From 0f2d5e460eb93f6fcc98df0cfde520ff87d56eae Mon Sep 17 00:00:00 2001 From: Michael Mahoney Date: Tue, 10 Sep 2024 20:32:06 -0400 Subject: [PATCH] Changes for mdsumner review (#78) * Add rev roles * Discuss colocated compute * Clarify downloading files without processing * Assign downloads to objects * Better check inputs to query function * Add examples of stretching images * Document merge option * Highlight using st_make_grid() --- DESCRIPTION | 4 ++ R/download.R | 6 ++ R/get_stac_data.R | 30 +++++++- R/query_and_sign.R | 27 +++++++- README.Rmd | 13 +++- README.md | 64 ++++++++++++------ man/figures/README-unnamed-chunk-5-1.png | Bin 0 -> 178398 bytes man/figures/README-unnamed-chunk-7-1.png | Bin 168487 -> 172003 bytes man/figures/README-unnamed-chunk-8-1.png | Bin 0 -> 168487 bytes man/get_stac_data.Rd | 30 +++++++- man/rsi-package.Rd | 2 + man/rsi_query_api.Rd | 7 +- tests/testthat/test-query_and_sign.R | 30 ++++++++ ...nloading-data-from-STAC-APIs-using-rsi.Rmd | 20 ++++-- vignettes/articles/How-can-I-.Rmd | 34 ++++++++++ 15 files changed, 232 insertions(+), 35 deletions(-) create mode 100644 man/figures/README-unnamed-chunk-5-1.png create mode 100644 man/figures/README-unnamed-chunk-8-1.png create mode 100644 tests/testthat/test-query_and_sign.R diff --git a/DESCRIPTION b/DESCRIPTION index 193acdb..d183bf4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -4,6 +4,10 @@ Version: 0.2.1.9000 Authors@R: c( person("Michael", "Mahoney", , "mike.mahoney.218@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2402-304X")), + person("Felipe", "Carvalho", role = "rev", + comment = "Felipe reviewed the package (v. 0.3.0) for rOpenSci, see "), + person("Michael", "Sumner", role = "rev", + comment = "Michael reviewed the package (v. 0.3.0) for rOpenSci, see "), person("Permian Global", role = c("cph", "fnd")) ) Description: Downloads spatial data from spatiotemporal asset catalogs diff --git a/R/download.R b/R/download.R index 2b0be42..cc0f64f 100644 --- a/R/download.R +++ b/R/download.R @@ -80,6 +80,12 @@ rsi_download_rasters <- function(items, gdalwarp_options <- set_gdalwarp_extent(gdalwarp_options, aoi, NULL) } + # Which loop gets parallelized is determined based on which has more steps, + # working from the assumption that all downloads take about as long + # + # so if we aren't merging, or if there's more assets than tiles, we'll + # parallelize the outside loop that walks over assets (which, unless the user + # has set up wild nested futures, turns the inside one into a serial process) asset_iterator <- ifelse( merge || (n_tiles_out < ncol(download_locations)), function(...) future.apply::future_lapply(..., future.seed = TRUE), diff --git a/R/get_stac_data.R b/R/get_stac_data.R index 7890212..cd7368b 100644 --- a/R/get_stac_data.R +++ b/R/get_stac_data.R @@ -132,7 +132,10 @@ #' @param composite_function Character of length 1: The name of a #' function used to combine downloaded images into a single composite #' (i.e., to aggregate pixel values from multiple images into a single value). -#' Must be one of of "sum", "mean", "median", "min", "max". +#' Options include "merge", which 'stamps' images on top of one another such that +#' the "last" value downloaded for a pixel -- which isn't guaranteed to be the most +#' recent one -- will be the only value used, or any of "sum", "mean", "median", +#' "min", or "max", which consider all values available at each pixel. #' Set to `NULL` to not composite #' (i.e., to rescale and save each individual file independently). #' @inheritParams rstac::stac_search @@ -184,6 +187,11 @@ #' output_filename = tempfile(fileext = ".tif") #' ) #' +#' landsat_image |> +#' terra::rast() |> +#' terra::stretch() |> +#' terra::plotRGB() +#' #' # The `get_*_imagery()` functions will download #' # all available "data" assets by default #' # (usually including measured values, and excluding derived bands) @@ -214,6 +222,26 @@ #' output_filename = tempfile(fileext = ".tif") #' ) #' names(terra::rast(sentinel2_imagery)) +#' +#' # If you're downloading data for a particularly large