The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio.

Amid escalating urban environmental challenges, digital evaluation of street environments becomes vital. Deep Learning (DL) and related technologies have been increasingly applied to analyze streetscapes. Existing research predominantly focuses on urban street elements, neglecting comparative analyses across diverse contexts like urban, suburban, and distinctive street types. This research, conducted in Taichung City, Taiwan, employs DL and other methods for a comprehensive assessment of urban street elements, including building facades, dimensions, furniture, greening, and openness, creating a thorough streetscape quality evaluation framework. Utilizing the Delphi method and Analytic Hierarchy Process (AHP), 5 main categories and 16 factors were identified. Street greening emerged as the most critical, accounting for 33.39%, while street proportions were the least at 12%. Visible sky extent and building color harmony were prioritized. The study applied DL to assess landscape quality preferences near the National Taichung Theater, Shuren Business District, and Audit New Village, revealing the highest landscape quality score around the National Taichung Theater (82.7%), the lowest in Shuren Business District (59.6%), and a mid-level at Audit New Village (63.2%). Pearson correlation analysis confirmed a positive correlation between questionnaire and DL-evaluated scores. Multivariable linear regression showed these five categories significantly impact streetscape quality preferences, providing a scientific basis for urban development and enhancing resident satisfaction.

In our living environment, we frequently encounter natural disasters such as typhoons, floods, earthquakes, drought, petrochemical plant safety accident, stripping accident and so on. Due to global climate change, natural disasters have become even more devastating. Thus, it is important to look for ways to mitigate disasters. This study employs the concept of resilience to examine the ability of parks to recover from natural disasters. According to previous disaster history, the damage caused by natural disasters in the parks is mainly water and wind. Therefore, this study first conducted a literature review to find out relevant resilience criteria that are advantageous for combating water and wind disasters, and to recognize the degree of resilience achieved by each criterion. Then, based on the elements of the resilience criteria, the study establishes a preliminary resilience grading system. Furthermore, three different types of parks are evaluated under the grading system. This study provides a theoretical basis for resilience grading and suggestions for improving parks in the future.