Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas.

Scheduling plays a fundamental role in construction projects’ success and thus has drawn attention from both academic researchers and industry practitioners. A large number of research articles tend to solve emerging challenges in construction project schedule (CPS). Therefore, there is a strong need of systematic review on existing studies. In this study, a total of 332 articles were retrieved from Scopus database using title, abstract and keywords with respect to CPS and filtered by document type, language type and abstract content. In particular, science mapping approach was adopted to analyse selected journal articles. These articles were examined using three sequential processes, including bibliometric search, scientometric analysis, and in-depth qualitative discussion. It could demonstrate the most influential journals, researchers, published articles, and active countries/regions in this area. In addition, major CPS knowledge areas were identified and summarized as CPS constructability, applications of variety of CPS methods, CPS optimization models and algorithms, identification and quantification of schedule risks and uncertainties, CPS performance management, and adopting new emerging CPS technologies and methods. Furthermore, knowledge gaps and future potential research directions were also discussed in detail. Finally, a comprehensive CPS framework was proposed as a sound reference in future research.

In almost all modern cities, various utility pipelines and cables often form a maze of underground utility network. Mismanagement of underground utility incidents could cause serious consequences including unavailability of services, property damages, and personnel injuries and fatalities. Since one major reason for mismanagement is unsuitable urban utilities planning, it is a challenging task to plan urban utilities in an efficient way. This paper proposes an underground utility occupation index (UUOI), which serves as an indicator of urban utilities development. With the statistical information of existing utilities in the predefined polygons, UUOI provides planning information, including underground space already occupied, and space available for future utilization by analyzing from multiple perspectives: by utility, by zone, by road category (footway and carriageway), and by city district. UUOI supports utility companies and governmental authorities in their decision-making at both the microlevel and macrolevel. At the microlevel, UUOI provides the necessary utility and space information for utility companies to share information in making long-term and short-term development plans to partially avoid conflicts between utilities belonging to different companies and adopt suitable construction methods to minimize damages to existing utilities. At the macrolevel, the UUOI can be used to extrapolate from a small scale to a large scale to facilitate government authorities in urban planning and development. The consolidated UUOI enables the differentiation and prioritization of areas of different levels of utility density and facilitates government authorities in formulating guidelines for future urban development, establishing strategic development areas and issuing permits for underground works. The improved practices of both government authorities and utility companies will also reduce disturbance to the general public. An illustrative example has demonstrated the applicability and usefulness of UUOI.