Regional population forecast and analysis is of essence to urban and regional planning, and a well-designed plan can effectively construct a sound national infrastructure and stabilize positive population growth. Traditionally, either urban or regional planning relies on the opinions of demographers in terms of how the population of a city or a region will grow. Multi-regional population forecast is currently possible, carried out mainly on the basis of the Interregional Cohort-Component model. While this model has its unique advantages, several demographic rates are determined based on the decisions made by primary planners. Hence, the only drawback for cohort-component type population forecasting is allowing the analyst to specify the demographic rates of the future, and it goes without saying that this tends to introduce a biased result in forecasting accuracy. To effectively avoid this problem, this work proposes a machine learning-based method to forecast multi-regional population growth objectively. Thus, this work, drawing upon the newly developed machine learning technology, attempts to analyze and forecast the population growth of major cities in Taiwan. By effectively using the advantage of the XGBoost algorithm, the evaluation of feature importance and the forecast of multi-regional population growth between the present and the near future can be observed objectively, and it can further provide an objective reference to the urban planning of regional population.

Remote sensing images are the product of information obtained by various sensors, and the higher the resolution of the image, the more information it contains. Therefore, improving the resolution of the remote sensing image is conducive to identify Earth resources from the remote sensing image. In this article, we present a multiple-branch panchromatic image resolution restoration network based on the convolutional neural network to improve the spatial and spectral resolution of the panchromatic image simultaneously, named MBPRR-Net. Specifically, we adopt a multibranch structure to extract abundant features and utilize a feature channel mixing block to enhance the interaction of adjacent channels between features. Feature aggregation in our method is used to learn more effective features from each branch, and then a cubic filter is utilized to enhance the aggregated features. After feature extraction, we use a recovery architecture to generate the final image. Moreover, we utilize image super-resolution to restore spatial resolution and image colorization to restore the spectral resolution so that we can compare it with some image colorization and super-resolution methods to verify the proposed method. Experiments show that the performance of our method is outstanding in terms of visual effects and objective evaluation metrics compared with some existing excellent image super-resolution and colorization methods.