d4c-datapkg-orthoimagery

Look through our d4c-datapkg-orthoimagery repo for the datasets that are currently being processed and datasets being acquired.

Development Environment

Although we prioritize open-source tools, we currently use MapTiler Engine Pro because it outperforms available open-source alternatives for this specific workflow.

Specifications of Tile Packages

The specifications for the tile packages are defined in this code.

#!/bin/bash
PROJECT_DIR="~/Documents/Personal/Projects/dataforcanada/d4c-datapkg-orthoimagery"
DATASET_ID="ca-mb_winnipeg-2024A00054611040_orthoimagery_2024_075mm"
DATA_DIR="${PROJECT_DIR}/data"
DATA_INPUT_DIR="${DATA_DIR}/input/${DATASET_ID}"
DATA_OUTPUT_DIR="${DATA_DIR}/output/${DATASET_ID}"

MBTILES_OUTPUT_FILE="${DATA_OUTPUT_DIR}/${DATASET_ID}.mbtiles"
PMTILES_OUTPUT_FILE="${DATA_OUTPUT_DIR}/${DATASET_ID}.pmtiles"

# Define arguments in an array
ARGS=(
  -progress
  -name "City of Winnipeg Orthoimagery for 2024 / Ortho-imagerie de la Ville de Winnipeg de 2024"
  -description "Orthoimagery 7.5cm resolution. / Ortho-imagerie à résolution de 7,5 cm."
  -attribution "Source: data.winnipeg.ca / Source: data.winnipeg.ca"
  -srs_epsg
  -mbtiles_compatible
  -wo "NUM_THREADS=ALL_CPUS"
  -wo "USE_OPENCL=TRUE"
  -sparse
  -scale 2.000000
  -work_dir ~/tmp/maptiler_engine
  -f webp32
  -webp_quality 85
  -webp_lossy
  -webp_preset photo
  -resampling cubic
  -overviews_resampling average
  -o "${MBTILES_OUTPUT_FILE}"
  $DATA_INPUT_DIR/*.ecw
)

# Run the command with the array
maptiler-engine "${ARGS[@]}"

pmtiles convert --tmpdir=~/tmp/pmtiles ${MBTILES_OUTPUT_FILE} ${PMTILES_OUTPUT_FILE}

Download and Preview

Here is a table of some of the datasets created from the current process.

Place	ISO	Year	Provider	Dataset ID	PMTiles
Canada	CA	2025	Versatiles	ca_versatiles-2021A000011124_d4c-datapkg-orthoimagery_2025-08-10_satellite_v0.1.0-beta	Coming Soon
Canada	CA	2020	NRCan	ca_nrcan-2021A000011124_d4c-datapkg-orthoimagery_2020_30m_v0.1.0-beta	Coming Soon
Edmonton	CA-AB	2023	Edmonton	ca-ab_edmonton-2023A00054811061_d4c-datapkg-orthoimagery_2023_075mm_v0.1.0-beta	Coming Soon
Red Deer	CA-AB	2024	Red Deer	ca-ab_red-deer-2024A00054808011_d4c-datapkg-orthoimagery_2024_075mm_v0.1.0-beta	Coming Soon
Red Deer	CA-AB	2025	Red Deer	ca-ab_red-deer-2025A00054808011_d4c-datapkg-orthoimagery_2025_075mm_v0.1.0-beta	Coming Soon
Burnaby	CA-BC	2020	Burnaby	ca-bc_burnaby-2020A00055915025_d4c-datapkg-orthoimagery_2020_075mm_v0.1.0-beta	Coming Soon
Vancouver	CA-BC	2022	Vancouver	ca-bc_vancouver-2022A00055915022_d4c-datapkg-orthoimagery_2022_075mm_v0.1.0-beta	Coming Soon
Winnipeg	CA-MB	2024	Winnipeg	ca-mb_winnipeg-2024A00054611040_d4c-datapkg-orthoimagery_2024_075mm_v0.1.0-beta	Coming Soon
Whitehorse	CA-YT	2019	Whitehorse	ca-yt_whitehorse-2019A000556001009_d4c-datapkg-orthoimagery_2019_200mm_v0.1.0-beta	Coming Soon
Ontario	CA-ON	2024	Geospatial Ontario	ca-on_geospatial-ontario-2024A000235_d4c-datapkg-orthoimagery_2024_16cm_v0.0.1-beta	Coming Soon

The Plan

The objective is to standardize the source datasets used by Data for Canada processes. For instance, Vancouver’s orthoimagery is currently distributed in proprietary formats (MrSID and ECW) that require specialized drivers. We are currently evaluating open alternatives, such as Cloud Optimized GeoTIFFs, while ensuring that the conversion process preserves full visual fidelity. Future iterations will also explore the integration of multispectral data.