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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 It is mostly for learning purposes as some of the libraries listed are wip/outdated/not maintained anymore.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

Market-Based Valuation of Equity Options

CQF Lecture, 09. April 2018, London

Dr. Yves J. Hilpisch, The Python Quants GmbH

Resources

	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."

	import operator
	import re

	def pairs(l):
	return zip(l[::2], l[1::2])

	def vm(ops, env={}):
	class closure:
	def __init__(self, pc, env): self.pc, self.env = pc, env
	def popn(n):