Operational change detection for geographical information: Overview and challenges

The rapid evolution of territories due to climate change and human impact necessitates prompt and effective updates to geospatial databases maintained by National Mapping Agencies. That is why change detection is a rapidly evolving and increasingly important field with crucial applications in environmental monitoring, urban expansion, and disaster management. In this review, we present a comprehensive overview of change detection methods and propose future research directions to guide the community towards robust, interpretable, and operational systems, extending beyond the alert stage. This paper first outlines the fundamental definition of change, emphasizing its multifaceted nature, from temporal to semantic characterization. It categorizes automatic change detection methods into four main families: rule-based, statistical, machine learning, and simulation methods. The strengths, limitations, and applicability of every family are discussed in the context of various input data. Then, key applications for National Mapping Agencies are identified, particularly the optimization of geospatial database updating, change-based phenomena, and dynamics monitoring. Finally, the paper highlights the current challenges for leveraging change detection such as the variability of change definition, the missing of relevant large-scale datasets (for both training and evaluation), the diversity of input data, the unstudied no-change detection and the human in the loop integration. • Change detection can improve timeliness and reduce the cost of updating geodatabases. • Change detection methods need to be operational rather than experimental. • Change detection research should focus on developing “no-change” detection methods. • The difficulty of defining change is the main challenge in change detection. • The issues of change detection should lead to new research projects with high impact.