Ecological Index (IRSEI) that synthesizes fractional vegetation cover (FVC), normalized difference built-up index (NDBI), and land surface temperature (LST). Combining a Geographically and Temporally Weighted Regression (GTWR) model, we systematically quantify the ecological response mechanisms to urban expansion across 12 counties/districts in Zhengzhou. Results reveal three key insights: First, spatial patterns exhibit “monocentric dominance-gradient differentiation,” with the southeastern plains (Zhongmu County, Xinzheng City) experiencing an annual expansion intensity of $\text{8. 2 1 \%}$, driving increases of 35.6% in patch density and 59.4% in landscape shape index, while the northwestern ecological barrier zone (Dengfeng City, Xingyang City) maintained a maximum patch index above 0.62 under ecological conservation redline constraints. Second, IRSEI evolution displays a “rise-then-decline” trend, with its mean value fluctuating from 0.469 (2000) to 0.487 (2020). Ecological quality improved by 12.6% in northwestern mountainous areas, whereas the southeastern core zone (Jinshui District, Zhongyuan District) suffered a 7.3% IRSEI decline due to synergistic heat island effects and construction land sprawl. Third, GTWR modeling identifies expansion intensity and landscape morphology as dominant drivers, where a 1-unit increase in southeastern urban expansion intensity reduces IRSEI by 0.38, with landscape fragmentation explaining 68% of ecological degradation. This research provides a quantitative basis for balancing territorial spatial development and ecological conservation in the Zhengzhou Metropolitan Area, and the IRSEI-GTWR framework offers a novel methodology for sustainable urban agglomeration assessments.

With the continuous expansion of highway networks in recent years, the monitoring and repair of road diseases have become one of the important tasks in traffic management. Traditional manual inspection methods are inefficient and prone to missing disease areas. Therefore, a pavement disease detection system based on UAV vision and deep learning algorithms has emerged. The formation mechanism of pavement diseases was first analyzed, and the UAV pavement image acquisition equipment was introduced. Then, a pavement disease identification algorithm using YOLOv8 was constructed. Finally, Python programming was employed to complete the construction of a regional identification model for highway pavement diseases based on CNN, data loading, and model training and testing. The experimental results show that the system exhibits high accuracy and real-time performance in image, video, and real-time camera identification, effectively assisting in the monitoring of highway diseases. This system can significantly improve the automation level of pavement disease detection, reduce manual workload, enhance road management efficiency, and provide strong support for the safety and maintenance of highways.

Coastal cities are among the fastest urbanizing and industrializing regions, but they also face critical issues, including environmental degradation, uncontrolled urban expansion, and an intense increase in carbon discharges. Hence, it was necessary to conduct a rigorous assessment of changes in landscape coverage and carbon storage. This research focused on Lianyungang and proposed an integrated method, CMCPI, which coupled multi-feature for land classification, PLUS, and InVEST. The purpose was to detect alterations in land utilization patterns and evaluate carbon discharges. Utilizing remote sensing data from 2010 to 2020, a detailed land use classification was performed for the research area, and its spatiotemporal change characteristics were analyzed. Subsequently, simulations predicted land use patterns for 2030 across four distinct scenarios: Nature-Focused Development (ND), Urban Rapid Development (UD), Cropland Protection (CP), and Ecological Sustainable Protection (EP). Additionally, the variations in regional carbon storage across different scenarios were assessed. The consequences indicated that the CMCPI achieved high accuracy and reliability in land classification. From 2010 to 2020, impervious surface areas in Lianyungang significantly increased to 1276.62 km2, while cropland, forestland, and grassland areas decreased. Carbon storage initially declined and then increased. Predictions for future scenarios indicated that only the EP scenario would promote an increase in carbon storage, reaching 9200.25 mmt, whereas the other three scenarios would result in varying degrees of carbon storage reduction. This research not only provides new insights into understanding the ecosystem benefits service values and carbon storage capacities of coastal areas but also offers scientific support for policy-making towards achieving “carbon neutrality” and provides references for sustainable development planning for Lianyungang.