# Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction

arXiv: https://arxiv.org/abs/2412.09507 · HTML: https://mlsanity.com/paper/2412.09507/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments](https://mlsanity.com/paper/2105.07158/) (2021)
- [Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)](https://mlsanity.com/paper/2302.13022/) (2023)
- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation](https://mlsanity.com/paper/2304.11396/) (2023)
- [Outdoor Environment Reconstruction with Deep Learning on Radio Propagation Paths](https://mlsanity.com/paper/2402.17336/) (2024)
- [Empirical Application Insights on Industrial Data and Service Aspects of Digital Twin Networks](https://mlsanity.com/paper/2405.07605/) (2024)

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

- [The First Indoor Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2501.13698/) (2025)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities](https://mlsanity.com/paper/2508.03736/) (2025)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)
- [Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction](https://mlsanity.com/paper/2610.19015/) (2026)

## Abstract (arXiv)

Indoor pathloss prediction is a fundamental task in wireless network planning, yet it remains challenging due to environmental complexity and data scarcity. In this work, we propose a deep learning-based approach utilizing a vision transformer (ViT) architecture with DINO-v2 pretrained weights to model indoor radio propagation. Our method processes a floor map with additional features of the walls to generate indoor pathloss maps. We systematically evaluate the effects of architectural choices, data augmentation strategies, and feature engineering techniques. Our findings indicate that extensive augmentation significantly improves generalization, while feature engineering is crucial in low-data regimes. Through comprehensive experiments, we demonstrate the robustness of our model across different generalization scenarios.