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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

	#!/bin/bash

	TMP_DIR="/tmp"
	URL_LINKS=$"http://www.blocklist.de/lists/ssh.txt
	http://www.blocklist.de/lists/apache.txt
	http://www.blocklist.de/lists/asterisk.txt
	http://www.blocklist.de/lists/bots.txt
	http://www.blocklist.de/lists/courierimap.txt
	http://www.blocklist.de/lists/courierpop3.txt
	http://www.blocklist.de/lists/email.txt

	// IDC script for identifying functions and such
	#include <idc.idc>

	#define RenameFunction(a, b) Message("Found %s at %a\r\n", b, a); MakeName(a, b)
	#define START_ADDR 0x00400000

	static main() {
	auto addr = BADADDR;

	auto onMovePacket = FindAoBAndRename("25 0C FE FF FF 05 E8 03", "?OnMovePacket@CMovePath@@QAEXAAVCInPacket@@H@Z");

	[TeamSpeak3]
	title=TeamSpeak3
	description=TeamSpeak3 Server
	ports=9987/udp\|10011,30033/tcp

	! model
	pc101 Generic 101-key PC
	pc102 Generic 102-key (Intl) PC
	pc104 Generic 104-key PC
	pc105 Generic 105-key (Intl) PC
	dell101 Dell 101-key PC
	latitude Dell Latitude series laptop
	dellm65 Dell Precision M65
	everex Everex STEPnote
	flexpro Keytronic FlexPro

	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