Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors

Unsupervised domain adaptation for object detectors addresses the problem of improving the cross-domain robustness of object detection from label-rich to label-poor domains, which has been explored in many studies. However, one important issue in terms of when to apply the domain adaptation algorithm for geospatial object detectors has not been fully considered in the literature. In this paper, we tackle the problem of detecting the moment or change-point when the domain of geospatial images changes based on conformal test martingale. Beyond the simple introduction of this martingale-based process, we also propose a novel transformation approach to the original conformal test martingale to make change-point detection more efficient. The experiments are conducted with two partitions of our released large-scale remote sensing dataset and the experimental results empirically demonstrate the promising effectiveness and efficiency of our proposed algorithms for change-point detection.