Change Detection for Geodatabase Updating

TL;DR

This paper reviews state-of-the-art change detection methods in remote sensing and geomatics, focusing on updating geodatabases efficiently by identifying changes in 2D and 3D spatial data.

Contribution

It provides a comprehensive overview of current change detection techniques for geospatial data, highlighting their applications in geodatabase updating.

Findings

Change detection methods vary for 2D and 3D data.

Automated change detection improves geodatabase update efficiency.

Review identifies challenges and future directions in the field.

Abstract

The geodatabase (vectorized data) nowadays becomes a rather standard digital city infrastructure; however, updating geodatabase efficiently and economically remains a fundamental and practical issue in the geospatial industry. The cost of building a geodatabase is extremely high and labor intensive, and very often the maps we use have several months and even years of latency. One solution is to develop more automated methods for (vectorized) geospatial data generation, which has been proven a difficult task in the past decades. An alternative solution is to first detect the differences between the new data and the existing geospatial data, and then only update the area identified as changes. The second approach is becoming more favored due to its high practicality and flexibility. A highly relevant technique is change detection. This article aims to provide an overview the state-of-the-art change detection methods in the field of Remote Sensing and Geomatics to support the task of updating geodatabases. Data used for change detection are highly disparate, we therefore structure our review intuitively based on the dimension of the data, being 1) change detection with 2D data; 2) change detection with 3D data. Conclusions will be drawn based on the reviewed efforts in the field, and we will share our outlooks of the topic of updating geodatabases.