3D building data is vital in smart city planning, management, and energy assessment. However, constructing accurate 3D models for existing buildings remains challenging due to the labor-intensive processes and insufficient automation. This study proposes an algorithmic strategy based on multi-view UAV imagery to generate 3D wireframe models with detailed lateral geometric features, serving as a foundation for constructing 3D building models of existing structures. This approach aims to improve operational efficiency and reduce costs. The algorithm employs a pre-trained corner detection model and a novel corner extraction algorithm, utilizing a "coarse-to-fine" strategy to achieve precise corner localization. Additionally, a virtual corner reconstruction strategy is employed to mitigate the inevitable occlusion and data loss in urban UAV imagery. Experimental results demonstrate that this algorithmic strategy adapts well to buildings with curved architectural structures, achieving an average corner localization accuracy of approximately 30 cm and up to 98% completeness in wireframe reconstruction.

This study integrated greenhouse gases, environmental, and anthropogenic variables, utilizing big data and five machine learning algorithms, including Random Forest (RF), Gradient Boosting (GBR), Light Gradient Boosting Machine Regressor (LGBMR), CatBoost Regressor (CBR), and eXtreme Gradient Boosting (XGBoost), to establish models for estimating ambient temperatures based on two greenhouse gases, CO2 and CH4. The LGBMR model performed best for CO2, while the XGBR model showed better performance for CH4. The R2 values for the CO2 and CH4 estimation models were 0.993 and 0.999, respectively. Analysis of SHAP values confirmed the significant influence of greenhouse gas concentration, humidity, wind speed, and other factors on predictions. The findings of this study offer new evaluation methods for greenhouse gas emission reduction strategies and provide crucial insights for global climate change research and policy-making, highlighting the importance of interdisciplinary collaboration.

The COVID-19 epidemic has brought significant changes to human activities worldwide, including in Taiwan. On May 19, 2021, the government announced a level 3 alert to restrict public movement. This study aims to assess the impact of the lockdown policy on PM2.5 concentrations in Taiwan's industrial city, Kaohsiung. Daily PM2.5 observations and geographic data from 1994 to 2020 were collected. A land-use regression model, combined with stepwise variable selection, was used to identify important factors affecting PM2.5 variability. These predictors were used to develop machine learning models with algorithms such as Random Forest (RF), which showed the best performance with an R2 of 0.95. Paired t-tests indicated that PM2.5 levels were significantly lower during the alert (18.1 μg/m3) compared to before (19.9 μg/m3), with consistent results across residential, industrial, street, and green areas (p < 0.001).

This study explores the use of Generative Adversarial Networks (GAN) to enhance the spatial resolution of satellite images, addressing resolution degradation caused by factors such as off-nadir angles, weather conditions, and sensor limitations. The research utilizes medium, high, and very-high-resolution images, applying downsampling and training the GAN to generate super-resolution images. During GAN training, the generator reconstructs low-resolution images into super-resolution ones, while the discriminator distinguishes between generated and real images. The study also utilizes the VGG-19 pre-trained model for feature extraction to improve image quality. Experimental results show that image details become sharper as training progresses, and the GAN outperforms traditional methods in terms of structural similarity. However, issues like color shifts and artifacts emerged during image generation. To address these problems, the study recommends deeper training, post-processing techniques, and model optimizations such as removing Batch Normalization. Overall, while GAN models show potential for enhancing satellite image resolution, further improvements are needed to resolve color shifts and artifacts, focusing on model stability and post-processing.