arXiv Machine Learning

Graph Neural Networks for RFID-Based Spatial Geometry Inference in Spatial AI Systems

arXiv:2607. 10822v1 Announce Type: new Abstract: Indoor spatial understanding remains a fundamental challenge for intelligent systems operating in physical environments.

arXiv Machine Learning

Jun 30

Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks

arXiv:2409. 11972v4 Announce Type: replace-cross Abstract: Enabling robots to autonomously discover high-level spatial concepts (e.

By Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas, Martin R. Oswald, Holger Voos, Jose Luis Sanchez-Lopez

robotics

arXiv Machine Learning

Jun 11

OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments

arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.

By Lei Chu, Yuning Zhang, Omer Gokalp Serbetci, Anushka Katiyar, Bassel Abou Ali Modad, Andreas F. Molisch

llms rag nlp benchmarks

arXiv Machine Learning

Jun 19

Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation

arXiv:2606. 19956v1 Announce Type: new Abstract: Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints.

By Yanning Wang, Zhiyong Zhou, Zhouyu Liu, Mengni Yu, Yu Feng

arXiv Machine Learning

Sep 1

Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion

arXiv:2608.28733v1 Announce Type: cross Abstract: Indoor 3D Scene Graphs (3DSGs) represent environments as multi-layer hierarchies that connect observed geometric primitives (e.g., planes) to higher-...

By Jose Andres Millan-Romera, Samuel Cognolato, Holger Voos, Jose Luis Sanchez-Lopez, Luciano Serafini

diffusion robotics

arXiv Machine Learning

Sep 23

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...

By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)

fine-tuning

arXiv Machine Learning

2d ago

Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks

arXiv:2602.23321v2 Announce Type: replace-cross Abstract: Using advanced machine learning techniques, we developed a method to reconstruct the arrival direction and energy of ultra-high-energy cosmic...

By Ars\`ene Ferri\`ere, Aur\'elien Benoit-L\'evy, Olivier Martineau-Huynh, Mat\'ias Tueros

arXiv AI

Jul 3

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

arXiv:2607. 01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization.

By Lizhou Liu, Xiaohui Chen, Zihan Tang, Mengyao Ma, Wenyi Zhang

diffusion

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

arXiv Machine Learning

Jul 23

Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

arXiv:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.

By William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu

benchmarks

arXiv AI

Jun 15

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.

By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis

llms computer-vision

arXiv Machine Learning

Jul 7

Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios

arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.

By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott

arXiv AI

Jun 12

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.

By Mohammad Hashemi, Hossein Amiri, Andreas Zufle

rag benchmarks

Related stories