Any digital elevation map fans in the house? The Kyoto area according to ASTER v2 and the SRTM v3, both ~1 arcsecond (30 meters per pixel). In the former you can much more clearly see the Kamo River, flowing alongside 下鴨神社 and 上賀茂神社 near the top.
Texture shading forthcoming.
https://octodon.social/media/eHhMOdCopR7C-H4JXAY https://octodon.social/media/oBtRksIHEbAuUiYHtmw
#Kyoto, ASTER v2 and SRTM v3 again, but this time accompanied by texture-shaded render.
(Super-rough Julia code at https://github.com/fasiha/Texshade.jl/blob/master/src/Texshade.jl)
https://octodon.social/media/dzN4X8aqdEImlPKw4JI https://octodon.social/media/q9ZnzYd60SV6L0_8aPM https://octodon.social/media/-k0iBGcgabtxjSXpbk0 https://octodon.social/media/HoQBFpbplB2MJ9B-cV4
Notice how, in the texture-shaded versions, the spiny ridges of mountains and the river valleys pop out. Texture-shading is just a specific sharpening filter invented by Leland Brown and implementable in ~2 lines of Julia/Python/Matlab. (To make it work on global datasets, i.e., terabyte-sized maps, though, you need a few more lines—basically Hankel transforms, overlap-add convolution, and memory-mapped files.)
The conventional way to show terrain is hill-shading, per this Google Terrain map. For many use-cases, like preparing to walk a terrain, or understand its drainage systems, or trace the ridges of its hills, texture-shading is way better 😌. (10 meters per pixel USGS NED data, #Cleveland, #Ohio, USA).
https://octodon.social/media/5YUByLb6gkob3ClA77g https://octodon.social/media/k004K9eQ6y2nCjAs-2M https://octodon.social/media/6l4LxiJiMH07Y7gikMk https://octodon.social/media/nh71xqdX7kJUzCBwC4A
Should probably publicly tag these with #Kyoto #GIS 😅!