AOI, +#' # and can't composite the resulting images or want to make +#' # sure you can continue an interrupted download, +#' # consider tiling your AOI and requesting each tile separately: +#' aoi <- sf::st_make_grid(aoi, n = 2) +#' tiles <- lapply( +#' seq_along(aoi), +#' function(i) { +#' get_landsat_imagery( +#' aoi[i], +#' start_date = "2022-06-01", +#' end_date = "2022-08-30", +#' output_filename = tempfile(fileext = ".tif") +#' ) +#' } +#' ) +#' # You'll get a list of tiles that you can then composite or +#' # work with however you wish: +#' unlist(tiles) #' #' @export get_stac_data <- function(aoi, diff --git a/R/query_and_sign.R b/R/query_and_sign.R index 0d09a54..6c1e6f8 100644 --- a/R/query_and_sign.R +++ b/R/query_and_sign.R @@ -10,9 +10,10 @@ #' available for your AOI, or to perform cloud filtering prior to downloading #' assets. #' -#' @param bbox An sfc object representing the spatial bounding box of your area -#' of interest. This must be in EPSG:4326 coordinates (and, if this function is -#' called from within `get_stac_data()`, it will be) +#' @param bbox A `bbox` or `sfc` object, from the sf package, representing the +#' spatial bounding box of your area of interest. This must be in EPSG:4326 +#' coordinates (and, if this function is called from within `get_stac_data()`, +#' it will be) or else it will be automatically reprojected. #' @inheritParams get_stac_data #' @param start_date,end_date Character strings of length 1 representing the #' boundaries of your temporal range of interest, in RFC-3339 format. Set either @@ -52,6 +53,26 @@ rsi_query_api <- function(bbox, datetime <- NULL } + if (!(inherits(bbox, "bbox") | inherits(bbox, "sfc"))) { + rlang::abort( + "`bbox` must be either an sfc or a bbox object from the sf package.", + class = "rsi_bbox_wrong_class" + ) + } + + if (!(sf::st_crs(bbox) == sf::st_crs("EPSG:4326"))) { + rlang::warn( + "Reprojecting `bbox` to EPSG:4326.", + class = "rsi_reprojecting_bbox" + ) + if (inherits(bbox, "bbox")) { + bbox <- sf::st_as_sfc(bbox) + } + bbox <- sf::st_transform(bbox, 4326) + } + + if (inherits(bbox, "sfc")) bbox <- sf::st_bbox(bbox) + items <- rstac::stac_search( rstac::stac(stac_source), collections = collection, diff --git a/README.Rmd b/README.Rmd index 7590d9b..c38ed7d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -34,6 +34,8 @@ The goal of rsi is to address several **r**epeated **s**patial **i**nfelicities, + A method for downloading STAC data -- excuse me, **r**etriving **S**TAC **i**nformation -- from any STAC server, with additional helpers for downloading Landsat, Sentinel-1, and Sentinel-2 data from free and public STAC servers providing **r**apid **s**atellite **i**magery, + A **r**aster **s**tack **i**ntegration method for combining multiple rasters containing distinct data sets into a single raster stack. +The functions in rsi are designed around letting you use the tools you're familiar with to process raster data using compute that you control -- whether that means grabbing imagery with your laptop to add some context to a map, or grabbing tranches of data to a virtual server hosted near your data provider for lightning fast downloads. The outputs from rsi functions are standard objects -- usually the file paths of raster files saved to your hard drive -- meaning it's easy to incorporate rsi into broader spatial data processing workflows. + ## Installation You can install rsi via: @@ -87,7 +89,7 @@ landsat_image <- get_stac_data( terra::plot(terra::rast(landsat_image)) ``` -For these common use cases, however, rsi also provides helper functions which +For common data sets, rsi also provides helper functions which provide most of these arguments for you. For instance, that `get_stac_data()` call could be as simple as: @@ -101,6 +103,15 @@ landsat_image <- get_landsat_imagery( terra::plot(terra::rast(landsat_image)) ``` +Note that we've been plotting each band individually so far by calling `terra::plot()`. We could also use `terra::plotRGB()` (after `terra::stretch()`ing the band values) to see what this mosaic of images would