mirror of
https://github.com/bellingcat/RS4OSINT.git
synced 2026-06-08 03:28:36 +03:00
test
This commit is contained in:
Ollie Ballinger
@@ -34,22 +34,6 @@ Below is an Earth Engine application that automates the detection of makeshift r
|
||||
|
||||
You can draw an Area of Interest (AOI) and get the total number of contaminated points as well as the total number of contaminated square meters within the AOI. drawing multiple AOIs will show a running total of these statistics. It's not perfect-- it misses some refineries and falsely identifies some others-- but it is generally quite accurate; you can visually verify the results of the prediction by zooming in using the "+" button. You can toggle different layers using the "layers" tab as well. This tool could be used to get an estimate of oil production in a user-defined area, and eventually to direct cleanup efforts. The fullscreen version of the application can be found [here](https://ollielballinger.users.earthengine.app/view/rojavaoil), and the source code [here](https://code.earthengine.google.com/7a80f10412e1eb2a4d2c5d95989e70bd). This tutorial will first cover the basics of multispectral remote sensing, before moving into a step-by-step guide in the construction of this model.
|
||||
|
||||
## Multispectral Remote Sensing
|
||||
|
||||
Different materials reflect light differently. An apple absorbs shorter wavelengths (e.g. blue and green), and reflects longer wavelengths (red). Our eyes use that information-- the color-- to distinguish between different objects. But our eyes can only see a relatively small sliver of the electromagnetic spectrum covering blue, yellow, and red; we can't see UV or infrared wavelengths, for example, though the extent to which different materials reflect or absorb these wavelengths is just as useful for distinguishing between them. For example, Astroturf (fake plastic grass) and real grass will both look green to us, espeically from a satellite image. But living plants absorb radiation from the sun in a part of the light spectrum that we can't see. There's a spectral index called the Normalized Difference Vegetation Index (NDVI) which exploits this fact to isolate vegetation in multispectral satellite imagery. So if we look at [Gilette Stadium](https://en.wikipedia.org/wiki/Gillette_Stadium) near Boston, we can tell that the three training fields south of the stadium are real grass (they generate high NDVI values, showing up red), while the pitch in the stadium itself is astroturf (generating low NDVI values, showing up blue).
|
||||
|
||||
|
||||
|
||||
In other words, even though these fields are all green and indistinguishable to the human eye, their *spectral profiles* beyond the visible light spectrum differ, and we can use this information to distinguish between them. Below is a plot of the spectral profiles of different materials, including oil.
|
||||
|
||||
<iframe title="Spectral Profiles of Different Materials" aria-label="Interactive line chart" id="datawrapper-chart-b1kcX" src="https://datawrapper.dwcdn.net/b1kcX/3/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="400"></iframe><script type="text/javascript">!function(){"use strict";window.addEventListener("message",(function(e){if(void 0!==e.data["datawrapper-height"]){var t=document.querySelectorAll("iframe");for(var a in e.data["datawrapper-height"])for(var r=0;r<t.length;r++){if(t[r].contentWindow===e.source)t[r].style.height=e.data["datawrapper-height"][a]+"px"}}}))}();
|
||||
</script>
|
||||
|
||||
The European Space Agency's Sentinel-2 satellite collects spectral information well beyond the visible light spectrum, enabling this sort of analysis. It chops the electromagnetic spectrum up into "bands", and measures how strongly wavelengths in each of those bands is reflected:
|
||||
|
||||
|
||||
|
||||
We'll be using this satellite to distinguish between oil and other materials, similar to the way we were able to distinguish between real and fake grass at Gilette Stadium. First, we'll have to do a bit of pre-processing on the Sentinel-2 imagery after which we'll train a machine learning model to identify oil.
