Based on the entropy TOPSIS model and clustering method, this article builds a multi-functional evaluation index system for land use based on 21 indicators from the perspective of “production-life-ecology”, evaluates the multifunctionality of land use, and analyzes its spatial differences in Baise, Guangxi, China. The results show that the multifunctional level of land use in Baise is still at a poor level, and the ecology function are better utilized. There are big differences in the spatial distribution of multifunctional land use among counties in Baise, and the overall spatial distribution pattern is “slightly stronger in the south than in the north”. Multifunctional land use in Baise is greatly affected by production function factors. It is recommended to accelerate industrial transformation and upgrading, and give full play to the different ecology, resource, location or policy advantages of each county to improve the level of land production functions, and pay attention to green development, the common development of each function and the regional coordinated development.

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization.