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How I do machine learning with geospatial data

I have a couple of AI/ML projects related to mapping things, often conservation related, with remote sensing data. Some details and packages will vary, but the process below describes how I generally approach these types of problems. Some of these tools I have only touched briefly, but I like them, and this is more an outline of how I would like to approach a new project than a retrospective look at my previous work.

We use AWS, so it makes sense to use datasets and services that are already hosted on AWS. The data discovery and loading part of this process would look somewhat different if we were using Azure and Planetary Computer, and very different if we were using GCP and Earth Engine.

Compute

All of my analysis will be done using python on an AWS VM in the same region as my data on S3, Probably using VSCode on Sagemaker or [JupyterLab](https://docs.aws.amazo

So you want to write a sync system for a web app with offline and realtime support? Good luck. You might find the following resources useful.

Overview articles

Database in a browser, a spec (Stepan Parunashvili)

What problem are we trying to solve with a sync system?
The web of tomorrow (Nikita Prokopov)

A non-exhaustive list of WebGL and WebGPU frameworks and libraries. It is mostly for learning purposes as some of the libraries listed are wip/outdated/not maintained anymore.

Engines and libraries ⚙️

Name	Stars	Last Commit	Description
three.js	![GitHub

	import Foundation


	final class FileIO {
	private let buffer:[UInt8]
	private var index: Int = 0

	init(fileHandle: FileHandle = FileHandle.standardInput) {

	buffer = Array(try! fileHandle.readToEnd()!)+[UInt8(0)] // 인덱스 범위 넘어가는 것 방지

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	Function Get-LocalGroupMembers
	{
	param(
	[Parameter(ValuefromPipeline=$true)][array]$server = $env:computername,
	$GroupName = $null
	)
	PROCESS {
	$finalresult = @()
	$computer = [ADSI]"WinNT://$server"

	#!/usr/bin/env bash
	function p() {
	jq -n \
	--arg content "$*" \
	'{
	"model": "pplx-7b-online",
	"messages": [
	{
	"role": "system",
	"content": "Be precise and concise."