look like to the human eye: + +```{r} +landsat_image |> + terra::rast(lyrs = c("R", "G", "B")) |> + terra::stretch() |> + terra::plotRGB() +``` + By default, these functions download data from Microsoft's Planetary Computer API, using a number of configuration options set in `rsi_band_mapping` objects provided by the package. You can see these default configuration options by diff --git a/README.md b/README.md index cf6e208..ae6816e 100644 --- a/README.md +++ b/README.md @@ -8,8 +8,8 @@ [![R-CMD-check](https://github.com/Permian-Global-Research/rsi/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Permian-Global-Research/rsi/actions/workflows/R-CMD-check.yaml) [![Codecov test coverage](https://codecov.io/gh/Permian-Global-Research/rsi/branch/main/graph/badge.svg)](https://app.codecov.io/gh/Permian-Global-Research/rsi?branch=main) -[![License: Apache -2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/license/apache-2-0) +[![License: +Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/license/apache-2-0) [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing) [![Project Status: Active – The project has reached a stable, usable @@ -27,17 +27,29 @@ The goal of rsi is to address several **r**epeated **s**patial and help avoid **r**epetitive **s**tress **i**njuries. Specifically, rsi provides: -- An interface to the **R**some – excuse me, [*Awesome* Spectral Indices - project](https://github.com/awesome-spectral-indices/awesome-spectral-indices), - providing the list of indices directly in R as a friendly tibble, -- A method for efficiently *calculating* those awesome spectral indices - using local rasters, enabling **r**apid **s**pectral **i**nference, -- A method for downloading STAC data – excuse me, **r**etriving **S**TAC - **i**nformation – from any STAC server, with additional helpers for - downloading Landsat, Sentinel-1, and Sentinel-2 data from free and - public STAC servers providing **r**apid **s**atellite **i**magery, -- A **r**aster **s**tack **i**ntegration method for combining multiple - rasters containing distinct data sets into a single raster stack. + - An interface to the **R**some – excuse me, [*Awesome* Spectral + Indices + project](https://github.com/awesome-spectral-indices/awesome-spectral-indices), + providing the list of indices directly in R as a friendly tibble, + - A method for efficiently *calculating* those awesome spectral + indices using local rasters, enabling **r**apid **s**pectral + **i**nference, + - A method for downloading STAC data – excuse me, **r**etriving + **S**TAC **i**nformation – from any STAC server, with additional + helpers for downloading Landsat, Sentinel-1, and Sentinel-2 data + from free and public STAC servers providing **r**apid **s**atellite + **i**magery, + - A **r**aster **s**tack **i**ntegration method for combining multiple + rasters containing distinct data sets into a single raster stack. + +The functions in rsi are designed around letting you use the tools +you’re familiar with to process raster data using compute that you +control – whether that means grabbing imagery with your laptop to add +some context to a map, or grabbing tranches of data to a virtual server +hosted near your data provider for lightning fast downloads. The outputs +from rsi functions are standard objects – usually the file paths of +raster files saved to your hard drive – meaning it’s easy to incorporate +rsi into broader spatial data processing workflows. ## Installation @@ -124,9 +136,9 @@ terra::plot(terra::rast(landsat_image)) -For these common use cases, however, rsi also provides helper functions -which provide most of these arguments for you. For instance, that -`get_stac_data()` call could be as simple as: +For common data sets, rsi also provides helper functions which provide +most of these arguments for you. For instance, that `get_stac_data()` +call could be as simple as: ``` r landsat_image <- get_landsat_imagery( @@ -140,6 +152,20 @@ terra::plot(terra::rast(landsat_image)) +Note that we’ve been plotting each band individually so far by calling +`terra::plot()`. We could also use `terra::plotRGB()` (after +`terra::stretch()`ing the band values) to see what this mosaic of images +would look like to the human eye: + +``` r +landsat_image |> + terra::rast(lyrs = c("R", "G", "B")) |> + terra::stretch() |> + terra::plotRGB() +``` + + + By default, these functions download data from Microsoft’s Planetary Computer API, using a number of configuration options set in `rsi_band_mapping` objects provided by the package. You can see these @@ -176,7 +202,7 @@ indices <- calculate_indices( terra::plot(terra::rast(indices)) ``` - + And last but not least, rsi includes a utility for efficiently combining rasters containing different data about the same location into a @@ -194,14 +220,14 @@ raster_stack <- stack_rasters( terra::plot(terra::rast(raster_stack)) ``` - + This can be extremely useful as a way to create predictor bricks and other multi-band rasters from various data sources. ## Contributing -We love contributions! See our [contribution +We love contributions\! 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single value). -Must be one of of "sum", "mean", "median", "min", "max". +Options include "merge", which 'stamps' images on top of one another such that +the "last" value downloaded for a pixel -- which isn't guaranteed to be the most +recent one -- will be the only value used, or any of "sum", "mean", "median", +"min", or "max", which consider all values available at each pixel. Set to \code{NULL} to not composite (i.e., to rescale and save each individual file independently).} @@ -396,6 +399,11 @@ landsat_image <- get_landsat_imagery( output_filename = tempfile(fileext = ".tif") ) +landsat_image |> + terra::rast() |> + terra::stretch() |> + terra::plotRGB() + # The `get_*_imagery()` functions will download # all available "data" assets by default # (usually including measured values, and excluding derived bands) @@ -426,5 +434,25 @@ sentinel2_imagery <- get_sentinel2_imagery( output_filename = tempfile(fileext = ".tif") ) names(terra::rast(sentinel2_imagery)) + +# If you're downloading data for a particularly large AOI, +# and can't composite the resulting images or want to make +# sure you can continue an interrupted download, +# consider tiling your AOI and requesting each tile separately: +aoi <- sf::st_make_grid(aoi, n = 2) +tiles <- lapply( + seq_along(aoi), + function(i) { + get_landsat_imagery( + aoi[i], + start_date = "2022-06-01", + end_date = "2022-08-30", + output_filename = tempfile(fileext = ".tif") + ) + } +) +# You'll get a list of tiles that you can then composite or +# work with however you wish: +unlist(tiles) \dontshow{\}) # examplesIf} } diff --git a/man/rsi-package.Rd b/man/rsi-package.Rd index 41b9227..2b42c5c 100644 --- a/man/rsi-package.Rd +++ b/man/rsi-package.Rd @@ -24,6 +24,8 @@ Useful links: Other contributors: \itemize{ + \item Felipe Carvalho (Felipe reviewed the package (v. 0.3.0) for rOpenSci, see ) [reviewer] + \item Michael Sumner (Michael reviewed the package (v. 0.3.0) for rOpenSci, see ) [reviewer] \item Permian Global [copyright holder, funder] } diff --git a/man/rsi_query_api.Rd b/man/rsi_query_api.Rd index 483d50e..5cafbea 100644 --- a/man/rsi_query_api.Rd +++ b/man/rsi_query_api.Rd @@ -7,9 +7,10 @@ rsi_query_api(bbox, stac_source, collection, start_date, end_date, limit, ...) } \arguments{ -\item{bbox}{An sfc object representing the spatial bounding box of your area -of interest. This must be in EPSG:4326 coordinates (and, if this function is -called from within \code{get_stac_data()}, it will be)} +\item{bbox}{A \code{bbox} or \code{sfc} object, from the sf package, representing the +spatial bounding box of your area of interest. This must be in EPSG:4326 +coordinates (and, if this function is called from within \code{get_stac_data()}, +it will be) or else it will be automatically reprojected.} \item{stac_source}{Character of length 1: the STAC URL to download imagery from.