https://github.com/mdsumner/rema-ovr
REMA v2 overviews
https://github.com/mdsumner/rema-ovr
Last synced: 5 months ago
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REMA v2 overviews
- Host: GitHub
- URL: https://github.com/mdsumner/rema-ovr
- Owner: mdsumner
- Created: 2023-11-25T22:16:24.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-12-21T08:12:36.000Z (6 months ago)
- Last Synced: 2025-12-23T01:47:00.110Z (6 months ago)
- Size: 185 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Code of conduct: CODE_OF_CONDUCT.md
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README
# REMA v2
We have created a VRT with efficient overviews, so a GDAL-capable software can read any portion very efficiently.
The DSN is here:
```
/vsicurl/https://raw.githubusercontent.com/mdsumner/rema-ovr/main/REMA-2m_dem_ovr.vrt
```
Size is 2725100, 2921100
## Examples
Use the warper, it will target the right level of detail. This works as well in Python osgeo.gdal as it does at the command line and in R's terra (there's some gotchas still in terra).
```python
from osgeo import gdal
import rasterio
gdal.UseExceptions()
## Davis in EPSG:3031
pt = [2302286, 490792]
dsn = "/vsicurl/https://raw.githubusercontent.com/mdsumner/rema-ovr/main/REMA-2m_dem_ovr.vrt"
ds = gdal.Open(dsn)
r = 500
gdal.Warp("/tmp/davis.tif", ds, outputBounds = [pt[0] - r, pt[1] - r, pt[0] + r, pt[1] + r])
# >
r = rasterio.open("/tmp/davis.tif")
r.shape
#(500, 500)
# array([[[17.632812, 17.632812, 17.632812, ..., 17.648438, 17.648438,
# 17.648438],
# [17.632812, 17.632812, 17.632812, ..., 17.648438, 17.648438,
# 17.648438],
# [17.632812, 17.632812, 17.632812, ..., 17.648438, 17.648438,
# 17.648438],
# ...,
# [17.648438, 17.648438, 17.648438, ..., 31.132812, 31.109375,
# 31.0625 ],
# [17.648438, 17.648438, 17.648438, ..., 31.09375 , 31.070312,
# 31.03125 ],
# [17.648438, 17.648438, 17.648438, ..., 31.070312, 31.039062,
# 31.015625]]], dtype=float32)
```
```R
library(terra)
rema <- rast("/vsicurl/https://raw.githubusercontent.com/mdsumner/rema-ovr/main/REMA-2m_dem_ovr.vrt")
rema
#class : SpatRaster
#dimensions : 2921100, 2725100, 1 (nrow, ncol, nlyr)
#resolution : 2, 2 (x, y)
#extent : -2700100, 2750100, -2500100, 3342100 (xmin, xmax, ymin, ymax)
#coord. ref. : WGS 84 / Antarctic Polar Stereographic (EPSG:3031)
#source : REMA-2m_dem_ovr.vrt
#name : REMA-2m_dem_ovr
plot(project(rema, rast(), by_util = TRUE))
plot(project(rema, aggregate(rast(rema), 1e4), by_util = TRUE))
```
We can get exactly the level we want, say let's hit the 1km:
```R
r1km <- project(rema, rast(ext(rema), res = 1000, crs = crs(rema)), by_util = TRUE)
r1km
#class : SpatRaster
#dimensions : 5842, 5450, 1 (nrow, ncol, nlyr)
#resolution : 1000, 1000 (x, y)
#extent : -2700100, 2749900, -2500100, 3341900 (xmin, xmax, ymin, ymax)
#coord. ref. : WGS 84 / Antarctic Polar Stereographic (EPSG:3031)
#source(s) : memory
#name : REMA-2m_dem_ovr
#min value : -66.44531
#max value : 4639.31250
plot(crop(r1km, ext(0, xmax(r1km), 0, ymax(r1km))))
```
```R
ex <- c(2301786, 490292, 2302786, 491292)[c(1, 3, 2, 4)]
r_davis <- project(rema, rast(ext(ex), res = 2, crs = crs(rema)), by_util = TRUE)
r_davis
# class : SpatRaster
# dimensions : 500, 500, 1 (nrow, ncol, nlyr)
# resolution : 2, 2 (x, y)
# extent : 2301786, 2302786, 490292, 491292 (xmin, xmax, ymin, ymax)
# coord. ref. : WGS 84 / Antarctic Polar Stereographic (EPSG:3031)
# source(s) : memory
# name : REMA-2m_dem_ovr
# min value : 17.63281
# max value : 44.60938
plot(r_davis, col = hcl.colors(64))
```
## PS. Don't use these files below, they are fyi
Use the single-end-point VRT above with the warper :)
These files are involved along with the 2m DEM tiles of the original 2m VRT.
```
https://raw.githubusercontent.com/mdsumner/rema-ovr/main/rema-vrt/10m_dem_tiles.vrt
https://raw.githubusercontent.com/mdsumner/rema-ovr/main/rema-vrt/32m_dem_tiles.vrt
https://github.com/mdsumner/rema-ovr/raw/main/rema_mosaic_100m_v2.0_filled_cop30_dem_int16.tif
https://github.com/mdsumner/rema-ovr/raw/main/rema_mosaic_500m_v2.0_filled_cop30_dem.tif
https://github.com/mdsumner/rema-ovr/raw/main/rema_mosaic_1km_v2.0_filled_cop30_dem.tif
```
The .vrt files are provided by REMA v2 itself. The 500m and 1km tifs are also provided by REMA, but in very slow-to-acccess tarballs. They are copied here.
The 100m is a conversion of the `rema_mosaic_100m_v2.0_dem.tif` to remove overviews, use sparse tiles, and Int16 storage so it's small enough for Github.
We use the filled_cop30 in preference.
## File conversion
```
gdal_translate rema_mosaic_100m_v2.0_filled_cop30_dem.tif rema_mosaic_100m_v2.0_filled_cop30_dem_int16.tif -of COG -co COMPRESS=LZW -co SPARSE_OK=YES -co OVERVIEWS=NONE -ot Int16
```
(this one is not used by the VRT): rema_mosaic_100m_v2.0_dem_int16.tif was created with
```
gdal_translate rema_mosaic_100m_v2.0_dem.tif -of COG -co COMPRESS=ZSTD -co SPARSE_OK=YES -co OVERVIEWS=NONE out.tif -ot Int16
```
## Code of Conduct
Please note that the rema-ovr project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.