# DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System

arXiv: https://arxiv.org/abs/2106.13632 · HTML: https://mlsanity.com/paper/2106.13632/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Dejavu: An Accurate Energy-Efficient Outdoor Localization System](https://mlsanity.com/paper/1310.2342/) (2013)
- [LaneQuest: An Accurate and Energy-Efficient Lane Detection System](https://mlsanity.com/paper/1502.03038/) (2015)
- [semMatch: Road Semantics-based Accurate Map Matching for Challenging Positioning Data](https://mlsanity.com/paper/1510.03533/) (2015)
- [A Robust Zero-Calibration RF-based Localization System for Realistic Environments](https://mlsanity.com/paper/1605.02287/) (2016)

## Built on by (most cited first, 5 of 5)

- [DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization](https://mlsanity.com/paper/2407.16927/) (2024)
- [Outdoor Position Recovery from HeterogeneousTelco Cellular Data](https://mlsanity.com/paper/2108.10613/) (2021)
- [Improving Depth Estimation using Location Information](https://mlsanity.com/paper/2112.13925/) (2021)
- [A Novel Framework for Brain Tumor Detection Based on Convolutional Variational Generative Models](https://mlsanity.com/paper/2202.09850/) (2022)
- [Handling Device Heterogeneity for Deep Learning-based Localization](https://mlsanity.com/paper/2407.16923/) (2024)

## Abstract (arXiv)

Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large areas, handling the inherent noise in the cellular signal and geo-tagged data, as well as providing enough data that is required for deep learning models with low-overhead. We implemented DeepLoc on different Android devices. Evaluation results in realistic urban and rural environments show that DeepLoc can achieve a median localization accuracy within 18.8m in urban areas and within 15.7m in rural areas. This accuracy outperforms the state-of-the-art cellular-based systems by more than 470% and comes with 330% savings in power compared to the GPS. This highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead localization system.