# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 5 Conclusions  
This paper focuses on the problem of detecting domain shift for geospatial object detectors automatically with few false alarms. To do so, we first partition our released large-scale remote sensing dataset using clustering analysis, which attempts to build different domains for geospatial object detection. After this preparation, the original CTM and its transformation are tested for four different domain shift cases. The experimental results confirm the effectiveness and efficiency of our proposed algorithms, and the variant with adaptive momentum parameters demonstrates equivalent and in some cases superior performance to the state-of-the-art CTM-based methods for change-point detection. In addition, since CTM is a standard statistical hypothesis testing for detecting domain shift and all four domain shift cases are detected by CTM, we can reject the null hypothesis of the IID assumption and verify the partitions for different domains statistically. Our methods of constructing the domain shift datasets and detecting change-point are valuable for research on geospatial object detection in practice.  
Conceptualization, Z.L. and P.W.; methodology, D.W.; software, G.W. and D.W.; validation, G.W., S.Z. and D.W.; formal analysis, G.W. and S.Z.; investigation, Z.L.; resources, Z.L. and P.W.; data curation, S.Z.; writing--original draft preparation, G.W. and D.W.; writing--review and editing, G.W. and D.W.; visualization, G.W.; supervision, P.W. and D.W.; project administration, D.W.; funding acquisition, G.W. and D.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62106169 and Grant U2082064, and in part by the CETC Key Laboratory of Aerospace Information Applications under Grant SXX19629X060.  
Not applicable.  
Figure 4: Visualization of Mean Delay for all algorithms with conformal test martingales. Since Alg.2(\(r_{n}=0.5\)) has high POFD, CTM(\(e=10\)) performs best from Clear to Blur Domain and Alg.2(Adaptive) achieves the best performance in the other three domain shift cases.  
Data Availability Statement: The data presented in this study are openly available at [[https://github.com/[PERSON]/geospatial-object-detection](https://github.com/[PERSON]/geospatial-object-detection)]([https://github.com/[PERSON]/geospatial-object-detection](https://github.com/[PERSON]/geospatial-object-detection)), accessed on 20 December 2019.  
The authors wish to thank the anonymous editor and reviewers for their valuable comments and suggestions which improved this work.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CTM & Conformal Test Martingale \\ IID & Independent and Identically Distributed \\ LAB & Lightness, Red/Green Value, Blue/Yellow Value \\ YOLOv3 & You Only Look Once Object Detector, Version 3 \\ mAP & Mean Average Precision \\ POD & Probability of Detection \\ POFD & Probability of False Detection \\ Alg.1 & Algorithm 1 \\ Alg.2 & Algorithm 2 \\ \end{tabular}