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TIFF compression options

Summary

Realistically, especially when considering the inherent noise in the original image, I'd settle for lossy compression with COMPRESS=JPEG JPEG_QUALITY=90, which could reduce file size fairly quickly to 16% of the original.

But if lossless compression is a hard requirement, I'd probably go with COMPRESS=LZW PREDICTOR=2. However, I would want to verify that any downstream tools wolud support this sort of compression.

UPDATE: When saving with JPEG compression, further speed and size improvements are gained by adding PHOTOMETRIC=YCBCR, which uses a different color space that has even better compression. I've added new rows to the table for YCBCR, as well as .jp2 formats.

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Libraries for a modern geospatial workflow

Distribution

• Writing, Running, and Distributing
• Tox driven development
• Publishing python libraries

Numpy

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#!/bin/bash

GRAPH_NAME=tlehman

function roam_api_get_blocks_containing_string() {
	curl -X POST "https://api.roamresearch.com/api/graph/$GRAPH_NAME/q" --location-trusted \
		-H  "accept: application/json" \
		-H  "Authorization: Bearer $ROAM_API_KEY" \
		-H  "Content-Type: application/json" \
		-d  "{\"query\" : \"[:find ?block-str :in \$ ?search-string :where [?b :block/uid ?block-uid] [?b :block/string ?block-str] [(clojure.string/includes? ?block-str ?search-string)]]\", \"args\": [\"$1\"]}"

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Based on:

• https://gist.github.com/mattiaslundberg/8620837
• https://wiki.archlinux.org/index.php/Installation_guide
• http://www.muktware.io/arch-linux-guide-the-always-up-to-date-arch-linux-tutorial/ (Link is broken)

There are 2 choices:

• UEFI/GPT mode: UEFI boot mode / GPT partition table
• BIOS/MBR mode: Legacy boot mode / MBR partition table

I tried to install in UEFI mode, but my laptop (Acer E5-475-336H) had problems with it, and didn't boot after installation.

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# *** WARNING ***
# This gist is no longer maintained
# It has been replaced by aws-assume-role-lib in PyPI
# Documentation at https://github.com/benkehoe/aws-assume-role-lib
# It is still a single-file library, you can find the stable version here:
# https://raw.githubusercontent.com/benkehoe/aws-assume-role-lib/stable/aws_assume_role_lib/aws_assume_role_lib.py
# (link also available in the docs)

# Copyright 2020 Ben Kehoe
#

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/*
 * credit to Dhrumil Shah (@wandcrafting) and Robert Haisfield (@RobertHaisfield)
 * for the original concept which was part of their RoamGames submission
 * and can be found at: https://www.figma.com/file/5shwLdUCHxSaPNEO7pazbe/
 *
 */

/* ======= OPTIONS ======== */
/* note: if you change these, reload the page to see the effect */

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• Zoom
  • CMD 0 100%
  • CMD 1 Entire project
  • CMD 2 Zoom to selected object
• Hide/Show Sidebars
  • CMD ALT 1 Hide left sidebar
  • CMD ALT 2 Hide right ridebar
  • CMD ALT 3 Hide both
  • CMD . Presentation mode
• CTRL R Ruler

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Important

DO NOT USE VSCODE terminal and make sure uname -m returns arm64

1. First Install Xcode command line tool

xcode-select --install

2. Create and change /opt directories owner

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Hi, and welcome!

We love pixels. We build our tools such that they allow us to work with pixels at a wide range of scales, from individual landsat scenes, to global mosaics, via command line programs or as python libraries we can integrate into large processing pipelines.

Today we'll walk through:

• processing an individual Landsat scene using rasterio, a tool which serves as the foundation for most of our work
• uploading the outputs of ^ to Mapbox, where we can visualize it on top of mapbox.satellite, and serve it globally at crazy speed