Environmental Change Detection
for Real-World Change Analysis

Abstract

Scene Change Detection (SCD) evaluates changes using predefined query-reference (i.e., present-past) image pairs. However, this formulation overlooks a critical dependency: the corresponding query-reference pair is assumed to be prepared in advance. In real-world applications, such as mobile robots, future query views cannot be known in advance, and thus their corresponding reference images cannot be predefined. To remove this dependency and push change detection toward more practical applications, we introduce Environmental Change Detection (ECD). A key aspect of ECD is to avoid unrealistically predefined and aligned query-reference pairs and instead retrieve environmental cues from an uncurated image database of reference scenes. To tackle this new challenging task, we additionally introduce an initial solution that enables change detection under unknown and imperfect query-reference conditions. The main idea of our solution is to retrieve multiple reference candidates and aggregate semantically rich representations for change detection. We further construct ECD benchmark sets by reformulating three standard change detection datasets. Extensive experimental results demonstrate the efficacy of our solution in both ECD and SCD.

BibTeX

@inproceedings{cho2026environmental,
  title     = {Environmental Change Detection for Real-World Change Analysis},
  author    = {Cho, Kyusik and Woo, Suhan and Seong, Hongje and Kim, Euntai},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026},
  url       = {https://github.com/kyusik-cho/ECD}
}