Pine wilt disease (PWD) is a severe forest disease caused by the invasion of pine wood nematode (Bursaphelenchus xylophilus), which has caused significant damage to China’s forestry resources due to its short disease cycle and strong infectious ability. Benefiting from the development of unmanned aerial vehicle (UAV)-based remote sensing technology, the use of UAV images for the detection of PWD-infected trees has become one of the mainstream methods. However, current UAV-based detection studies mostly focus on multispectral and hyperspectral images, and few studies have focused on using red–green–blue (RGB) images for detection. This study used UAV-based RGB images to extract feature information using different color space models and then utilized semantic segmentation techniques in deep learning to detect individual PWD-infected trees. The results showed that: (1) The U-Net model realized the optimal image segmentation and achieved the highest classification accuracy with F1-score, recall, and Intersection over Union (IoU) of 0.9586, 0.9553, and 0.9221, followed by the DeepLabv3+ model and the feature pyramid networks (FPN) model. (2) The RGBHSV feature scheme outperformed both the RGB feature scheme and the hue saturation value (HSV) feature scheme, which were unrelated to the choice of the semantic segmentation techniques. (3) The semantic segmentation techniques in deep-learning models achieved superior model performance compared with traditional machine-learning methods, with the U-Net model obtaining 4.81% higher classification accuracy compared with the random forest model. (4) Compared to traditional semantic segmentation models, the newly proposed segment anything model (SAM) performed poorly in identifying pine wood nematode disease. Its success rate is 0.1533 lower than that of the U-Net model when using the RGB feature scheme and 0.2373 lower when using the HSV feature scheme. The results showed that the U-Net model using the RGBHSV feature scheme performed best in detecting individual PWD-infected trees, indicating that the proposed method using semantic segmentation technique and UAV-based RGB images to detect individual PWD-infected trees is feasible. The proposed method not only provides a cost-effective solution for timely monitoring forest health but also provides a precise means to conduct remote sensing image classification tasks.

Urban residents’ transportation modes play a pivotal role in shaping transportation planning and policies for sustainable cities. Mining refined transportation modes from mobile phone location (MPL) data is a key spatiotemporal big data application for sustainable city planning and traffic management. However, key challenges persist: low recognition accuracy due to insufficient consideration of travel features of transportation modes, the positioning uncertainty of MPL data, and ineffective evaluation due to lacking validation datasets. To address these limitations, we propose an analytical framework for transportation mode recognition. First, precise moving segments are constructed through road network matching and linear interpolation, resolving the positioning uncertainty issues of MPL data. Then, we propose a comprehensive feature parameter system for transportation mode recognition and construct a transportation mode recognition model based on eXtreme Gradient Boosting (XGBoost). Finally, using synchronously collected GPS data and travel logs, we validated the framework’s recognition results, demonstrating its ability to improve the accuracy of transportation mode recognition.

Existing concepts and frameworks of soundscapes focus on the analysis and description of the sound source but do not explore geographical environment parameters and receiver characteristics in the geographical scene. Existing soundscape data models ignore the geographical environment and receiver information, which limits the comprehensive understanding and expression of soundscapes. They cannot study the relationship between the elements related to the sound source or explore the interaction mechanism between the sound and geographical environments. From the geographical perspective, this study extends soundscape to geographical soundscape (geo-soundscape), defines geo-soundscape by the cognition of the geographical scene, analyzes and expresses the conceptual framework of soundscapes through a content hierarchy structure, and expands the characteristics of the receiver, geographical environment parameters, further-obtained geographical scene elements, and scene element description dimensions. Based on the MPEG-7 data model, this study develops a geographical-MPEG-7 data model which consists of low-, medium-, and high-level feature classes. Taking as an example soundscape data collected on a university road in Nanjing, Jiangsu Province, in a real geographical environment, the concept, framework, and data model architecture of the geo-soundscape proposed in this study are demonstrated and described to validate the completeness and feasibility of the proposed model. The results show that our basic framework for a geo-soundscape is well adapted to the Geo-MPEG-7 data model. The model can store, organize, and describe all the soundscape information containing all elements and inter-element relationships. The soundscape in the real environment is fully expressed and described. This study provides a new research direction for soundscapes from a geographical perspective and provides guidance for urban planning and landscape design.