Extract byte-range chunk references and decode tiles from cloud-hosted TIFF and COG files, entirely from R, without Python or GDAL.
Uses Rust crates async-tiff and object_store (Apache arrow-rs) for async I/O across S3, GCS, Azure, HTTP, and local storage.
Three modes:
- Inspect (
tiff_ifd_info): compact one-row-per-IFD structural summary - Scan (
tiff_refs): full per-tile byte-range references for Kerchunk/Zarr virtual stores - Decode (
tiff_read_tiles,tiff_tile,tiff_tiles): fetch and decompress pixel data
Requires a Rust toolchain (rustup). See
inst/design-docs/rust-setup.md for guidance.
install.packages("rustycogs", repos = "https://hypertidy.r-universe.dev")library(rustycogs)
# One row per IFD — fast way to understand a file before fetching tiles
tiff_ifd_info("https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif")
#> path ifd is_tiled
#> 1 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 0 TRUE
#> 2 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 1 TRUE
#> 3 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 2 TRUE
#> 4 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 3 TRUE
#> 5 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 4 TRUE
#> 6 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 5 TRUE
#> 7 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 6 TRUE
#> 8 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 7 TRUE
#> 9 https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif 8 TRUE
#> image_w image_h tile_w tile_h n_tiles_x n_tiles_y dtype compression
#> 1 86400 43200 512 512 169 85 <i2 Deflate
#> 2 43200 21600 512 512 85 43 <i2 Deflate
#> 3 21600 10800 512 512 43 22 <i2 Deflate
#> 4 10800 5400 512 512 22 11 <i2 Deflate
#> 5 5400 2700 512 512 11 6 <i2 Deflate
#> 6 2700 1350 512 512 6 3 <i2 Deflate
#> 7 1350 675 512 512 3 2 <i2 Deflate
#> 8 675 337 512 512 2 1 <i2 Deflate
#> 9 337 168 512 512 1 1 <i2 Deflate
#> bits_per_sample samples_per_pixel photometric predictor planar_configuration
#> 1 16 1 BlackIsZero None Chunky
#> 2 16 1 BlackIsZero None Chunky
#> 3 16 1 BlackIsZero None Chunky
#> 4 16 1 BlackIsZero None Chunky
#> 5 16 1 BlackIsZero None Chunky
#> 6 16 1 BlackIsZero None Chunky
#> 7 16 1 BlackIsZero None Chunky
#> 8 16 1 BlackIsZero None Chunky
#> 9 16 1 BlackIsZero None Chunky
#> crs_epsg gdal_nodata scale_x scale_y origin_x origin_y
#> 1 4326 -32767 0.004166667 0.004166667 -180 90
#> 2 NA -32767 NA NA NA NA
#> 3 NA -32767 NA NA NA NA
#> 4 NA -32767 NA NA NA NA
#> 5 NA -32767 NA NA NA NA
#> 6 NA -32767 NA NA NA NA
#> 7 NA -32767 NA NA NA NA
#> 8 NA -32767 NA NA NA NA
#> 9 NA -32767 NA NA NA NA# Scan a COG — one row per tile per IFD
refs <- tiff_refs("s3://sentinel-2-c1-l2a/55/G/DN/2026/2/S2C_T55GDN_20260227T000650_L2A/B04.tif", region = "us-west-2", anon = TRUE)
#> Warning: Failed to open TIFF s3://sentinel-2-c1-l2a/55/G/DN/2026/2/S2C_T55GDN_20260227T000650_L2A/B04.tif: Object at location 55/G/DN/2026/2/S2C_T55GDN_20260227T000650_L2A/B04.tif not found: Error performing GET https://s3.us-west-2.amazonaws.com/sentinel-2-c1-l2a/55/G/DN/2026/2/S2C_T55GDN_20260227T000650_L2A/B04.tif in 524.722502ms - Server returned non-2xx status code: 404 Not Found: <?xml version="1.0" encoding="UTF-8"?>
