How Does Google Maps Work? 20 Years of Mapping Innovation

Google Maps will celebrate its 20th anniversary in 2025. What started as a simple mapping project has become the most widely used location tool in the world — 1.6 billion monthly users use it to navigate, discover businesses, or check restaurant hours.

But how does Google Maps actually work? Where do the satellite images come from? How does the app calculate travel times with such precision? And why are some areas blurred while others are crystal clear?

This article breaks down the technical mechanisms behind the magic and shows how you can leverage this data for your business prospecting.

The Two Layers of Maps: The Technical Foundation of Google Maps

When you open Google Maps, you have access to two types of displays: the default map (drawn) and the satellite view (photographic). This duality is not a cosmetic detail — it is the fundamental architecture that makes Google Maps possible.

The Mapping Layer: Drawings and Symbols

The first layer is a stylized vector map: roads in gray, parks in green, water in blue. Google does not draw everything by hand. The app combines data from public sources (cadastre, local administrations) with its own surveys.

This layer allows for things that photos cannot do: - Display closed or under-construction roads - Highlight areas of heavy traffic - Show administrative boundaries (neighborhoods, municipalities) - Add icons for businesses, schools, hospitals

The Satellite Layer: Mosaic of Images

The second layer is a mosaic of satellite and aerial images. Look at the bottom right of the satellite view: you will see the credits. TerraMatrix, NASA, Landsat Copernicus, Airbus — these are not Google subsidiaries. They are external providers.

Google did not build its own constellation of satellites. That would be economically unrealistic. Instead, Google buys or rents images from specialized providers and then merges them with its own aerial data (collected by plane).

The technical challenge: these images come from different sources, taken at different times, with different weather conditions, and different camera settings. Google uses image processing algorithms to align them, correct colors, and create a coherent mosaic.

The Sources of Satellite Images: Who Really Provides the Photos?

Google Maps does not own the satellite images. This is crucial to understand.

The Major Providers

Source	Coverage	Type	Update
TerraMatrix	Global	Satellite	1-3 years
NASA	Global	Satellite	Monthly
Landsat Copernicus	Global	Satellite	16 days
Airbus Defense & Space	Selective	Aerial	1-2 years
Google Earth imagery	Selective	Aerial/Drone	Annual

TerraMatrix is the main provider for most regions. It is a private company that compiles images from several commercial satellites. The resolution depends on the satellite: between 1 and 10 meters per pixel depending on the region.

NASA provides freely accessible data via Landsat, a space program launched in 1972. These images have a lower resolution (30 meters per pixel) but cover the entire planet and are updated regularly.

Airbus Defense & Space provides the most recent and detailed images for certain urban areas — resolution up to 30 centimeters per pixel. But the cost is enormous, so Google only uses them for major cities.

Why Multiple Sources?

A single source would have gaps: clouds, uncovered areas, outdated images. By overlaying multiple sources, Google ensures global coverage and can choose the best available image for each region.

Concrete example: for Paris, Google likely uses an Airbus image from 2023 (very detailed) combined with a TerraMatrix image from 2022 for less critical areas. For a small town in Africa, Landsat dominates.

Google Street View: How Google Photographed the Entire Planet

Street View is the most ambitious project of Google Maps. Launched in 2007, it revolutionized mapping by making photogrammetry possible — creating a 3D image from 2D photos.

The Google Car: The Heart of Collection

You have probably encountered a Google Car on the street. It is a car equipped with:

• Omnidirectional camera (15 lenses) that captures 360°
• LIDAR sensors (laser) to measure distances and create a 3D map
• High-precision GPS to geolocate each photo
• Autonomous navigation system to drive on roads

The car drives slowly (about 40 km/h) capturing images every 2-3 meters. A day of collection = 100,000 images.

Adaptation According to Terrain: Beyond the Car

Google realized that the car could not access everywhere. Hence the innovation:

Backpack (Trekker): for pedestrian paths, parks, forests. Worn by a Google agent, it captures the same data as a car but in portable mode.

Bicycle: for bike paths and narrow streets. The sensor is mounted on the frame.

Snowmobile: for snowy regions (Scandinavia, Canada, Siberia).

Camel: yes, really. Google used a camel to photograph the sand dunes of the Moroccan desert.

Diver: for coral reefs. The sensor is waterproof and captures the underwater environment.

Astronaut: Google even has images from space (via ISS). This is orbital Street View.

Each method produces the same type of data: geolocated photos + LIDAR depth + metadata.

The Traffic Algorithm: How Google Predicts Your Routes

When you request a route on Google Maps, the app offers you several paths with time estimates. These estimates are not magical — they rely on a layering of massive data.

The Three Layers of Data

Layer 1: Real-Time Traffic

Google collects anonymized position data from billions of devices using Google Maps. Each user who agrees to location sharing sends their position every few seconds. Google aggregates this data to calculate the average speed on each road segment.

Result: you see roads in red (congestion), orange (slow), or green (smooth) in real-time.

Layer 2: Municipal and Transport Data

Google integrates official public transport data: metro, bus, train schedules, and live information on delays. Is a metro line closed for maintenance? RATP or SNCF data indicates this, and Google adds it to its calculation.

Layer 3: Collective Intelligence of Users

Google Maps sometimes asks you: "How many people are here right now?" These user reports (rare, optional) validate algorithmic data and refine predictions.

How Google Predicts the Future

AI comes into play here. Google has 20 years of historical data: for each road segment, it knows: - What is the typical traffic on a Monday at 8 AM? - What is the traffic on a Sunday at 2 PM? - Are there events (matches, concerts) that impact traffic? - How do school holidays change the flows?

