bellingcat/RS4OSINT
1
0
Fork 0
mirror of https://github.com/bellingcat/RS4OSINT.git synced 2026-06-08 03:28:36 +03:00
Code Issues Packages Projects Releases Wiki Activity

fixing image links

Browse Source
This commit is contained in:
Ollie Ballinger
2022-12-07 11:34:53 +00:00
parent 11b1ebbf15
commit ca3d32a828
12 changed files with 17 additions and 1174 deletions
Download Patch File Download Diff File Expand all files Collapse all files

79
docs/search.json
View File

@@ -1,73 +1,24 @@
[
{
"objectID": "ch2.html#optical-imagery",
"href": "ch2.html#optical-imagery",
"title": "2  Data Acquisition",
"section": "2.1 Optical Imagery",
"text": "2.1 Optical Imagery\n\n\n\nSentinel-2 timelapse showing the ancient city of Hasankeyf being flooded following the construction of a dam by the Turkish government.\n\n\nOptical satellite imagery is the bread and butter of many open source investiagtions. It would be tough to list off all of the possible use cases, so here’s a handy flowchart:\n\n\n\n\n%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#FFFFFF' ,'primaryBorderColor':'#000000' , 'lineColor':'#009933'}}}%%\n\nflowchart\n A(Does it happen outside?) \n A--> B(Yes)\n A--> C(No)\n D(Is it very small?)\n B-->D\n E(Yes)\n F(No)\n D-->F\n D-->E\nG(Use optical satellite imagery)\nH(Don't use optical satellite imagery)\nE-->H\nF-->G\nC-->H\n\n\n\n\n\n\n\n\nThis is, of course, a bit of an exaggeration. But if you’re interested in a visible phenomenon that happens outdoors and that isn’t very tiny, chances are an earth-observing satellite has taken a picture of it. What that picture can tell you naturally depends on what you’re interested in learning. For a deeper dive into analyzing optical satellite imagery, see the subsection on multispectral remote sensing..\nThere are several different types of optical satellite imagery available in the GEE catalogue. The main collections are the Landsat and Sentinel series of satellites, which are operated by NASA and the European Space Agency, respectively. Landsat satellites have been in orbit since 1972, and Sentinel satellites have been in orbit since 2015. Norway’s International Climate and Forest Initiative (NICFI) has also contributed to the GEE catalogue by providing a collection of optical imagery from Planet’s PlanetScope satellites. These are higher resolution (4.7 meters per pixel) than Landsat (30m/px) and Sentinel-2 (10m/px), but are only available for the tropics. Even higher resolution imagery (60cm/px) is available from the GEE catalogue from the National Agriculture Imagery Program, but it is only available for the United States. For more details, see the “Datasets” section below.\n\nApplications\n\nGeolocating pictures\n\nSome of Bellingcat’s earliest work involved figuring out where a picture was taken by cross-referencing it with optical satellite imagery.\n\nGeneral surveillance\n\nMonitoring Chinese missile silo construction.\nAmassing evidence of genocide in Bucha, Ukraine\n\nDamage detection\n\nUkraine\nMali\nAround the World\n\nVerifying the locations of artillery/missile/drone strikes\n\nThe 2019 attack on Saudi Arabia’s Abqaiq oil processing facility.\n\nMonitoring illegal mining/logging\n\nGlobal Witness investigation into illegal mining by militias in Myanmar.\nTracking illegal logging across the world.\n\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nLandsat 1-5\n1972–1999\n30m\nGlobal\n\n\nLandsat 7\n1999–2021\n30m\nGlobal\n\n\nLandsat 8\n2013–Present\n30m\nGlobal\n\n\nLandsat 9\n2021–Present\n30m\nGlobal\n\n\nSentinel-2\n2015–Present\n10m\nGlobal\n\n\nNICFI\n2015-Present\n4.7m\nTropics\n\n\nNAIP\n2002-2021\n0.6m\nUSA"
"objectID": "index.html",
"href": "index.html",
"title": "Google Earth Engine for OSINT",
"section": "",
"text": "Introduction\nThe analysis of satellite imagery is a foundational element of open source investigations. In the past decade, the quantity, quality, and availability thereof has increased dramatically. Capabilities and insights that were once only available to governments are now accessible to the general public. Satellite imagery is being used to collect evidence of genocide and other war crimes in Ukraine, Nigeria, Burundi, Cameroon, the DRC, South Sudan, Papua, and Venezuela. It has been used to monitor environmental degradation and hold extractive industries to account from Iraq to Guatemala. The ability to analyze satellite imagery is a critical skill for anyone interested in open source investigations.