#> <Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>sentinel-2-c1-l2a</BucketName><RequestId>R0WPQZ2HBQCQNQ4S</RequestId><HostId>JC/FZM/8tWQ11CPHZ9dfiRJT41Yts4PkyX253FVaJ4T5+HaQdlQ1xmAgKCmR29n/69vf5hIrvIw=</HostId></Error>
## or just
#refs <- tiff_refs(""https://e84-earth-search-sentinel-data.s3.us-west-2.amazonaws.com/sentinel-2-c1-l2a/55/G/DN/2026/2/S2C_T55GDN_20260227T000650_L2A/B04.tif",
# region = "", anon = TRUE)
## Write to Parquet for large reference sets
#arrow::write_parquet(refs, "references.parquet")# From a refs data frame — multi-file, vectorized, list-column result
refs <- tiff_refs("scene.tif")
refs <- tiff_read_tiles(refs)
arrays <- lapply(refs$data, tile_to_array)
# Single file batch
tiles <- tiff_tiles("scene.tif", cols = 0:3, rows = rep(0L, 4))
arrays <- lapply(tiles, tile_to_array)
# Single tile
tile <- tiff_tile("scene.tif", col = 0L, row = 0L)
m <- tile_to_array(tile)
ximage::ximage(m)One row per IFD:
path | ifd | is_tiled | image_w | image_h | tile_w | tile_h |
n_tiles_x | n_tiles_y | dtype | compression | bits_per_sample |
samples_per_pixel | photometric | predictor | planar_configuration |
crs_epsg | gdal_nodata | scale_x | scale_y | origin_x | origin_y
One row per tile per IFD — all columns from tiff_ifd_info plus:
tile_col | tile_row | offset | length
refs with a data list-column appended. Each element is a numeric
vector of decoded pixel values in row-major order; pass to
tile_to_array() to get a matrix or array.
A list (or list of lists) with:
data: numeric vector of decoded pixel values (row-major)dim: integer vectorc(height, width, bands)dtype: numpy-style type string ("<f4","<u2", etc.)
tile_to_array() returns a matrix filled byrow = TRUE — consistent
with row-major order from async-tiff and expected by rasterImage() and
ximage(). For a round-trip: as.vector(t(m)) recovers the original
vector. Multi-band tiles: aperm(a, c(2, 1, 3)) swaps spatial axes to R
column-major while keeping bands in the third position.
The refs table contains everything needed to build a simple reader without decoding in Rust — useful for formats or compressions not yet in async-tiff:
library(rustycogs)
refs <- tiff_refs(
"https://s3.ap-southeast-2.amazonaws.com/ausseabed-public-warehouse-bathymetry/L3/6009f454-290d-4c9a-a43d-00b254681696/Australian_Bathymetry_and_Topography_2023_250m_MSL_cog.tif",
anon = TRUE
)
tile_via_vsi <- function(refs, idx = 1, dsn_prefix = "/vsicurl/") {
r <- refs[idx, ]
vsi <- new(gdalraster::VSIFile, paste0(dsn_prefix, r$path))
vsi$seek(r$offset, gdalraster::SEEK_SET)
bytes <- vsi$read(r$length)
vsi$close()
uncomp <- memDecompress(bytes, "gzip")
readBin(uncomp, "numeric", n = r$tile_w * r$tile_h, size = r$bits_per_sample / 8)
}
tail(refs[, 2:6], 5)
#> ifd tile_col tile_row offset length
#> 3491 4 3 2 10873651 445133
#> 3492 5 0 0 44388 920443
#> 3493 5 1 0 964839 906069
#> 3494 5 0 1 1870916 234626
#> 3495 5 1 1 2105550 234953
tilevals <- tile_via_vsi(refs[nrow(refs) - 3, ])
ximage::ximage(
matrix(tilevals, 512L, byrow = TRUE),
col = hcl.colors(24),
breaks = quantile(tilevals, seq(0, 1, length.out = 25))
)
- vapour — GDAL-based raster/vector reading
- grout — tile scheme calculations
- gdalraster — GDAL bindings for R
- async-tiff — the Rust crate
- virtual-tiff — Python equivalent
Please note that the rustycogs project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.