Articles

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

There have been various concerns about the environmental impact of construction works. This generates a need to take a more proactive approach in evaluating the environmental impacts of construction operations and further explore ways to reduce the environmental impacts. Enormous opportunities exist within the building industry to achieve a reduction in greenhouse gas emissions. The aim of the study is to develop a framework for the evaluation of improvement opportunities for environmental impact for onsite and offsite building construction works using life cycle assessment (LCA) and value-stream-mapping concepts. Various tools for LCA exist; however, there is a need for the development of an LCA framework and improvement opportunities that can be localized to various communities to evaluate improvement opportunities for building construction. This study conducts a review of methods to evaluate the LCA of buildings on local construction sites. A procedure for establishing improvement opportunities is also developed. Based on the author’s knowledge and experience, including site visits, using value stream mapping (VSM) techniques, a conceptual framework of the present state map and future state map of residential construction works was developed. The study presents a procedure for the evaluation of improvement opportunities for the environmental impacts of construction operations.

Life cycle assessment, LCA is one of the tools that is used to measure the environmental impacts of a process or an operation. Various studies have mentioned the benefits of mass timber in building construction. This study presents an evaluation of the LCA of certain mass timber in relation to concrete-based materials. Using Athena impact estimator for buildings, the study compared the results of an LCA study for a house that is designed with concrete beams, concrete columns, and concrete walls with brick in the envelope category (Material group 1) with those that are made with glulam beams, glulam columns, CLT walls with spruce wood bevel siding (Material group 2), and another building with LVL columns, LVL beams, CLT walls with spruce wood bevel siding (Material group 3). The results are in line with those that were reported by the majority of previous researchers. For the location that is being reviewed (Calgary, Alberta), the designs showed that construction with wood materials having mass timber components will have a better environmental performance than that for a building design with more concrete-based materials. The building design with more concrete-based material (group 1) showed 242% and 60% higher global warming and acidification potential respectively than the building with glulam beams and columns (material group 2). Except for ozone depletion potential, material group 2 (with glulam beams and columns) has a lower impact than material group 3 (with LVL/PSL beams and columns). The differences in impacts are more pronounced when the comparison is with design with more concrete-based products. This report further shows that LCA can be helpful during the preliminary design to evaluate the expected environmental impacts of the choice of different materials. This study recommends that material manufacturers and building contractors pay attention to LCA results to evaluate areas for continuous improvement.

The economic and social effects that the COVID-19 pandemic and the associated measures to address it are having in Latin America may lead to serious long-term consequences that would affect the achievement of the Sustainable Development Goals. In this article, the collaboration of environmental economists from eight countries in the region discusses the possible effects of the pandemic on air pollution, deforestation, and other relevant environmental aspects related to the SDGs. In addition to presenting an account of some of the initial effects of the health crisis on the environment, the paper discusses potential effects in terms of environmental regulations and public policy interventions. Finally, the paper presents an agenda on new research topics that arise due to the pandemic or have gained greater importance due to it, including the impacts on the achievement of the Sustainable Development Goals. Briefly, this paper is a novel view of the sustainable development in Latin America and the Covid-19 impacts on this process.