arXiv AI June 15, 2026 By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

arXiv:2510. 13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.

By Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

llms multimodal benchmarks

arXiv Machine Learning

Jul 21

EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning

arXiv:2607. 16449v1 Announce Type: new Abstract: Accurate path loss prediction is a critical component of wireless network planning.

By Jonathan O'Shea (DCU School of Electronic Engineering), Conor Brennan (DCU School of Electronic Engineering)

arXiv AI

Jun 16

AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

arXiv:2511. 05522v4 Announce Type: replace-cross Abstract: Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications.

By Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia

arXiv AI

Jun 17

HRDX: A Large-Scale Vector HD-Map Dataset

arXiv:2606. 17080v1 Announce Type: cross Abstract: Reliable autonomous driving requires vectorized HD maps that are geometrically accurate, semantically rich, and scalable to long-horizon driving.

By Sahith Reddy Chada, Isht Dwivedi, Nirav Savaliya

agents robotics multimodal benchmarks

arXiv Machine Learning

Jun 3

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.

By Richard Schwarzkopf, Fabian Immel, Alexander Blumberg, Jonas Merkert, Nils Rack, Kaiwen Wang, Fabian Konstantinidis, Julian Truetsch, Carlos Fernandez, Annika B\"atz, Kevin R\"osch, Marlon Steiner, Willi Poh, Yinzhe Shen, Royden Wagner, Felix Hauser, Dominik Strutz, Jaime Villa, Gleb Stepanov, Holger Caesar, \"Omer \c{S}ahin Ta\c{s}, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller

agents computer-vision robotics multimodal benchmarks

arXiv Machine Learning

Jun 30

Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse

arXiv:2605. 14925v2 Announce Type: replace-cross Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.

By Yunsong Fang, Tingyu Wang, Zhedong Zheng

robotics multimodal benchmarks safety

Related stories

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning

AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

HRDX: A Large-Scale Vector HD-Map Dataset

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse