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OSINT: Locating Hidden Security Cameras with Overpass Turbo
Welcome back, aspiring cyberwarriors!
In the reconnaissance phase of any security engagement, information gathering is crucial. Previously, we discussed using Google Earth Pro for investigations. Today, let’s shift our focus from satellite OSINT to map‑based reconnaissance. Many of you are already familiar with Google Maps and its alternatives, such as OpenStreetMap (OSM). But did you know that you can easily extract specific data from OpenStreetMap, like security cameras or Wi‑Fi hotspots, using a tool called Overpass Turbo?
Let’s explore in this article how to leverage this powerful reconnaissance tool.
Step #1: Understanding Overpass Turbo Basics
Overpass Turbo is accessible at https://overpass-turbo.eu and requires no installation or registration. It provides a web-based interface for querying the Overpass API, which is OpenStreetMap’s data extraction engine.
The interface consists of three main components:
Query Editor (left side): Where you write your queries using the Overpass Query Language (QL)
Interactive Map (right side): Displays your query results geographically
Toolbar (top): Contains the Run button, Wizard, Export options, and settings
When you first access Overpass Turbo, you’ll see a default query loaded in the editor. The map displays the current viewport, which you can pan and zoom to focus on your area of interest.
The Query Wizard
For beginners, the Wizard tool (accessible from the toolbar) provides a simplified interface. You can enter search terms in plain English, and the Wizard converts them into proper Overpass QL syntax. For example:
Type: amenity=atm in London
Click “build and run query”.
The Wizard generates the appropriate query syntax and executes it automatically.
As a result, we can see a map of ATMs in London.
Step #2: Writing Overpass Queries
Overpass Query Language follows a specific structure. Let’s break down the anatomy of our query built by a wizard:
[out:json][timeout:25];
// fetch area “London” to search in
{{geocodeArea:London}}->.searchArea;
// gather results
nwr["amenity"="atm"](area.searchArea);
// print results
out geom;It already includes comments, but for better understanding, let’s dive a bit deeper.
[out:json][timeout:25] – Sets the output format to JSON and limits the server-side execution time to 25 seconds.
{{geocodeArea:London}}→.searchArea; – A macro that resolves the administrative boundary of London (its OSM relation). The result is stored in a temporary set named .searchArea for later reference.
nwr["amenity"="atm"](area.searchArea); – nwr stands for nodes, ways, and relations.
OpenStreetMap uses three element types: nodes, which represent single-point locations such as cameras or Wi-Fi access points; ways, which represent lines and closed shapes such as roads or building outlines; and relations, which group nodes and ways together to represent features such as building complexes or campuses.
The filter ["amenity"="atm"] selects all OSM elements tagged as ATMs. (area.searchArea) restricts the search to the previously defined London area.
out geom; – Outputs the matching elements, including their full geometry (geom) – points with latitude/longitude, ways with their node lists, and relations with their member geometries.
Tag Filters
The core of your reconnaissance queries are the tag filters. Tags in OSM follow a key=value structure.
node["key"="value"]
By opening the page at https://wiki.openstreetmap.org/wiki/Map_features
you can view a comprehensive list of possible keys and values. From a hacker’s perspective, you can examine the man_made key to discover surveillance‑related options.
Now, let’s edit out query and try to find out surveillance cameras in California.
[out:json][timeout:25];
{{geocodeArea:California}}->.searchArea;
nwr["surveillance"="camera"](area.searchArea);
out geom;
Now, let’s try to find data centers in Moscow.
[out:json][timeout:25];
{{geocodeArea:Moscow}}->.searchArea;
nwr["building"="data_center"](area.searchArea);
out geom;
Summary
OpenStreetMap data helps companies and independent researchers work more efficiently. And Overpass Turbo simplifies tasks such as tracking urban growth and analyzing surveillance patterns. OSINT investigators and cyberwarriors can also use it to extract precise information from OpenStreetMap’s extensive geographic database.
If you’d like to advance in OSINT, consider checking out our OSINT training class.
The post OSINT: Locating Hidden Security Cameras with Overpass Turbo first appeared on Hackers Arise.