} diff --git a/tests/testthat/test-query_and_sign.R b/tests/testthat/test-query_and_sign.R new file mode 100644 index 0000000..0a138e8 --- /dev/null +++ b/tests/testthat/test-query_and_sign.R @@ -0,0 +1,30 @@ +test_that("non-4326 CRS warns", { + nc <- sf::read_sf( + system.file("shape/nc.shp", package = "sf") + ) + expect_warning( + rsi_query_api( + sf::st_as_sfc(sf::st_bbox(nc)), + stac_source = "https://planetarycomputer.microsoft.com/api/stac/v1/", + collection = "landsat-c2-l2", + start_date = "2023-08-01", + end_date = "2023-09-01", + limit = 10 + ), + class = "rsi_reprojecting_bbox" + ) +}) + +test_that("unaccepted bbox objects error well", { + expect_error( + rsi_query_api( + "not a bbox", + stac_source = "https://planetarycomputer.microsoft.com/api/stac/v1/", + collection = "landsat-c2-l2", + start_date = "2023-08-01", + end_date = "2023-09-01", + limit = 10 + ), + class = "rsi_bbox_wrong_class" + ) +}) diff --git a/vignettes/articles/Downloading-data-from-STAC-APIs-using-rsi.Rmd b/vignettes/articles/Downloading-data-from-STAC-APIs-using-rsi.Rmd index 3edaa63..78ce0fc 100644 --- a/vignettes/articles/Downloading-data-from-STAC-APIs-using-rsi.Rmd +++ b/vignettes/articles/Downloading-data-from-STAC-APIs-using-rsi.Rmd @@ -228,12 +228,14 @@ So, with that out of the way, let's actually download and composite some Landsat ```{r} projected_ashe <- sf::st_transform(ashe, 6542) -get_landsat_imagery( +ashe_landsat <- get_landsat_imagery( aoi = projected_ashe, start_date = "2021-06-01", end_date = "2021-06-30", output_filename = tempfile(fileext = ".tif") -) |> +) + +ashe_landsat |> terra::rast() |> terra::plot() ``` @@ -256,14 +258,15 @@ landsat_band_mapping$planetary_computer_v1["coastal"] And last but not least, if we wanted to skip compositing altogether, we could set the `composite_function` argument to `NULL` in order to download each image in our spatiotemporal AOI separately: ```{r} -get_landsat_imagery( +ashe_no_composite <- get_landsat_imagery( aoi = projected_ashe, start_date = "2021-06-01", end_date = "2021-06-30", output_filename = tempfile(fileext = ".tif"), composite_function = NULL, mask_function = NULL # otherwise half of these images are blank -) |> +) +ashe_no_composite |> lapply(terra::rast) |> lapply(terra::plot) |> invisible() @@ -338,13 +341,14 @@ custom_query_function <- function(bbox, rstac::items_fetch(rstac::post_request(request)) } -get_landsat_imagery( +ashe_cql <- get_landsat_imagery( aoi = projected_ashe, start_date = "2021-06-01", end_date = "2021-06-30", output_filename = tempfile(fileext = ".tif"), query_function = custom_query_function -) |> +) +ashe_cql |> terra::rast() |> terra::plot() ``` @@ -355,4 +359,6 @@ If we know how to express our desired query in CQL2, we can write arbitrarily co rsi is not nearly the only package aiming to help R users take advantage of STAC APIs and build cloud-native geospatial workflows. As shown above, rsi is fundamentally built on top of the excellent rstac package, which I think is a fantastic tool for interactively exploring STAC APIs as well as building and executing queries. My hope is that rsi can provide a useful layer of abstraction over rstac for efficiently downloading assets and performing some of the most common rescaling, masking, and compositing tasks involved in standard data processing workflows. -There are also several other packages which also implement workflows for efficiently accessing and processing data from STAC endpoints, among them the [gdalcubes](https://gdalcubes.github.io/) and [sits](https://e-sensing.github.io/sitsbook/) packages. A core difference between rsi and these packages is that rsi does not have a data model: rsi is focused entirely on finding the bits of data you want from remote endpoints, and getting those bits on your local machine for you to process with your normal spatial data tooling. There are no new classes in rsi (other than the band mapping objects), and the outputs of functions are local rasters. This is an approach that fits better in my head than the more abstract delayed computations in some other packages; at the same time, it's possible that this approach can be less efficient, downloading more data at finer resolutions