# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 3 Results  
Both maps of individual landslides and of landslide density are useful in the emergency response process [9]. We therefore assessed each classification surface in terms of their ability to: (1) identify individual landslides at a pixel-by-pixel scale; and (2) identify areas that had experienced extensive landsl at a series of increasingly coarse spatial scales. To do this we produced aggregate classification surfaces, for which the original surface was divided into \(N\times N\) pixel squares and the mean pixel value within each square was taken as the aggregate classifier value. These were then normalised as before to produce a surface of values between 0 and 1 for each classifier. A landslide density surface was calculated as the percentage mapped landslide area of each aggregate pixel. For the purpose of ROC analysis, which requires a binary validation dataset, we assigned aggregate pixels with over 50% landslide density as 'landslide' and those with under 50% 'non-landslide', although we also test the sensitivity of all methods to this choice. Figure 8 shows a map of landslides from [PERSON] et al. [2] and each normalised coherence-based classification surface. Two areas are shown, selected to contain different sizes of landslides. The first is around the Village Development Committee (an administrative region) of Jharlang, located in the Himalayan foothills within Dhading District. The second area covers the Langtang Valley in  
Figure 7: Regions that were masked before analysis was carried out, either due to being unmapped by [PERSON] et al. [2] (green, blue) or because of poor SAR image quality, identified using the contributing pixel area (orange, red). Underlying surface shows topography. Rasuwa District, where an exceptionally large landslide with an area of 1.7 km\({}^{2}\) led to hundreds of fatalities [6; 41]. As was found by [PERSON] et al. [21], the large landslide in the Langtang Valley is visible in the ARIA classification surface. However, in the Jharlang area, where landslides were smaller, the ARIA method was less successful, and the surface is noisy. The new method, Bx-S struggles to differentiate between landslide and non-landslide pixels in both locations, as does absolute coherence, which suffers from false positives. Figure 8: Classification of individual landslides for two example locations. Each column shows mapped landslide polygons (pink) [2] and the normalized classification surface calculated from each method for the Jharlang (left) and Langtang Valley (right) regions. Colour bars are non-linear but linear between the white lines and labelled with the percentage of pixels across the two areas that lie in this linear range. The main body of the Langtang Valley landslide is indicated in white. ROC analysis confirms that all three methods perform poorly as landslide classifiers on a pixel-by-pixel scale, with AUC <0.6 (Figure 9). However, Bx-S and to a lesser extent ARIA and absolute coherence are more successful at identifying areas of intense landsliding. ROC analysis shows that increasing pixel size through aggregation results in improved performance for all methods. In particular, Bx-S outperforms the two existing methods at all aggregations and does better with increasing aggregation. ROC AUC for Bx-S increases from 0.56 to 0.77 when aggregated from 20 m \(\times\) 22 m pixels to 300 m \(\times\) 330 m. For the same aggregation, absolute coherence ROC AUC increases from 0.55 to 0.72 and the ARIA method ROC AUC increases from 0.57 to 0.68. Figure 10 shows classification surfaces for the whole area at an aggregated pixel size of 200 m \(\times\) 220 m (\(10\times 10\) pixels), along with a smaller region within Gorkha district. In this smaller region, Bx-S appears relatively successful in recreating the spatial pattern of landslide density, while ARIA and absolute coherence have many false positives, making it difficult to identify the correlation with landslide density.