Geolocation technology is everywhere—whether you’re finding your way around a new city, ordering your favorite takeout, or just checking in on social media. But nailing that perfect location accuracy has always been tricky due to signal interference, weather conditions, and tall buildings messing with your GPS.
With inaccurate geolocation delivery drivers struggle to find your address, making your food show up late and cold. Road trips turn into frustrating detours and ride-shares become a hassle when the app places you a few blocks away.
This is where Artificial Intelligence (AI) enters as a game-changer for making geolocation more accurate and reliable than ever before.
This blog will explore the ways AI can improve geolocation accuracy along with some practical applications.
3 Ways to Improve Geolocation Accuracy with AI
Traditional geolocation methods, like GPS, rely on satellite signals to triangulate positions. While widely used, GPS struggles in urban areas with tall buildings, dense forests, and places with weak satellite signals, leading to significant positioning errors.
Here’s how AI can make a difference.
1. Contextual awareness
AI is smart enough to understand the type of environment you’re in. For example, a tall mall building or a subway station can interfere with GPS and Wi-Fi signals. By recognizing these contexts, AI can anticipate where signal disruptions might occur.
Similarly, during crowded concerts, AI understands signal disruptions and adjusts by relying on data from other sensors and previously learned patterns of signal behavior in similar crowded environments.
AI also tracks your behavior and movement patterns. If it notices you’re moving through areas where signals typically fluctuate—like transitioning from outdoors to indoors or from one type of building to another—it can adjust how it calculates your location to account for these changes.
AI language models also process and analyze textual data related to your location. For example, they can sift through user reviews, social media posts, or check-ins to identify patterns and provide more accurate location predictions based on real-world context.
So, if you frequently mention certain places or activities, the language model can use this information to refine location accuracy further.
2. Machine learning algorithms
Machine learning (ML) algorithms are trained on historical geolocation data to identify patterns and correct errors in real time. Plus, these models quickly adapt to new data for more accurate location information. Here’s how it works:
- Predict signal issues: ML models can look at past data to figure out where signal loss often happens. This way, if you’re in an area with weak signals, the system can adjust your location more accurately.
- Fix signal reflections: Sometimes, signals bounce off buildings or other surfaces, causing errors. ML models can detect these reflections and correct the location data so it’s more precise.
- Deep learning for details: Deep learning models, a type of ML, dives deeper into data to recognize complex patterns and relationships that regular models might miss. For example, it can improve indoor positioning by analyzing how signal strength changes from different sources, like Wi-Fi routers or beacons.
3. Predictive analytics
Predictive analytics takes geolocation to the next level by making it way more accurate.
It uses information about nearby landmarks and the type of area you’re in to fine-tune its location predictions. So, if you’re near a well-known spot or in a busy part of town, it adjusts accordingly, making the recommendations or alerts you get more relevant to your current situation.
Plus, it tailors the experience to your habits. Over time, the system learns where you usually go and what you like, which means it can offer personalized suggestions or alerts based on your preferences. This makes using location-based services feel more intuitive and custom-fit to your lifestyle.
For businesses, this means more efficient operations. With better location data, companies can optimize delivery routes, target marketing efforts more accurately, and generally offer a smoother service.
In a nutshell, predictive analytics helps turn raw location data into actionable insights, making everything from personal navigation to business logistics run more smoothly.
5 Practical Applications of AI-enhanced Geolocation
The integration of AI in geolocation has led to numerous practical applications across various industries, such as:
1. Navigation and mapping
Applications like Google Maps use AI to predict traffic conditions, suggest optimal routes, and provide real-time updates.
2. Location-based services
Location-based services such as ride-hailing apps (Uber, Lyft) and food delivery services (DoorDash, Uber Eats) rely heavily on accurate geolocation. AI helps these services provide precise pick-up and drop-off locations, improving customer satisfaction and operational efficiency.
3. Emergency public safety
If you call emergency services, AI can analyze your surroundings and movement patterns to give responders a more accurate idea of your location, even in challenging environments like underground areas or dense crowds.
4. Retail and marketing
Retailers and marketers can leverage AI-driven geolocation to deliver personalized and context-aware experiences to customers. For example, AI can provide targeted promotions based on a customer’s precise location within a store.
5. Route planning
For businesses that rely on transportation and delivery services, AI-enhanced geolocation can optimize route planning and fleet management. It can predict delays, suggest alternate routes, and monitor vehicle locations in real time, leading to more efficient operations and faster delivery times.
Summing Up
Integrating AI into geolocation is a major leap forward, bringing unmatched accuracy and reliability. It offers a significant edge by enhancing user experiences, boosting security, and streamlining operations.
As AI continues to evolve, we can expect even greater advancements in geolocation accuracy, which will likely revolutionize industries such as autonomous driving, smart city infrastructure, and personalized marketing strategies.
Whether you’re a business leader, tech enthusiast, or simply interested in the future of technology, keeping an eye on these advancements will help you leverage the opportunities that lie ahead.
James is the head of marketing at Tamoco
