Improving Data Acquisition Processes for Geospatial Building Information Applications

This study presents different technologies for processing geospatial building information in 2D models and discusses potential problems with an enormous growth in the data volume and availability of GIS software. The problems arise from collecting data from multiple data sources (e.g., mobile devices, websites, sensors, computers, GPS, or WFS) with different context problems (e.g., missing data, data formats, invalid values) and inefficient pre-processing data pipelines for examining the complex structure of spatial datasets. Thus, there is a need for a system that can manage such data automation issues in this case. One needs a data processing pipeline and data modeling for geospatial datasets. This process allows faster examination and visualization of the map to detect patterns. We present different GIS tools with various functionalities in handling geometric objects and introduce efficient data acquisition processing for these platforms. We conduct several experiments with these GIS applications to explore possibilities and program capabilities in terms of performance. The study analyzes the workflows for data collection, integration, and spatial data processing based on different formats, tools, and methods. The thesis studies and combines many techniques from GIS technologies to improve practices for software development teams and geospatial management systems. Data acquisition and integration apply these techniques to gain better optimization based on tool experiments and the user perspective. The findings provide the foundation for future work to have a standard methodology or processes for working with geospatial applications in file conversion, loading, processing, and exporting