## 4 Results
### Detection of Surface Deformation (DInSAR, AI)  
A number of processes can cause ground deformation at volcanoes: dyke intrusions, reservoir pressurization, caldera subsidence, cooling of eruptive deposits (lava/pyroclastic flows), landslides, etc. These can to some extent be distinguished based on distinctive patterns of ground displacement imaged in the interferograms [75]. As described previously, MOUNTS focuses on the automatic detection of processes generating large ground deformations in short time intervals (i.e., between two successive S1 acquisitions, usually 6 to 12 days apart), which typically imprint on interferograms as many colored fringes. In particular, dyke intrusions (whereby magma intrudes into a fissure and pushes the surrounding rock aside), are commonly characterized by two lobes with opposite displacements \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Volcano**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**N Interferograms**} & \multirow{2}{*}{**Time Span**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**TPR**} & \multirow{2}{*}{**TNR**} & \multicolumn{2}{c}{**Exec. Time**} \\  & & (**P + N**) & & & & & **(Interf. Size)** \\ \hline \multirow{2}{*}{Etra Ale} & proposed & \multirow{2}{*}{134 (2 + 132)} & 3 February 2016–19 May 2019 & 1.000 & 1.000 & 1.83 (875 \(\times\) 817) \\  & & & 11 December 2016–6 December 2017 & 0.994 & 1.000 & 0.988 & 1.50 s (500 \(\times\) 500) \\ \hline \multirow{2}{*}{Etra} & proposed & \multirow{2}{*}{126 (2 + 124)} & 8 May 2018–21 May 2019 & 0.993 & 1.000 & 0.993 & - \\ \hline \multirow{2}{*}{Etra} & \multirow{2}{*}{189 (2 + 187)} & 3 September 2016–9 November 2017 & 0.871 & 0.747 & 0.981 & - \\  & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) From [34].  
\end{table} 
Table 2: Evaluation of the performances of the proposed CNN, and comparison with the CNN presented by [34] (AlexNet). The metrics are derived from the confusion matrix, where P is the number of real positives in the dataset (i.e., number of interferograms where deformation is recorded), N is the number of real negatives, TPR is the true positive rate, and TNR is the true negative rate. For each volcano we evaluate on the entire interferogram dataset (since training is performed on an independent synthetic dataset), whereas [34] report the average over multiple random splits into training and testing groups of equal size. Interferograms where deformation is expected were marked manually for ground truth (i.e., real positives P), and during evaluation deformation detection threshold was DEF = 0.001, the same threshold as for the email notifications. directions, that imprint as a \"butterfly\" shape. If the dyke reaches the surface, it will result in eruptive fissures/eruptive vents from which lava flows are emplaced. Figure 5 illustrates the efficiency of this automated deformation detection system, taking the case of Piton de la Fournaise (Reunion Island) as an example. The volcano experienced 5 intrusive episodes in the past year (April 2018-April 2019), all of which reached the surface and resulted in lava flows that lasted between 0.7-47 days (episode 1 and 4 respectively). The system successfully detected the interferograms where ground deformation is recorded (Figure 5a-c), illustrating its robustness against various artifacts in the geocoded interferogram (related to strong topography gradients, strong atmospheric phase noise, and decorrelation in vegetated areas). Furthermore, thermal anomalies detected by S2 SWIR analysis (i.e., number of hot pixels) are compared with the Volcanic Radiative Power (VRP) provided by the MIROVA system (Figure 5d) to testify the good correspondence between the active flow area and the heat radiated by the flow surface.