# AI Powered Road Network Prediction with Multi-Modal Data

arXiv: https://arxiv.org/abs/2312.17040 · HTML: https://mlsanity.com/paper/2312.17040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)
- [A survey and taxonomy of loss functions in machine learning](https://mlsanity.com/paper/2301.05579/) (2023)
- [Measures of Complexity for Large Scale Image Datasets](https://mlsanity.com/paper/2008.04431/) (2020)
- [City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times](https://mlsanity.com/paper/1908.09715/) (2019)
- [DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories](https://mlsanity.com/paper/2002.06832/) (2020)
- [Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction](https://mlsanity.com/paper/2111.15119/) (2021)
- [Road Network and Travel Time Extraction from Multiple Look Angles with SpaceNet Data](https://mlsanity.com/paper/2001.05923/) (2020)

## Abstract (arXiv)

This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before. We rigorously investigate both early and late fusion strategies, and assess deep learning based road detection performance using different fusion settings. Our extensive ablation studies assess the efficacy of our framework under diverse model architectures, loss functions, and geographic domains (Istanbul and Montreal). For an unbiased and complete evaluation of road detection results, we use both region-based and boundary-based evaluation metrics for road segmentation. The outcomes reveal that the ResUnet model outperforms U-Net and D-Linknet in road extraction tasks, achieving superior results over the benchmark study using low-resolution Sentinel-2 data. This research not only contributes to the field of automatic road detection but also offers novel insights into the utilization of data fusion methods in diverse applications.