Lili Wu

Landslide susceptibility mapping (LSM) based on ensemble learning is commonly evaluated using deterministic metrics, whereas prediction reliability and resampling-based prediction uncertainty remain insufficiently explored. This study assessed whether a heterogeneous Stacking ensemble model was associated with higher predictive performance and greater spatial stability of susceptibility predictions in Bayi District, Tibet. Six machine-learning models and a two-layer Stacking model were trained using 12 conditioning factors and evaluated through 100 bootstrap iterations with out-of-bag (OOB) validation and leave-one-township-out cross-validation (LOTO-CV). The Stacking model yielded the highest observed predictive performance (OOB AUC = 0.978; LOTO-CV AUC = 0.9216) and was associated with lower uncertainty, with 70.85% of the study area classified as low-uncertainty (SD < 0.05). Among the 370 confirmed landslides, approximately 60% were located in areas characterized by high susceptibility and low uncertainty. SHAP analysis consistently identified distance to roads, slope, and elevation as dominant conditioning factors across models. The results suggest that predictive accuracy and stability should be jointly considered in ensemble-based LSM, because higher predictive performance was not always associated with lower prediction uncertainty.

The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attraction perimeters, and existing approaches are often inadequate to meet the evolving needs of contemporary tourism development. To address this gap, we proposed a method for inferring vitality around attractions based on a knowledge graph. Our approach began by analyzing the functional and morphological characteristics of the areas surrounding the attractions, followed by the design of a vitality calculation model for these regions. Next, we developed a knowledge graph structure tailored for vitality reasoning around the attractions and established reasoning rules based on this graph. Finally, we conducted experiments to apply the vitality inference method to the main urban area of Kaifeng City as a case study. The results indicated that our method could effectively reason about vitality around the attractions. Notably, the vitality levels around the attractions in Kaifeng’s main urban area exhibited clear spatial differentiation. Attractions such as the Yang Family’s Tianbo Mansion, the Millennium City Park, and Lord Bao’s Memorial Temple showed higher vitality values, largely due to their advantageous functional integration and synergistic morphological characteristics.

Map symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic workflows. Icon design often relies on manual drawing, which is not only time-consuming but also heavily dependent on specialized skills. Automating the extraction of symbols from existing maps could greatly enhance the map symbol database, offering a valuable resource to support both symbol design and map production. Nevertheless, the intricate shapes and dense distribution of symbols in tourist maps complicate the accurate and efficient detection and extraction using existing methods. Previous studies have shown that You Only Look Once (YOLO) series models demonstrate strong performance in object detection, offering high accuracy and speed. However, these models are less effective in fine-grained boundary segmentation. To address this limitation, this article proposes integrating YOLO models with the Segment Anything Model (SAM) to tackle the challenges of combining efficient detection with precise segmentation. This article developed a dataset consisting of both paper-based and digital tourist maps, with annotations for five main categories of symbols: human landscapes, natural sceneries, humans, animals, and cultural elements. The performance of various YOLO model variants was systematically evaluated using this dataset. Additionally, a user interaction mechanism was incorporated to review and refine detection results, which were subsequently used as prompts for the SAM to perform precise symbol segmentation. The results indicate that the YOLOv8x model achieved excellent performance on the tourist map dataset, with an average detection accuracy of 94.4% across the five symbol categories, fully meeting the requirements for symbol detection tasks. The inclusion of a user interaction mechanism enhanced the reliability and flexibility of detection outcomes, while the integration of the SAM significantly improved the precision of symbol boundary extraction. In conclusion, the integration of YOLOv8x and SAM provides a robust and effective solution for automating the extraction of map symbols. This approach not only reduces the manual workload involved in dataset annotation, but also offers valuable theoretical and practical insights for enhancing cartographic efficiency.

In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, we propose a method to symbolize regional elements by combining textual analysis and image processing. Firstly, local chronicles are used as the textual information source, and regional elements are extracted through textual data mining. Second, the real image data of the elements are selected, and the image segmentation algorithm, clustering algorithm, etc., are used to extract contours and colors from the images and carry out corresponding symbol simplification and color matching, to create highly recognizable symbols. Finally, we apply the symbols to two map types: the thematic map and the tourist map, and design a questionnaire to evaluate the outcomes of the symbol design. After a thorough review, it has been found that the method is superior to related symbolization studies in terms of data source authority, symbol generation efficiency, and symbol information carrying. In conclusion, guided by interdisciplinary thinking, this study effectively combines theoretical analysis and design practice, proposes a new idea of symbolization, and opens up a new way for geographic information visualization.

Climate change, population growth, and socio-economic transformations present multifaceted challenges to the water resource systems in the four major river basins of Henan Province. Consequently, to gain a comprehensive understanding of water security within the study area, a quantitative assessment of water resource vulnerability (WRV) is imperative. In this investigation, the vulnerability scoping diagram (VSD) model serves as the analytical framework, subdividing vulnerability into three critical subsystems: exposure, sensitivity, and adaptive capacity. Utilizing a comprehensive evaluation index system, this study assesses WRV in Henan Province’s four primary watersheds. Temporal variations and spatial patterns of WRV from 2000 to 2020 are systematically examined through the standard deviational ellipse (SDE) and GeoDetector methods. The findings indicate that vulnerability within these river basins is shaped by the interactions among exposure, sensitivity, and adaptive capacity. Moreover, exposure and sensitivity are found to be more influential than adaptive capacity. Although there are indications of future improvements in the system’s resilience against water resource vulnerability, the overarching scenario remains precarious, particularly within the Hai and Yellow River basins. Considering the diverse characteristics of the watershed, it is recommended that adaptive management strategies be implemented. This study enhances the understanding of WRV dynamics in Henan Province, thereby aiding more effective decision making in sustainable water resource management.

