Update README.md

README.md

@@ -120,6 +120,9 @@ The LiDAR layer was derived from a Digital Elevation Model (DEM) with a spatial
 The Landslides layer represents ground truth data, manually annotated by experts from the State Geological Institute of Dionýz Štúr. A pixel value of <b>1</b> indicates the presence of a landslide.
 The data in both the <b>raw</b> and <b>processed</b> folders are identical, as no additional processing was applied to this layer.
 
+## Aspect
+TODO
+
 ## Curvature
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651d578a1819d9e5754ffc47/H1r3Gb1Fho2h-Unc7gy6e.png)
 The Curvature layer was calculated from the DEM after applying a smoothing filter to reduce noise. A 3×3 kernel was used to smooth the DEM before curvature computation. The curvature was computed using the following formula (see accompanying image for the legend):
@@ -162,6 +165,15 @@ The NDVI layer was computed from the annual average satellite data covering the
 It is used to determine the health and density of vegetation and is computed as a normallized difference of bands B8 (Near-Infrared) and B4 (Visible Red).
 The data in both the <b>raw</b> and <b>processed</b> folders are identical, as no further processing was applied to this layer.
 
+## Negative openness
+TODO
+
+## Positive openness
+TODO
+
+## Possitive and negative openness difference
+TODO
+
 ## Roughness
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651d578a1819d9e5754ffc47/qcLbwXT_n_uQpywolUX7N.png)
 The Roughness layer was derived from the DEM using the roughness function in QGIS, which measures the variability of elevation within a local neighborhood.