🌍 Introducing Locus: A High-Precision AI Geolocation Model
Today, we are pleased to announce a significant development in the field of computer vision and spatial intelligence: Locus, an AI GeoGuessr model developed by the Infinitode team. Locus achieves a remarkable level of accuracy in pinpointing real-world locations solely from visual data, setting a new benchmark for image-based geolocation.
This project represents a sophisticated convergence of advanced machine learning techniques, designed to solve one of the most compelling challenges in AI: determining precise geographic coordinates from an arbitrary photograph.
Robustness Across Image Inputs
One of the defining features of Locus is its robust capability to process a wide variety of visual inputs. Unlike models optimized purely for unedited landscape photography, Locus is trained to handle images that include common digital artifacts and user interfaces.
This means Locus can reliably process images containing elements such as:
- Overlaid User Interfaces (UIs)
- Screenshots from digital maps (e.g., Google Maps)
- Embedded textual elements or overlays
This versatility ensures that Locus is highly functional in real-world scenarios where images frequently contain extraneous digital information.
The Technical Approach: Speed Meets Granular Precision
The exceptional performance of Locus is rooted in a highly optimized, two-stage prediction architecture that prioritizes both computational efficiency and geographic granularity.
1. Global Pre-Classification via Learned Hash Splitting
The initial phase of the Locus pipeline is dedicated to rapid global localization. The AI model itself implements the concept of hash splitting by partitioning the entire world into a vast array of discrete geographic cells. Upon receiving an image, a classification component within the model runs first, predicting the specific cell the image most likely belongs to.
This initial classification effectively transforms the global problem into a localized one, drastically reducing the subsequent search space and achieving high speed.
2. Optimized Regression for Coordinate Refinement
Immediately following the successful cell prediction, Locus transitions to the second stage: optimized regression. Within the localized cell boundary, the model executes a precise coordinate prediction, refining the initial regional estimate into a highly accurate latitude and longitude pair.
This dual-stage process allows Locus to maintain an average prediction time of just 17 milliseconds, demonstrating an impressive capability for real-time inference.
Locus Performance Metrics
To validate its capability, Locus was rigorously tested against a bespoke dataset of 24 unseen images (visual data only, absent any contextual metadata). The results confirm the model's high precision:
Metric Result Description Median Distance 2.07 km 50% of predictions were within 2.07 kilometers of the actual location. % within Cell () 66.7% Two-thirds of predictions were placed accurately within the model's defined target cell. % within 10 km 83.3% Over 83% of all predictions fell within a 10-kilometer radius of the true location. Inference Time (Average) 17 ms High-speed processing, facilitating rapid application deployment. 🛡️ Planned Release and Ethical Considerations
Given the inherent sensitivity associated with highly accurate geolocation technologies, our release strategy for Locus emphasizes responsible access and use.
We intend to release Locus on Hugging Face Spaces as a resource for research and academic exploration. This platform will facilitate controlled experimentation and responsible interaction with the model's capabilities.
Commitment to Ethical Use:
Infinitode is committed to upholding the highest standards of ethical AI deployment. The Locus model is intended strictly for research, educational, and ethical applications.
We explicitly prohibit and strongly discourage the use of Locus for:
- Activities related to unconsented surveillance or tracking.
- Doxing or unauthorized identification of individuals or private properties.
- Any activity that constitutes a violation of individual privacy rights or local/international statutes.
Users are solely responsible for ensuring their use of Locus aligns with all applicable legal and ethical frameworks.
