The rapid development and increasing availability of high-resolution satellite (HRS) images provides increased opportunities to monitor large scale land cover. However, inefficiency and excessive independence on expert knowledge limits the usage of HRS images on a large scale. As a knowledge organization and representation method, ontology can assist in improving the efficiency of automatic or semi-automatic land cover information extraction, especially for HRS images. This paper presents an ontology-based framework that was used to model the land cover extraction knowledge and interpret HRS remote sensing images at the regional level. The land cover ontology structure is explicitly defined, accounting for the spectral, textural, and shape features, and allowing for the automatic interpretation of the extracted results. With the help of regional prototypes for land cover class stored in Web Ontology Language (OWL) file, automated land cover extraction of the study area is then attempted. Experiments are conducted using ZY-3 (Ziyuan-3) imagery, which were acquired for the Jiangxia District, Wuhan, China, in the summers of 2012 and 2013.The experimental method provided good land cover extraction results as the overall accuracy reached 65.07%. Especially for bare surfaces, highways, ponds, and lakes, whose producer and user accuracies were both higher than 75%. The results highlight the capability of the ontology-based method to automatically extract land cover using ZY-3 HRS images.

Four natural lakes in the middle and lower reaches of the Yangtze River—Dongting Lake, Poyang Lake, Chaohu Lake and Taihu Lake—play a key role in the climate, environment, and ecology of this area. Upstream of these lakes, the Three Gorges Dam Project has been storing water for 12 years. Future monitoring and management of rivers and lakes can certainly benefit from research on the patterns of variation of natural lakes downstream of the Three Gorges Project. This research applies Landsat TM/ETM data to evaluate water area changes in the four lakes from 2002 to 2013. The water area is estimated using AWEI (Automated Water Extraction Index) from satellite images. The average areas decreased respectively 452, 11, and 5 km² (29.6%, 1.4% and 0.2%) from 2002 to 2013 for Dongting, Chaohu, and Taihu Lakes. Meanwhile, it increased 300 km² (11.0%) for Poyang Lake. Precipitation and changes in river inflow may account for the fluctuation in the surface area to a large degree, especially between 2009 and 2013. The present study was undertaken to characterize the evolution of lakes and to explore the potential driving force of variation in order to assist the management of dams upstream in the river basin.

Georeferencing by place names (known as toponyms) is the most common way of associating textual information with geographic locations. While computers use numeric coordinates (such as longitude-latitude pairs) to represent places, people generally refer to places via their toponyms. Query by toponym is an effective way to find information about a geographic area. However, segmenting and parsing textual addresses to extract local toponyms is a difficult task in the geocoding field, especially in China. In this paper, a local spatial context-based framework is proposed to extract local toponyms and segment Chinese textual addresses. We collect urban points of interest (POIs) as an input data source; in this dataset, the textual address and geospatial position coordinates correspond at a one-to-one basis and can be easily used to explore the spatial distribution of local toponyms. The proposed framework involves two steps: address element identification and local toponym extraction. The first step identifies as many address element candidates as possible from a continuous string of textual addresses for each urban POI. The second step focuses on merging neighboring candidate pairs into local toponyms. A series of experiments are conducted to determine the thresholds for local toponym extraction based on precision-recall curves. Finally, we evaluate our framework by comparing its performance with three well-known Chinese word segmentation models. The comparative experimental results demonstrate that our framework achieves a better performance than do other models.

The need for integrating geospatial information (GI) data from various heterogeneous sources has seen increased importance for geographic information system (GIS) interoperability. Using domain ontologies to clarify and integrate the semantics of data is considered as a crucial step for successful semantic integration in the GI domain. Nevertheless, mechanisms are still needed to facilitate semantic mapping between GI ontologies described in different natural languages. This research establishes a formal ontology model for cross-lingual geospatial information ontology mapping. By first extracting semantic primitives from a free-text definition of categories in two GI classification standards with different natural languages, an ontology-driven approach is used, and a formal ontology model is established to formally represent these semantic primitives into semantic statements, in which the spatial-related properties and relations are considered as crucial statements for the representation and identification of the semantics of the GI categories. Then, an algorithm is proposed to compare these semantic statements in a cross-lingual environment. We further design a similarity calculation algorithm based on the proposed formal ontology model to distance the semantic similarities and identify the mapping relationships between categories. In particular, we work with two GI classification standards for Chinese and American topographic maps. The experimental results demonstrate the feasibility and reliability of the proposed model for cross-lingual geospatial information ontology mapping.

Building community resilience is essential for ensuring that communities can not only survive but also thrive in the face of various challenges and uncertainties. However, existing research has deficiencies in the comprehensive evaluation framework and systematic analysis of different types of urban communities within high-density Chinese cities. This study constructed a comprehensive urban community resilience assessment system (UCRA) that covers four dimensions: environmental, service, social, and governance resilience. In a case study of the Chinese megacity of Tianjin, urban communities were categorized into three physical types and three regional categories. The UCRA contained 40 detailed indicators, and the weighting of indicators was was determined using a mixed approach combining the AHP and entropy methods. The findings revealed that tower apartments in urban Chinese communities demonstrated relatively high resilience, whereas older residential complexes exhibited the lowest resilience performance. Furthermore, central urban communities generally displayed high resilience, in contrast to peripheral urban areas, where low levels of resilience were often discovered. Building upon these findings, this study discusses the characteristics and challenges associated with the resilience of various community types. By establishing a theoretical basis for creating intelligent assessment and monitoring systems, we advocate for targeted community development strategies, thereby promoting smart transformation of community resilience.

Research on coastal recreational activities has grown substantially, yet studies focusing on user perceptions of these spaces—critical for optimizing tourism experiences and management—remain fragmented and underdeveloped. This study addresses this gap by examining tourist sentiment in Xiamen, a renowned coastal city in China, using social media data. Text mining tools were utilized to process the Weibo contents through text segmentation, frequency analysis and cluster analysis. The Two-way Neural Network Fusion Model Based on the BERT (TNNFMB) deep learning approach was employed using transfer learning for sentiment analysis, while the Latent Dirichlet Allocation (LDA) model was used to uncover latent thematic patterns. Sentiment polarity analysis revealed that positive comments constituted 56.47%, negative comments only 16.3%, and neutral comments 27.2%, confirming a generally positive perception of visitors’ coastal experiences. Tourists’ social media posts primarily revolve around five core themes in coastal areas: coastal waters, waterfronts, adjacent environments, culture and creativity, and reputation and expectation. The spatial and temporal changes in sentiment scores were discovered. Areas emphasizing sea–land landscapes, cultural theme reinforcement, and open public activities generally achieved high and stable sentiment scores. Natural and natural–artificial mixed coastlines experienced significant seasonal variations in sentiment. The recommendations of this study, generated from a sentiment perspective, include shaping a harmonious coastal environment by improving coastal management and support services to enhance the comfort of the tourist experience. This study advances understanding of user-centric coastal tourism dynamics, providing evidence-based tools for managers to enhance tourist experiences and spatial quality.