\nThough no-code platforms such as Sentinelhub have been invaluable in allowing the OSINT community to access and process satellite imagery, the analytical capabilities of these platforms are limited. Google Earth Engine (GEE) is a cloud-based platform that stores petabytes of satellite imagery from a variety of sources and allows users to perform advanced analyses on Google servers for free using a browser-based interface. This textbook is designed for investigators who want to perform more sophisticated analysis using geospatial data, and assumes no prior knowledge of coding or remote sensing (satellite imagery analysis). It is organized into two parts– an introduction to remote sensing and GEE, and a series of case studies that demonstrate how to use GEE for open source investigations:"
},
{
"objectID": "ch2.html#radar-imagery",
"href": "ch2.html#radar-imagery",
"title": "2  Data Acquisition",
"section": "2.2 Radar Imagery",
"text": "2.2 Radar Imagery\n\n\n\nShips and interference from a radar system are visible in Zhuanghe Wan, near North Korea.\n\n\nSynthetic Aperture Radar imagery (SAR) is a type of remote sensing that uses radio waves to detect objects on the ground. SAR imagery is useful for detecting objects that are small, or that are obscured by clouds or other weather phenomena. SAR imagery is also useful for detecting objects that are moving, such as ships or cars.\n\nApplications\n\nChange/Damage detection\nTracking military radar systems\nMaritime surveillance\nMonitoring illegal mining/logging\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nSentinel 1\n2014-Present\n10m\nGlobal"
"objectID": "index.html#table-of-contents",
"href": "index.html#table-of-contents",
"title": "Google Earth Engine for OSINT",
"section": "Table of Contents",
"text": "Table of Contents\n\nLearning\n\nRemote Sensing\nData Acquisition\nAlgorithms\nApplication Development\n\nCase Studies\n\nWar at Night\nRefinery Detection"
},
{
"objectID": "ch2.html#nighttime-lights",
"href": "ch2.html#nighttime-lights",
"title": "2  Data Acquisition",
"section": "2.3 Nighttime Lights",
"text": "2.3 Nighttime Lights\n\n\n\nA timelapse of nighttime lights over Northern Iraq showing the capture and liberation of Mosul by ISIS.\n\n\nSatellite images of the Earth at night a useful proxy for human activity. The brightness of a given area at night is a function of the number of people living there and the nature of their activities. The effects of conflict, natural disasters, and economic development can all be inferred from changes in nighttime lights.\nThe timelapse above reveals a number of interesting things: The capture of Mosul by ISIS in 2014 and the destruction of its infrastructure during the fighting (shown as the city darkening), as well as the liberation of the city by the Iraqi military in 2017 are all visible in nighttime lights. The code to create this gif, as well as a more in-depth tutorial on the uses of nighttime lights, can be found in the “War at Night” case study.\n\nApplications\n\nDamage detection\nIdentifying gas flaring/oil production\nIdentifying urban areas/military bases illuminated at night\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nDMSP-OLS\n1992-2014\n927m\nGlobal\n\n\nVIIRS\n2014-Present\n463m\nGlobal"
},
{
"objectID": "ch2.html#climate-and-atmospheric-data",
"href": "ch2.html#climate-and-atmospheric-data",
"title": "2  Data Acquisition",
"section": "2.4 Climate and Atmospheric Data",
"text": "2.4 Climate and Atmospheric Data\n\n\n\nSulphur Dioxide plume resulting from ISIS attack on the Al-Mishraq Sulphur Plant in Iraq\n\n\nClimate and atmospheric data can be used to track the effects of conflict on the environment. The European Space Agency’s Sentinel-5p satellites measure the concentration of a number of atmospheric gases, including nitrogen dioxide, methane, and ozone. Measurements are available on a daily basis at a fairly high resolution (1km), allowing for the detection of localized sources of pollution such as oil refineries or power plants. For example, see this Bellingcat article in which Wim Zwijnenburg and I trace pollution to specific facilities operated by multinational oil companies in Iraq.\nThe Copernicus Atmosphere Monitoring Service (CAMS) provides similar data at a lower spatial resolution (45km), but measurements are avaialble on an hourly basis. The timelapse above utilizes CAMS data to show a sulphur dioxide plume resulting from an ISIS attack on the Al-Mishraq Sulphur Plant in Iraq. The plant was used to produce sulphuric acid, for use in fertilizers and pesticides. The attack destroyed the plant, causing a fire which burned for a month and released 21 kilotons of sulphur dioxide into the atmosphere per day; the largest human-made release of sulphur dioxide in history.