When you request a route for tomorrow at 8 AM, Google uses these patterns to predict the likely traffic. The average error is 5-10% — impressive on the scale of an entire city.

Practical Case: Route to Orly on a Tuesday at 6 PM

Google Maps tells you: "1h 45min by car, 2h 30min by RER".

Here’s what happened in the background:

1. Real-time data: Google measured the current speed on each road between you and Orly.
2. Historical data: Google knows that a Tuesday at 6 PM is rush hour. The roads to Orly are saturated at 70%.
3. Transport data: the RER B has a departure every 5 minutes, but with an average wait of 5 minutes + 25 minutes of travel.
4. Optimal route calculation: Google tests several paths and chooses the one with the shortest estimated time.

The result: a reliable estimate that changes every 30 seconds as real conditions evolve.

Google Maps as a Local Search Engine: 200 Million Businesses

Beyond navigation, Google Maps is a business search engine — the largest business directory in the world.

Search by Category and Location

When you type "plumber Paris" into Google Maps, the app searches its database of 200 million businesses spread across 4000 categories. It returns the most relevant results based on:

• Proximity: the closest plumbers first
• Google rating: the highest rated rise to the top
• Number of reviews: more reviews = more weight
• Category relevance: the business must be classified as "plumber"
• Complete listing: a listing with photos, hours, website ranks higher than an empty listing

The Importance of User Data

Every review, every photo, every crowd report feeds the ranking algorithm. A restaurant with 500 reviews at 4.8 stars will appear before a restaurant with 20 reviews at 5 stars.

Google also values crowd times — these graphs that show when a place is crowded. This data comes from the anonymized location of users. No business has to fill them out manually.

Google Business Profile: How Businesses Take Back Control

If a business has not claimed its listing, it will see a mention "Claim this establishment". This is the signal that it does not have administrative access.

What Does Claiming Bring?

Once the business claims its Google Business Profile, it can:

• Update information: address, phone, website, hours
• Add photos: its own, not just user-submitted ones
• Respond to reviews: engage customers, show that you listen
• Add attributes: "Free parking", "Accessible for disabled", "Free WiFi"
• Create posts: announce a promotion, a new schedule
• View statistics: how many people found you, how many clicked on your site, how many called you

Impact on Ranking

A claimed and complete listing ranks 3 to 5 times higher than an empty listing. This is one of the reasons why the benefits of a Google My Business listing for a business are so significant.

The Staggering Numbers of Google Maps

To contextualize the scale:

• 1.6 billion monthly users access Google Maps
• 200 million businesses are listed
• 4000 categories cover all sectors
• Monthly updates: 5 million changes in information
• Monthly reviews: 100 million reviews added
• Monthly photos: 50 million photos uploaded by users

These figures illustrate why Google Maps has become the de facto business directory. If your business is not listed correctly, you are losing customers.

How to Leverage Google Maps Data for Your Prospecting

You now understand how Google Maps works. The next question: how to profit from it for your business?

Use Case 1: Classic Business Prospecting

You are a marketing agency targeting all restaurants in Lyon with less than 3 stars (potential clients for a rebranding). You need: - Name, address, phone, email - Google rating and number of reviews - Website (if it exists) - Specific categories

Manually retrieving 500 restaurants would take 20 hours. With a data extraction solution, it takes 5 minutes.

Use Case 2: Market Analysis

You are launching a coffee franchise and want to understand the saturation in your city. You need: - All cafes within a 2km radius - Their average rating and number of reviews - Their opening hours - Their reviews (to identify weaknesses: "too expensive", "no WiFi", "slow service")

Analyzing 150 reviews manually = 6 hours. Extracting the data and analyzing it = 30 minutes.

Use Case 3: Technical B2B Prospecting

You sell a CRM solution and are targeting all small law firms in Île-de-France. You need: - Name, address, phone, email - Technology used on their site (WordPress? Shopify? Custom?) - Social media presence

Identifying technologies manually = impossible task. Extracting data with technology detection = feasible.

IBLead: Extracting Google Maps Data Effectively

If you need to extract data from Google Maps for your prospecting, IBLead is a pre-indexed solution that saves you from technical scraping.

Why IBLead Instead of Scraping Yourself?

Scraping yourself (Python, Selenium, etc.): - Takes 2-3 weeks to develop - Requires technical knowledge - Risk of being blocked by Google (banned IPs) - Must be maintained when Google changes its interface - No historical data

IBLead: - Pre-indexed database: 50M+ businesses - Monthly updates: you always have fresh data - All filters starting at €44/month: Google rating, number of reviews, technologies, enriched email - Google reviews included: full text, rating, date, author — unique in the market - Detection of 160+ technologies: identify WordPress, Shopify, HubSpot sites, etc. - SIRET/SIREN for France: integrated INSEE data - CSV/Excel in 2 clicks: ready to import into your CRM

Concrete Example: Prospecting Plumbers with Low Ratings

Objective: find 200 plumbers in Île-de-France with a Google rating < 3 stars (potential clients to improve their reputation).

With IBLead: 1. Go to app.iblead.com 2. Select "Plumber" + "Île-de-France" 3. Filter by Google rating: < 3 stars 4. Click "Export" → CSV downloaded 5. Import into your CRM or emailing tool

Total time: 3 minutes.

Data obtained: - Name, address, phone, email - Google rating, number of reviews - Text reviews (to personalize your pitch) - Website + detected technologies - Social media

IBLead Pricing

Plan	Credits/month	Price	Per credit
Free	5,000	€0	Free
Starter	10,000	€44	€0.0035
Pro	20,000	€89	€0.0027
Business	40,000	€179	€0.0025
Enterprise	100,000	€449	€0.0025