To effectively classify tree species within datasets characterized by limited samples, we introduced a novel approach named DenseNetBL, founded upon the fusion of the DenseNet architecture and a pivotal bottleneck layer. This bottleneck layer, encompassing a compact convolutional component, played a central role in our methodology. The evaluation of DenseNetBL was conducted under varying conditions, encompassing small-sample tree species data, extensive remote sensing datasets, and state-of-the-art classifiers. Furthermore, a quantitative assessment was executed to extract tree species areas. This was achieved by quantifying pixel areas within manually delineated tree species maps and classifier-generated counterparts. The findings of our study indicated that, in scenarios devoid of pre-trained weights, DenseNetBL consistently outperformed its DenseNet counterpart with equivalent layer numbers. In the realm of small-sample situations, both the Swin Transformer and Vision Transformer exhibited inferior performance when juxtaposed with DenseNet and DenseNetBL. Remarkably, among the shallow architectures, DenseNet33BL showcased superior aptitude for small-sample tree species classification, culminating in the most commendable results (Overall Accuracy (OA) = 0.901, Kappa = 0.892). Conversely, the Vision Transformer yielded the least favorable classification outcomes (OA = 0.767, Kappa = 0.708). The amalgamation of DenseNet33BL and simple linear iterative clustering emerged as the optimal strategy for attaining robust tree species area extraction results across two prototypical forests. In contrast, DenseNet121 exhibited suboptimal performance in the same forests, attaining the least satisfactory tree species area extraction results. These comprehensive findings underscore the efficacy of our DenseNetBL approach in addressing the challenges associated with small-sample tree species classification and accurate tree species area extraction.

Accurate travel time prediction is essential for improving urban mobility, traffic management, and ride-hailing services. Traditional link- and path-based models face limitations due to data sparsity, segmentation errors, and computational inefficiencies. This study introduces an origin–destination (OD)-based travel time prediction framework leveraging high-resolution ride-hailing trajectory data. Unlike previous works, our approach systematically integrates spatiotemporal, quantified weather metrics and driver behavior clustering to enhance predictive accuracy. The proposed model employs a Back Propagation Neural Network (BPNN), which dynamically adjusts hyperparameters to improve generalization and mitigate overfitting. Empirical validation using ride-hailing data from Xi’an, China, demonstrates superior predictive performance, particularly for medium-range trips, achieving an RMSE of 202.89 s and a MAPE of 16.52%. Comprehensive ablation studies highlight the incremental benefits of incorporating spatiotemporal, weather, and behavioral features, showcasing their contributions to reducing prediction errors. While the model excels in moderate-speed scenarios, it exhibits limitations in short trips and low-speed cases due to data imbalance. Future research will enhance model robustness through data augmentation, real-time traffic integration, and scenario-specific adaptations. This study provides a scalable and adaptable travel time prediction framework, offering valuable insights for urban traffic management, dynamic route optimization, and sustainable mobility solutions within ITS.

We propose a pioneering approach for gathering data on the forest canopy, one that merges two cutting-edge technologies: ConvNext tiny and simple linear iterative clustering (SLIC) (ConvNeXt and SLIC vectorize for tree species mapping, CSVTSM). By leveraging the clustering label generated by SLIC, CSVTSM obtains the vectorized result of land cover and allows us to obtain the location and distribution range information about the individual canopy. Moreover, CSVTSM employed ConvNeXt tiny, an advanced model, to identify vector objects and produce vector tree species maps across the experimental area to obtain the area of various tree species. To evaluate the accuracy of our methodology, we compared the canopy boundary of the original image to the four experimental areas that were vectorized by CSVTSM. Our model’s efficacy was assessed by calculating the overall accuracy and Kappa coefficient on the validation set. The effectiveness of the proposed method was qualitatively evaluated by calculating the difference between the tree species area extracted by CSVTSM and manual extraction. The results indicate that, when considering the same canopy size, the vectorization approach based on the SLIC-based clustering label generates vector boundaries that are more closely aligned with the distribution of actual canopy boundaries in the experimental area. Furthermore, when used in conjunction with the SLIC vectorization approach, the ConvNeXt model can produce an even more precise vector map of tree species and more accurate tree species area information is obtained (only a 0.26 ha difference from manual extraction). These findings demonstrate that CSVTSM can be used to quantitatively acquire a wealth of information on individual tree positions, crown distribution ranges, and planting areas from high-resolution RGB photos captured by low-consumption unmanned aerial vehicle platforms. The implications of these results are wide-ranging, with local managers expressing keen interest in our findings as they offer trustworthy support for statistical application fields, such as gathering area ecological information, locating tree species resources, and disseminating spatial information about forests..

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks.

Surface water is an essential component of water resources. Obtaining spatial information on surface water quickly and accurately is of great significance for the management and utilization of water resources and the protection of the ecological environment. The research on water extraction methods using remote sensing images is a trending topic that has been extensively explored in water index, classification, subpixel analysis, and other related areas. Compared with other methods, a water-index-based method has the advantages of speed and convenience. The characteristics of surface water, such as its extensive coverage and instability, make the water index particularly effective in monitoring large areas of surface water. We propose a new Landsat-8 image water index combination (WI2023). We also utilize the ratio of the near-infrared band and short-wave infrared band to eliminate the shadow between water and non-water, thereby enhancing the contrast between water and other ground objects. According to the different terrains, the performance of WI2023 was verified in plain, hill, mountain, plateau, and basin areas and compared with four widely used water indexes. The results show that WI2023 has obvious advantages in water extraction in hilly areas compared with the other four methods (i.e., normalized differential water index, modified normalized differential water index, fringe cap humidity index, and multi-band water index). The mean overall accuracy and kappa coefficient are higher at 96.16% and 0.94, respectively. WI2023 holds the potential to be a useful surface water extraction technology for water resource studies and applications.