than is actually needed for a given task. As a result, users need to make sure they're only requesting data they actually need. +There are also several other packages which also implement workflows for efficiently accessing and processing data from STAC endpoints, among them the [gdalcubes](https://gdalcubes.github.io/) and [sits](https://e-sensing.github.io/sitsbook/) packages. A core difference between rsi and these packages is that rsi does not have a data model: rsi is focused entirely on finding the bits of data you want from remote endpoints, and getting those bits on your local machine for you to process with your normal spatial data tooling. There are no new classes in rsi (other than the band mapping objects), and the outputs of functions are local rasters. This is an approach that fits better in my head than the more abstract delayed computations in some other packages; at the same time, it's possible that this approach can be less efficient, downloading more data at finer resolutions than is actually needed for a given task. + +To make data-downloading functions run faster, make sure you're only requesting the data you actually need -- often restricting the number of assets to be downloaded from each item, specifying a more precise bounding box, or using a narrower date range can cut the amount of data to be downloaded significantly. If there's no way around downloading a large amount of data, consider if running your code on a virtual (cloud) server that's physically closer to your data makes sense for your use case. Many data providers publish information on where their collections are hosted (with cloud providers like Microsoft naming the specific region it's in), and using servers that are closer to the data can result in notably faster download speeds. diff --git a/vignettes/articles/How-can-I-.Rmd b/vignettes/articles/How-can-I-.Rmd index 84c9e36..6836674 100644 --- a/vignettes/articles/How-can-I-.Rmd +++ b/vignettes/articles/How-can-I-.Rmd @@ -53,6 +53,40 @@ This ensures that `get_stac_data()` is run using the same arguments each time, s invisible(lapply(lapply(downloaded_years, terra::rast), terra::plot)) ``` +## Get each distinct item that matches a query, without compositing? + +If we want to download the individual STAC items that are returned by our query, we can set the `composite_function` argument to `NULL`. This will return one file per STAC item, with the `datetime` element of the item appended to the filename if it exists (and a sequential identifier if it doesn't): + +```{r} +aoi <- sf::st_point(c(-74.912131, 44.080410)) +aoi <- sf::st_set_crs(sf::st_sfc(aoi), 4326) +aoi <- sf::st_buffer(sf::st_transform(aoi, 5070), 1000) + +our_imagery <- get_landsat_imagery( + aoi, + start_date = "2023-06-01", + end_date = "2023-07-01", + output_filename = tempfile(fileext = ".tif"), + composite_function = NULL +) + +our_imagery +``` + +Each of these files will then contain the assets corresponding to that individual STAC item: + +```{r} +terra::rast(our_imagery[[2]], lyrs = c("R", "G", "B")) |> + terra::stretch() |> + terra::plotRGB() +``` + +```{r} +terra::rast(our_imagery[[4]], lyrs = c("R", "G", "B")) |> + terra::stretch() |> + terra::plotRGB() +``` + ## Filter the imagery I download by cloud cover (or other metadata)? If you want to refine the outputs from your STAC query, the best approach is to write a custom query function using CQL2. CQL2 is a complicated topic, which is covered in a bit more detail both [in the Downloading Data vignette](https://permian-global-research.github.io/rsi/articles/Downloading-data-from-STAC-APIs-using-rsi.html#using-cql2-to-refine-queries-to-stac-apis) as well as in [the STAC website's tutorials](https://github.com/radiantearth/stac-site/blob/main/app/tutorials/r/2-using-rstac-and-cql2-to-query-stac-api/2-using-rstac-and-cql2-to-query-stac-api.en.md), but at its core is a query language that lets us filter our results down using a spatiotemporal area of interest as well as other item-level metadata.

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