How can you ensure geospatial data accuracy in underground areas?

1 Define your data requirements

Before you start collecting or acquiring geospatial data for underground areas, you need to define your data requirements clearly. What is the purpose and scope of your project? What level of detail and accuracy do you need? What are the spatial and temporal scales and resolutions of your data? What are the data formats and standards that you will use? Answering these questions will help you determine the most suitable data sources, methods, and tools for your project.

2 Choose appropriate data sources

Depending on your data requirements, you may need to use different data sources for underground areas. Remote sensing, such as satellite imagery, aerial photography, radar, and lidar, can provide large-scale and high-resolution data. However, it may have limitations in terms of penetration, coverage, and accuracy. Field surveys like GPS, total stations, laser scanners, and mobile mapping systems can be more accurate and detailed, but they may be time-consuming, costly, and risky. Existing data like maps, drawings, reports, and databases can provide useful information and context; however, they may be outdated, incomplete or inconsistent. To overcome these limitations or issues with data sources for underground areas you may need to use special sensors or techniques such as synthetic aperture radar (SAR), interferometric SAR (InSAR), ground-penetrating radar (GPR), differential GPS (DGPS), real-time kinematic (RTK), or inertial navigation systems (INS). Additionally, methods such as georeferencing, digitizing or spatial analysis can help verify, update or integrate existing data with other sources.

3 Apply quality control measures

Once you have collected or acquired geospatial data for underground areas, you need to apply quality control measures to ensure data accuracy. Data validation involves checking the data for errors, anomalies, or inconsistencies, such as through visual inspection, statistical analysis, or spatial queries. You may need to then correct, delete, or flag the erroneous data using tools like editing functions, attribute tables, or error layers. Data verification requires comparing the data with other sources of information, such as ground truth, reference data, or expert knowledge. You may need to measure, calculate, or assess the accuracy of the data using metrics like root mean square error (RMSE), coefficient of determination (R2), or confidence intervals. Additionally, you should document the data sources, methods, and results in formats like metadata, reports, or logs. Following standards and guidelines such as ISO 19115, FGDC, or INSPIRE can also help ensure quality. By following these tips and techniques you can guarantee geospatial data accuracy in underground areas and enhance the quality and reliability of your GIS project.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?