\n\nApplications\n\nMonitoring of airborne pollution\nTracing pollution back to specific facilities and companies\nVisualizing the effects of one-off environmental catastrophes\n\nNordstream 1 leak\nISIS setting Mishraq sulphur plant on fire\n\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nCAMS NRT\n2016-Present\n44528m\nGlobal\n\n\nSentinel-5p\n2018-Present\n1113m\nGlobal"
},
{
"objectID": "ch2.html#mineral-deposits",
"href": "ch2.html#mineral-deposits",
"title": "2  Data Acquisition",
"section": "2.5 Mineral Deposits",
"text": "2.5 Mineral Deposits\n\n\n\nZinc deposits across Central Africa\n\n\nMining activities often play an important role in conflict. According to an influential study, “the historical rise in mineral prices might explain up to one-fourth of the average level of violence across African countries” between 1997 and 2010. Data on the location of mineral deposits can be used to identify areas where mining activities are likely to be taking place, and several such datasets are available in Google Earth Engine.\n\nApplications\n\nMonitoring mining activity\nIdentifying areas where mining activities are likely to be taking place\nMapping the distribution of resources in rebel held areas in conflicts fueled by resource extraction\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\niSDA\n2001-2017\n30m\nAfrica"
},
{
"objectID": "ch2.html#fires",
"href": "ch2.html#fires",
"title": "2  Data Acquisition",
"section": "2.6 Fires",
"text": "2.6 Fires\n\n\n\nDetected fires over Ukraine since 27/02/2022 showing the frontline of the war\n\n\nEarth-observing satellites can detect “thermal anomalies” (fires) from space. NASA’s Fire Information for Resource Management System (FIRMS) provides daily data on active fires in near real time, going back to the year 2000. Carlos Gonzales wrote a comprehensive Bellingcat article on the use of FIRMS to monitor war zones from Ukraine to Ethiopia. The map above shows that FIRMS detected fires over Eastern Ukraine trace the frontline of the war.\nFIRMS data are derived from the MODIS satellite, but only show the central location and intensity of a detected fire. Another MODIS product (linked in the table below) generates a monthly map of burned areas, which can be used to assess the spatial extent of fires.\n\nApplications\n\nIdentification of possible artillery strikes/fighting in places like Ukraine\nEnvironmental warfare and “scorched earth” policies\nLarge scale arson\n\ne.g. Refugee camps burned down in Myanmar\n\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nFIRMS\n2000-Present\n1000m\nGlobal\n\n\nMODIS Burned Area\n2000-Present\n500m\nGlobal"
},
{
"objectID": "ch2.html#population-density-estimates",
"href": "ch2.html#population-density-estimates",
"title": "2  Data Acquisition",
"section": "2.7 Population Density Estimates",
"text": "2.7 Population Density Estimates\n\n\n\nPopulation density estimates around Pyongyang, North Korea\n\n\nSometimes, we may want to get an estimate the population in a specific area to ballpark how many people might be affected by a natural disaster, a counteroffensive, or a missile strike. You can’t really google “what is the population in this rectangle i’ve drawn in Northeastern Syria?” and get a good answer. Luckily, there are several spatial population datasets hosted in GEE that let you do just that. Some, such as WorldPop, provide estimated breakdowns by age and sex as well. However, it is extremely important to bear in mind that these are estimates, and will not take into account things like conflict-induced displacement. For example, Oak Ridge National Laboratory’s LandScan program has released high-resolution population data for Ukraine, but this pertains to the pre-war population distribution. The war has radically changed this distribution, so these estimates no longer reflect where people are. Still, this dataset could be used to roughly estimate displacement or the number of people who will need new housing.\n\nApplications:\n\nRough estimates of civilians at risk from conflict or disaster, provided at a high spatial resolution\n\n\n\nDatasets\n\n\n\nSensor\nTimeframe\nResolution\nCoverage\n\n\n\n\nWorldpop\n2000-2021\n92m\nGlobal\n\n\nGPW\n2000-2021\n927m\nGlobal\n\n\nLandScan\n2013–Present\n100m\nUkraine"