The study of the response of vegetation phenology in the Qinghai Tibet Plateau to various climatic variables is paramount to unveiling the reaction of alpine ecosystems to worldwide climate alterations. Nonetheless, the lagged and cumulative effects of various climatic variables on vegetation phenology in the Qinghai Tibet Plateau remain unclear. Therefore, based on MODIS NDVI data, we extracted vegetation phenological parameters from 2001 to 2020, including the start of the vegetation growing season (SOS) and the end of the vegetation growing season (EOS), and then analyzed the response mechanisms of vegetation phenology to pre-seasonal air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) in the Qinghai Tibet Plateau on the basis of an investigation of the lag and cumulative effects. The results showed that: (1) the multiyear mean values of the SOS mainly occurred from 120 to 160 days, accounting for 86.17% of the study area, while the multiyear mean values of the EOS were mainly concentrated between 260 and 280 days, accounting for 77.05% of the study area; (2) air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) had different degrees of lagging effects on the SOS and the EOS. Among them, the time lag effect of precipitation on vegetation phenology was more pronounced; (3) different climatic variables had distinct cumulative effects on vegetation phenology. In contrast to the insignificant cumulative effects of temperature and nighttime surface temperature on the SOS and the EOS, the cumulative effects of precipitation and daytime land surface temperature on the SOS were more pronounced than those on the EOS; (4) the SOS and air temperature, precipitation, and NLST were mainly negatively correlated, in which the proportion of the negative correlation between SOS and NLST was up to 68.80%, and SOS and DLST were mainly positively correlated with a positive correlation proportion of 73.27%, EOS and air temperature, precipitation, and NLST were positively correlated with a positive correlation proportion of EOS and precipitation of up to 71.52%, and EOS and DLST were mainly negatively correlated with a negative correlation ratio of 55.87%.

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

Based on the digital footprint data, exploring the differences in tourist market structure and driving factors before and after COVID-19 is important for identifying tourist market demand and optimizing tourism product supply in the post-pandemic era. Most of the existing studies have explored the impact of the pandemic on the tourist market in well-known or large cities and have provided suggestions for tourism recovery. However, these suggestions are not entirely applicable to smaller cities. Small cities have a single level of tourism product, high homogeneity of tourism resources, small tourist market scale, and high volatility of the tourism industry. Therefore, it is necessary to study the differences in the tourist market structure of small cities and its driving factors before and after the pandemic and to propose targeted measures for the tourism recovery in the post-pandemic period. This paper, taking small cities as the study area and using online travel diaries as the data source, analyzed the differences in the spatial and temporal structures of tourist markets and their driving factors in Dengfeng and Kaifeng, China, before and after the pandemic. Then, countermeasures for tourism industry recovery in the post-pandemic era were proposed. The results were as follows: the difference in the tourism off-peak season increased after the pandemic, and the concentration of tourist market spatial distribution in Dengfeng showed a decreasing trend while that in Kaifeng showed an increasing trend. In addition to region traffic, the driving effects of leisure time, climate comfort and residents’ income level weakened after the outbreak. Dengfeng and Kaifeng can enhance the tourist market tendency and attractiveness by creating special indoor tourism projects, strengthening tourism product promotion and marketing and enhancing the facilities related to self-driving tours.

Local chronicles are a kind of historical record in China that are written in detail and play an important role in the transmission of local history and culture. Due to the single-text-carrier form of local chronicles, people have limited access to information on urban characteristics and culture; therefore, based on the cultural gene theory and Hofstede model, also known as the cultural onion model, this paper develops a “Spirit–Sign” content framework with the themes of urban characteristics and culture. Based on this framework, we map the urban characteristics and culture (visualization strategy and map design) of local chronicles. Taking the historic city of Dengfeng in the Central Plains as an example, the spatial information of the four historical city characteristics of Dengfeng was mined for the map design using the content framework of the city characteristics proposed in this study. The results of the study found that (1) there is a certain overlap in the spatial distribution of the four characteristic cultures of Dengfeng, indicating that the spiritual (traditional customs and famous people and events) and material (famous buildings and products) are complementary and mutually reinforcing to a certain extent; and (2) with the iterative development of Chinese dynasties, the material characteristic cultures of Dengfeng show strong temporal and spatial differences, which laterally reflect the changes in human activities and urban changes of each dynasty and also reflect the very important historical position occupied by Dengfeng in China. Compared with the traditional text-carrier form of local chronicles, the content construction and map visualization of the city’s historical and cultural information proposed in this study can effectively explore more potential cultural characteristics, as well as spatial and temporal connections of Dengfeng and thus help people better understand the historical characteristics of the city.

With the development of medium- and high-resolution satellites, successfully segmenting differently sized geo-objects remains a challenging issue for geographic object-based image analysis (GEOBIA). The hybrid image segmentation method is a good alternative to produce good segmentation that best matched the different sizes of geo-objects. However, the existing methods almost use segmentation parameters (SPs), such as scale, to control the sizes and shapes of segments. This will lead to two issues: (1) one single scale is impossible to segment every geo-object well due to the land cover complexity within remote-sensing imageries; (2) over- and under-segmented regions still occur in the segmentation results, whatever using any advanced segmentation methods. To solve the above problems, this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing. First, the primitive segmentation was produced and then stratified into layers with different land covers. Then, the local scale was calculated for a more objective merging process in the separating layers. Third, the over- and under-segmentation at separating layers was recognized and re-processed for achieving a fine segmentation. To validate the proposed method, it was applied to three test images of gaofen-1 satellite with different land cover types, and ten competing methods were compared. The visual and quantitative results indicated the advantage of our method in segmenting out different sizes of geo-objects, which can effectively reduce the over- and under-segmentation error.