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api_detect_changes.R
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api_detect_changes.R
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#' @title Detect changes in time-series using various methods.
#' @name .detect_change_ts
#' @keywords internal
#' @noRd
.detect_change_ts <- function(samples,
cd_method,
filter_fn,
multicores,
progress) {
# Start parallel workers
.parallel_start(workers = multicores)
on.exit(.parallel_stop(), add = TRUE)
# Get bands from model
bands <- .ml_bands(cd_method)
# Update samples bands order
if (any(bands != .samples_bands(samples))) {
samples <- .samples_select_bands(samples = samples,
bands = bands)
}
# Apply time series filter
if (.has(filter_fn)) {
samples <- .apply_across(data = samples,
fn = filter_fn)
}
# Divide samples in chunks to parallel processing
parts <- .pred_create_partition(pred = samples, partitions = multicores)
# Detect changes!
.jobs_map_parallel_dfr(parts, function(part) {
# Get samples
values <- .pred_part(part)
# Detect changes! For detection, models can be time-aware. So, the
# complete data, including dates, must be passed as argument.
detections <- cd_method(values[["time_series"]], "ts")
detections <- tibble::tibble(detections)
# Prepare result
result <- tibble::tibble(data.frame(values, detections = detections))
class(result) <- class(values)
# return
result
}, progress = progress)
}
#' @title Detect changes from a chunk of raster data using multicores
#' @name .detect_change_tile
#' @keywords internal
#' @noRd
#' @param tile Single tile of a data cube.
#' @param band Band to be produced.
#' @param cd_method Change Detection Model.
#' @param block Optimized block to be read into memory.
#' @param roi Region of interest.
#' @param filter_fn Smoothing filter function to be applied to the data.
#' @param impute_fn Imputation function.
#' @param output_dir Output directory.
#' @param version Version of result.
#' @param verbose Print processing information?
#' @param progress Show progress bar?
#' @return List of the classified raster layers.
.detect_change_tile <- function(tile,
band,
cd_method,
block,
roi,
filter_fn,
impute_fn,
output_dir,
version,
verbose,
progress) {
# Output file
out_file <- .file_derived_name(
tile = tile,
band = band,
version = version,
output_dir = output_dir
)
# Resume feature
if (file.exists(out_file)) {
if (.check_messages()) {
.check_recovery(out_file)
}
detected_changes_tile <- .tile_derived_from_file(
file = out_file,
band = band,
base_tile = tile,
labels = .ml_labels_code(cd_method),
derived_class = "detections_cube",
update_bbox = TRUE
)
return(detected_changes_tile)
}
# Show initial time for tile classification
tile_start_time <- .tile_classif_start(
tile = tile,
verbose = verbose
)
# Tile timeline
tile_timeline <- .tile_timeline(tile)
# Create chunks as jobs
chunks <- .tile_chunks_create(
tile = tile,
overlap = 0,
block = block
)
# By default, update_bbox is FALSE
update_bbox <- FALSE
if (.has(roi)) {
# How many chunks there are in tile?
nchunks <- nrow(chunks)
# Intersecting chunks with ROI
chunks <- .chunks_filter_spatial(
chunks = chunks,
roi = roi
)
# Should bbox of resulting tile be updated?
update_bbox <- nrow(chunks) != nchunks
}
# Process jobs in parallel
block_files <- .jobs_map_parallel_chr(chunks, function(chunk) {
# Job block
block <- .block(chunk)
# Block file name
block_file <- .file_block_name(
pattern = .file_pattern(out_file),
block = block,
output_dir = output_dir
)
# Resume processing in case of failure
if (.raster_is_valid(block_file)) {
return(block_file)
}
# Read and preprocess values
values <- .classify_data_read(
tile = tile,
block = block,
bands = .ml_bands(cd_method),
ml_model = cd_method,
impute_fn = impute_fn,
filter_fn = filter_fn
)
# Get mask of NA pixels
na_mask <- C_mask_na(values)
# Fill with zeros remaining NA pixels
values <- C_fill_na(values, 0)
# Used to check values (below)
input_pixels <- nrow(values)
# Include names in cube predictors
colnames(values) <- .pred_features_name(
.ml_bands(cd_method), tile_timeline
)
# Prepare values
values <- .pred_as_ts(values, .ml_bands(cd_method), tile_timeline) |>
tidyr::nest(.by = "sample_id", .key = "time_series")
# Log here
.debug_log(
event = "start_block_data_detection",
key = "model",
value = .ml_class(cd_method)
)
# Detect changes!
values <- cd_method(values[["time_series"]], "cube") |>
dplyr::as_tibble()
# Are the results consistent with the data input?
.check_processed_values(
values = values,
input_pixels = input_pixels
)
# Log here
.debug_log(
event = "end_block_data_detection",
key = "model",
value = .ml_class(cd_method)
)
# Prepare probability to be saved
band_conf <- .conf_derived_band(
derived_class = "detections_cube",
band = band
)
offset <- .offset(band_conf)
if (.has(offset) && offset != 0) {
values <- values - offset
}
scale <- .scale(band_conf)
if (.has(scale) && scale != 1) {
values <- values / scale
}
# Mask NA pixels with same probabilities for all classes
values[na_mask, ] <- 0 # event detection = 1, no event = 0
# Log
.debug_log(
event = "start_block_data_save",
key = "file",
value = block_file
)
# Prepare and save results as raster
.raster_write_block(
files = block_file,
block = block,
bbox = .bbox(chunk),
values = values,
data_type = .data_type(band_conf),
missing_value = .miss_value(band_conf),
crop_block = NULL
)
# Log
.debug_log(
event = "end_block_data_save",
key = "file",
value = block_file
)
# Free memory
gc()
# Returned block file
block_file
}, progress = progress)
# Merge blocks into a new detections_cube tile
detections_tile <- .tile_derived_merge_blocks(
file = out_file,
band = band,
labels = .ml_labels_code(cd_method),
base_tile = tile,
block_files = block_files,
derived_class = "detections_cube",
multicores = .jobs_multicores(),
update_bbox = update_bbox
)
# show final time for detection
.tile_classif_end(
tile = tile,
start_time = tile_start_time,
verbose = verbose
)
# Return detections tile
detections_tile
}