# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 2 Dataset  
The ability to accurately detect and monitor changes is crucial for mining operations, and high-resolution imagery plays a fundamental role in this regard. Indeed, this enhanced resolution is particularly beneficial in mining scenarios, where the ability to discern fine details and small changes is essential. Among the satellite options available for such applications, the two common choices are TerraSAR-X and Cosmo SkyMed. TerraSAR-X is the most utilized satellite in mining monitoring due to certain advantages. One notable advantage is its narrower orbital tube compared to Cosmo SkyMed. This narrower orbital tube allows TerraSAR-X to mitigate geometrical decorrelation effects in areas with steep and dynamic topography. Additionally, the revisiting time of the TerraSAR-X satellite constellation is noteworthy. With a revisiting time of 4 + 7 days, the constellation can acquire updated imagery of mining sites within a relatively short time frame. This frequent revisiting schedule enables mining companies to monitor changes in near real time, facilitating prompt decision-making and effective responses to evolving situations and developments. This study considered high-resolution SAR images acquired using the X-Band TerraSAR-X satellite. Table 1 summarizes the sensor specification and the acquisition mode employed. For the dataset's creation, three different open-pit mines were considered for labeling, see Table 2. For each site, we used multiple acquisition pairs, three for Site A, two for Site B, and four for Site C. Each pair represents two different SAR acquisitions made at two different moments over the same site. The images were co-registered, and both flat-earth and topography-phase components were compensated [34] for. The pairs were selected and divided into non-overlapping 256 \(\times\) 256 patches. To evaluate the proposed methods, we considered two sites, Site A and Site B, as training and validation sets, using a two-fold cross-validation-like approach. In other words, we first employed Site A as the training and Site B as the validation set and then, we replicated the experiments switching the two sites (i.e., Site B as the training and Site A as validation set). Note that, we randomly discarded some patches that did not contain changes to reduce the number of negative training samples. In this way, we obtained balanced training datasets with 50% of the patches containing at least one pixel of change. For each validation set, instead, we considered all the patches available, to evaluate the model on the real distribution of the data. In this way, we did not create statistical distortions during the estimation of the models' performance. Finally, we tested our model performance on the entire third site, Site C. The characteristics of the three sites are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**TeraSAR-X Specifications** \\ \hline Antenna Length & 4.8 m \\ Nominal Look Direction & Right \\ Antenna Width & 0.7 m \\ Range Bandwidth & 150 MHz/300 MHz \\ SAR imaging mode & StripMap \\ Resolution & 3 m \\ Scene size & 30 km \(\times\) 50 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TerraSAR-X specifications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Site** & **Geometry** & **Data Ranges** \\ \hline A & Ascending & 4 January–5 May 2021 \\ B & Descending & 10 August–1 September 2022 \\ C & Ascending & 28 June–16 October 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acquisition details of the open-pit mining sites used. For the labeling activities, we had to adequately define the changes we were interested in, avoid ambiguities, and guarantee a good level of consistency between labels. Change detection is an important task in various applications, including flood detection, disaster monitoring and hazard assessment, crop growth monitoring, urban planning, land cover use, and natural resource management. The definition of what constitutes a change is highly dependent on the application.