# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 3 Dataset  
To develop and test the analysis described in Section 2, various dedicated datasets were employed (Table 1). These comprise images of natural environment sites with heterogeneous geo-hydrological phenomena in north-western (NW) Italy (Figure 3). The generality of the method was thus strengthened by using a wide range of images. Two different datasets comprise images from Monesi village (Liguria region, NW Italy), which is an Alpine village partially affected by a rotational slide activated during a flood in October 2016. MNS1 is the shaded relief extracted from a digital surface model (DSM) acquired using a drone and MNS2 is an RGB dataset acquired using a fixed camera installed on the opposite side of the valley. The ACC dataset (Piemonte region, NW Italy) comprises a sequence of images acquired by a fixed camera installed to monitor the evolution of a large bedrock outcrop that has historically been affected by rock falls. During the monitoring period, there was no evidence of any slope instability activation in the area studied. Figure 2: Workflow of an automated procedure used in image cross-correlation (ICC) applications. Three different datasets comprise images of the Mont de la Saxe rockslide (Valle d'Aosta region, NW of Italy); this is an active landslide with an estimated volume of \(10^{7}\) m\({}^{3}\) that endangers the underlying village and road, which road provides access to the Mont Blanc tunnel, an important communication route between Italy and France [5; 43; 44; 45]. The three different datasets that were acquired from three different view perspectives for this rockslide are SAXE2 and SAXE3, which were acquired by cameras positioned outside the area of gravitational instability, and SAXE1, the camera for which was installed on the rockslide and controlled one of the most active sectors. SAXE3 is divided into two subsamples: SAXE3_a, which includes pseudo-random images taken in 2014 and 2015, and SAXE3_b, which is a time-lapse relating to the period 15-30 April 2014, when activity was pronounced, and a large failure occurred. There are no images included in either SAXE3_a or SAXE3_b. The last dataset consists of images of the Planpincieux glacier on the Italian side of the Mont Blanc massif. The glacier is characterised by relevant kinematics and dynamics; numerous detachments from the snout have endangered the underlying hamlet of Planpincieux. This glacier has therefore been the subject of various remote sensing surveys in the recent past [15; 34; 46]. Two different cameras monitor the evolution of the glacier: PPCX1 acquires images of the lower part of the glacier while PPCX2 acquires images of the right lower tongue, the most active part of the glacier. The PPCX2 dataset, like the SAXE3 dataset, is divided into two distinct subsamples: PPCX2_a, which comprises images acquired during the years 2014-2017; and PPCX2_b, which includes the complete dataset acquired between 18 May and 3 October 2018. During this period, the automated ICC procedure described in Section 2.4 was applied in real time and more than 1500 images were collected. The following text describes and discusses the use of every dataset in the procedures described in Sections 2.2-2.4. To evaluate the effect of shadows we conducted two different analyses: (1) A synthetic set of ten shaded reliefs of a DSM was produced by changing the lighting source position to simulate the sun's motion during the day. Shadows cast by obstacles were projected by computing the viewshed from the light source. Three of the ten shaded reliefs produced are shown in Figure 4a.1-a.3. (2) Ground-based RGB pictures of the same area acquired in different illumination conditions were considered. Three groups of ten images were selected that were acquired in (i) diffuse illumination (Figure 4(b.1)), (ii) direct illumination (Figure 4(b b.2)), and (iii) direct illumination at the same hour (at 12:00) (Figure 4(b b.3)), so that similar direct illumination was maintained in the photos.