The integration of BIM (Building Information Modeling) and GIS (Geographic Information System) technologies allows for added value in many fields; starting from the construction industry to administrative operations. However, the issue of integration is currently quite challenging. This is due to the lack of consistency (inter alia, a lack of standards) in the integration of both technologies. It is the result of the different primary use of BIM and GIS. The use of BIM and GIS integration has great potential, especially in the construction industry. Therefore, it was decided to analyze the strengths and weaknesses of integration as well as the opportunities and threats in the future by performing a SWOT analysis. The analysis was performed cross-sectionally based mainly on the existing literature. Finally, six strengths, five weaknesses, five opportunities, and four threats were identified and described.

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.

Monitoring the progress of construction work and adhering to the schedule is crucial for the timely completion of projects. Integrating data from various sensors (e.g., cameras, laser scanners) mounted on diverse platforms (rovers, drones, satellites) with BIM 4D (Building Information Modelling) enables effective construction control solutions. By leveraging 3D models enriched with temporal information, project management can be significantly enhanced. This paper focuses on a comprehensive review of current literature and state-of-the-art practices to design a framework for integrating satellite remote sensing data with BIM 4D, termed the Sat4BIM4D method. Proposals for this method are developed alongside algorithms for processing satellite-derived data to monitor construction progress, particularly for infrastructure projects. The study emphasizes the compatibility and synergy between satellite data and BIM 4D, providing a structured direction for future research. Advantages, limitations, and potential challenges of the proposed approach are also critically analyzed to pave the way for further development in this domain.