Background: Accelerating global urbanization and increasing transportation demands highlight the critical need for precise and efficient road system planning in geospatial applications. Existing methods for extracting roads from high-resolution remote sensing images face challenges in balancing accuracy and computational efficiency, especially in complex urban environments. To address this, we introduce a data-model co-optimization framework that enhances both accuracy and efficiency. Methods: Our research focuses on three key aspects: 1) Multi-dimensional data quality optimization through a fourth-order image processing chain (“Gaussian noise reduction - non-local mean denoising - CLAHE contrast enhancement - edge enhancement”) combined with mask double median filtering and morphological closure operations to improve input data quality. 2) Development of a Lightweight Dynamic Attention Network Architecture (MACL-U-ResNet) based on ResNet50, integrating four attention modules (SE/CBAM/ECA/CA) to dynamically select channel-space-position perceptions via hyperparameter $\varphi $ . This architecture includes a depth-feature retention structure and cross-layer fusion decoder. 3) Proposal of an efficiency-accuracy balance coefficient $\eta $ ( $\eta = $ IoU/parameter count) to evaluate resource-constrained scenarios, revealing the gains of data preprocessing on system efficiency. Results: Experiments on the DeepGlobe dataset show that our system achieves 66.82% IoU and 79.32% F1-Score, surpassing the state-of-the-art LCMorph model by 0.52% in IoU while reducing the parameter count by 39.9%. The $\eta $ coefficient value of 2.33 validates the system’s balanced accuracy-efficiency, offering a significant advantage for edge computing applications. This study demonstrates a robust solution for road extraction, advancing the field towards more efficient and accurate geospatial applications.

Background: This study addresses the challenges posed by the growing number of self-guided tourists and proposes an optimized tourist bus route planning model to enhance visitor satisfaction and support sustainable tourism. Methods: Using machine learning algorithms—adaptive boosting (AdaBoost), support vector machine (SVM), naive Bayes, and K-Nearest Neighbor (KNN)—we analyze sentiment in tourist reviews, with SVM showing the best performance. A multi-criteria evaluation model combining analytic hierarchy process (AHP) and the entropy weight method (EWM) identifies key satisfaction factors, which are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model and the rank-sum ratio (RSR) method to recommend attractions. Results: The optimized route is determined using a modified Genetic-Greedy Algorithm (GGA), which improves convergence speed by 94.489% compared to traditional genetic algorithms. Applied to a case study in Tibet, the model achieved a 94.6% satisfaction rate, demonstrating its effectiveness and adaptability for diverse tourism planning contexts.