},
{
"objectID": "ch2.html#building-footprints",
"href": "ch2.html#building-footprints",
"title": "2  Data Acquisition",
"section": "2.8 Building Footprints",
"text": "2.8 Building Footprints\n\n\n\nBuilding footprints in Mariupol, Ukraine colored by whether the building is damaged\n\n\nA building footprint dataset contains the two dimensional outlines of buildings in a given area. Currently, GEE hosts one building footprint dataset which covers all of Africa. In 2022, Microsoft released a free global building footprint dataset, though to use it in Earth Engine you’ll have to download it from their GitHub page and upload it manually to GEE. The same goes for OpenStreetMap (OSM), a public database of building footprints, roads, and other features that also contains useful annotations for many buildings indicating their use. Benjamin Strick has a great youtube video on conducting investigations using OSM data.\n\nApplications:\n\nJoining damage estimate data with the number of buildings in an area\n\n\n\nDatasets\n\n\n\nDataset\nTimeframe\nCoverage\n\n\n\n\nOpen Buildings\n2022\nAfrica"
},
{
"objectID": "ch2.html#administrative-boundaries",
"href": "ch2.html#administrative-boundaries",
"title": "2  Data Acquisition",
"section": "2.9 Administrative Boundaries",
"text": "2.9 Administrative Boundaries\n\n\n\nSecond-level administrative boundaries in Yemen\n\n\nSpatial analysis often have to aggregate information over a defined area; we may want to assess the total burned area by province in Ukraine, or count the number of Saudi airstrikes by district in Yemen. For that, we need data on these administrative boundaries. GEE hosts several such datasets at the country, province, and district (or equivalent) level.\n\nApplications\n\nQuick spatial calculations for different provinces/districts in a country\n\ne.g. counts of conflict events by district over time\n\n\n\n\nDatasets\n\n\n\nDataset\nTimeframe\nCoverage\n\n\n\n\nFAO GAUL\n2015\nGlobal"
},
{
"objectID": "ch2.html#global-power-plant-database",
"href": "ch2.html#global-power-plant-database",
"title": "2  Data Acquisition",
"section": "2.10 Global Power Plant Database",
"text": "2.10 Global Power Plant Database\n\n\n\nPower plants in Ukraine colored by type\n\n\nThe Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights. Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. As of June 2018, the database includes around 28,500 power plants from 164 countries. The database is curated by the World Resources Institude (WRI).\n\nApplications:\n\nAnalyzing the impact of conflict on critical infrastructure.\n\ne.g. fighting in Ukraine taking place around nuclear power facilities.\n\nCould be combined with the atmospheric measurements of different pollutants and the population estimates data to assess the impact of various forms of energy generation on air quality and public health.\n\n\n\nDatasets\n\n\n\nDataset\nTimeframe\nCoverage\n\n\n\n\nGPPD\n2018\nGlobal"
"objectID": "index.html#what-is-google-earth-engine",
"href": "index.html#what-is-google-earth-engine",
"title": "Google Earth Engine for OSINT",
"section": "What is Google Earth Engine?",
"text": "What is Google Earth Engine?\nAs geospatial datasets—particularly satellite imagery collections—increase in size, researchers are increasingly relying on cloud computing platforms such as Google Earth Engine (GEE) to analyze vast quantities of data.\nGEE is free and allows users to write open-source code that can be run by others in one click, thereby yielding fully reproducible results. These features have put GEE on the cutting edge of scientific research. The following plot visualizes the number of journal articles conducted using different geospatial analysis software platforms:\n\nDespite only being released in 2015, the number of geospatial journal articles using Google Earth Engine (shown in red above) has outpaced every other major geospatial analysis software, including ArcGIS, Python, and R in just five years. By storing and running computations on google servers, GEE is far more accessible to those who don’t have significant local computational resources; all you need is an internet connection.\nGEE applications have been developed and used to present interactive geospatial data visualizations by NGOs, Universities, the United Nations, and the European Commission."
},
{
"objectID": "ch3.html#getting-started",