|
||||
|
||||
# Machine Learning Workflow
|
||||
|
||||
|
||||
@@ -261,7 +261,8 @@ ul.task-list li input[type="checkbox"] {
|
||||
|
||||
</header>
|
||||
|
||||
<p>One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, <a href="https://developers.google.com/earth-engine/datasets">all in one place</a>. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics. This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are <strong>not</strong> an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section.</p>
|
||||
<p>One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, <a href="https://developers.google.com/earth-engine/datasets">all in one place</a>. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics.</p>
|
||||
<p>This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are <strong>not</strong> an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section.</p>
|
||||
<section id="optical-imagery" class="level2" data-number="1.1">
|
||||
<h2 data-number="1.1" class="anchored" data-anchor-id="optical-imagery"><span class="header-section-number">1.1</span> Optical Imagery</h2>
|
||||
<div class="quarto-figure quarto-figure-center">
|
||||
|
||||
@@ -38,11 +38,11 @@ ul.task-list li input[type="checkbox"] {
|
||||
<script src="site_libs/quarto-html/anchor.min.js"></script>
|
||||
<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
|
||||
<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" class="quarto-color-scheme" id="quarto-text-highlighting-styles">
|
||||
<link href="site_libs/quarto-html/quarto-syntax-highlighting-dark.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-text-highlighting-styles">
|
||||
<link href="site_libs/quarto-html/quarto-syntax-highlighting-dark.css" rel="stylesheet" class="quarto-color-scheme quarto-color-alternate" id="quarto-text-highlighting-styles">
|
||||
<script src="site_libs/bootstrap/bootstrap.min.js"></script>
|
||||
<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
|
||||
<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" class="quarto-color-scheme" id="quarto-bootstrap" data-mode="light">
|
||||
<link href="site_libs/bootstrap/bootstrap-dark.min.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-bootstrap" data-mode="dark">
|
||||
<link href="site_libs/bootstrap/bootstrap-dark.min.css" rel="stylesheet" class="quarto-color-scheme quarto-color-alternate" id="quarto-bootstrap" data-mode="dark">
|
||||
<script id="quarto-search-options" type="application/json">{
|
||||
"location": "sidebar",
|
||||
"copy-button": false,
|
||||
@@ -85,7 +85,7 @@ ul.task-list li input[type="checkbox"] {
|
||||
<nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
|
||||
<div class="pt-lg-2 mt-2 text-left sidebar-header sidebar-header-stacked">
|
||||
<a href="./" class="sidebar-logo-link">
|
||||
<img src="./logo_black.png" alt="" class="sidebar-logo py-0 d-lg-inline d-none">
|
||||
<img src="./logo_white.png" alt="" class="sidebar-logo py-0 d-lg-inline d-none">
|
||||
</a>
|
||||
<div class="sidebar-title mb-0 py-0">
|
||||
<a href="./">Google Earth Engine for OSINT</a>
|
||||
@@ -134,7 +134,7 @@ ul.task-list li input[type="checkbox"] {
|
||||
<ul class="list-unstyled mt-1">
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./index.html" class="sidebar-item-text sidebar-link">Preface</a>
|
||||
<a href="./index.html" class="sidebar-item-text sidebar-link">Introduction</a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
@@ -161,6 +161,16 @@ ul.task-list li input[type="checkbox"] {
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./ch5.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span> <span class="chapter-title">Case Studies</span></a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./SyriaNTL.html" class="sidebar-item-text sidebar-link">War at Night</a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./RojavaRefineries.html" class="sidebar-item-text sidebar-link">Refinery Detection</a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
@@ -306,7 +316,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
|
||||
return localAlternateSentinel;
|
||||
}
|
||||
}
|
||||
let localAlternateSentinel = 'default';
|
||||
let localAlternateSentinel = 'alternate';
|
||||
// Dark / light mode switch
|
||||
window.quartoToggleColorScheme = () => {
|
||||
// Read the current dark / light value
|
||||
|
||||
@@ -166,6 +166,11 @@ ul.task-list li input[type="checkbox"] {
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./SyriaNTL.html" class="sidebar-item-text sidebar-link">War at Night</a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
<a href="./RojavaRefineries.html" class="sidebar-item-text sidebar-link">Refinery Detection</a>
|
||||
</div>
|
||||
</li>
|
||||
<li class="sidebar-item">
|
||||
<div class="sidebar-item-container">
|
||||
|
||||
@@ -32,7 +32,7 @@
|
||||
"href": "ch1.html",
|
||||
"title": "1 Data Acquisition",
|
||||
"section": "",
|
||||
"text": "One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, all in one place. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics. This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are not an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section."
|
||||
"text": "One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, all in one place. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics.\nThis section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are not an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section."
|
||||
},
|
||||
{
|
||||
"objectID": "ch1.html#osint-relevant-data-sets",
|
||||
|
||||
4
ch1.qmd
4
ch1.qmd
@@ -1,6 +1,8 @@
|
||||
# Data Acquisition
|
||||
|
||||
One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, [all in one place](https://developers.google.com/earth-engine/datasets). Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics. This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are **not** an exhaustive list-- there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn't hosted in the GEE catalog, you can upload your own data. We'll cover that in the next section.
|
||||
One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, [all in one place](https://developers.google.com/earth-engine/datasets). Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics.
|
||||
|
||||
This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are **not** an exhaustive list-- there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn't hosted in the GEE catalog, you can upload your own data. We'll cover that in the next section.
|
||||
|
||||
|
||||
## Optical Imagery
|
||||
|
||||
Reference in New Issue
Block a user