# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 6 Conclusions  
In this paper, we proposed a deep learning methodology for change detection in the mining sector using SAR images. The proposed method is based on a CNN architecture, which was trained on a dataset of SAR images of mining sites. SAR data for change detection have several advantages. Indeed, the images can be acquired regardless of weather or lighting conditions, providing information about the earth's surface. Additionally, they can be used to monitor changes in large areas with high resolution and accuracy, making them an attractive option for monitoring mining operations. Unfortunately, there are no public SAR change detection datasets for the case of open-pit mines. For this reason, despite the high human cost, we created a new dataset annotated by experts in the field. To evaluate the effectiveness of our approach, we conducted experiments on three different mining sites: Site A, Site B, and Site C. These sites were chosen as they represent the types of mining operations commonly found and have different characteristics, providing a comprehensive evaluation of the model's performance. The visual results of the experiments, together with the F1 Score values obtained, demonstrate that the proposed model could effectively identify changes. The use of coherence information, data augmentation techniques, and a pseudo-labeling algorithm significantly improved the model's performance. To further evaluate the model's generalization capabilities, we tested it on Site C, which was completely unseen by the model during training and validation. Our results demonstrate the potential of deep learning approaches for change detection in the mining sector using SAR data. The proposed methodology can provide reliable and accurate results, which can be used to support decision-making in the mining industry, monitor the evolution of mining operations, detect illegal mining activities, or monitor the impact of mining on the environment.  
**Author Contributions:** Conceptualization, G.M., F.R., A.R., B.L.S. and C.P.; methodology, G.M., F.R., A.R., B.L.S. and C.P.; software, G.M. and F.R.; validation, G.M. and F.R.; formal analysis, G.M. and F.R.; investigation, G.M. and F.R.; resources, G.M., F.R. and A.R.; data curation, G.M., F.R., A.R. and A.F.; writing--original draft preparation, G.M. and F.R.; writing--review and editing, G.M., F.R., A.R., B.L.S. and C.P.; visualization, G.M. and F.R.; supervision, A.R., B.L.S. and C.P.; project administration, A.R., B.L.S. and C.P.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is part of the BulletInSAR project, which is co-funded by ESA through the \(\phi\)-lab InCubed programme grant number 4000138160/22/I-DT-bgh. In this framework, the activities were executed as a cooperation between the ESA \(\phi\)-lab and TRE ALTAMIRA. The APC was funded by TRE ALTAMIRA.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** [PERSON], [PERSON], [PERSON] were employed by TRE ALTAMIRA s.r.l., [PERSON] was employed by European Space Agency (ESA) and [PERSON], [PERSON] were employed by Polytechnic University of Milan. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from TRE ALTAMIRA s.r.l. and European Space Agency (ESA). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.