# Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

arXiv: https://arxiv.org/abs/2201.04125 · HTML: https://mlsanity.com/paper/2201.04125/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Spectrum Cartography via Coupled Block-Term Tensor Decomposition](https://mlsanity.com/paper/1911.12468/) (2019)
- [Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond](https://mlsanity.com/paper/1511.06890/) (2015)
- [Learning Power Spectrum Maps from Quantized Power Measurements](https://mlsanity.com/paper/1606.02679/) (2016)
- [Spatial Signal Strength Prediction using 3D Maps and Deep Learning](https://mlsanity.com/paper/2011.03597/) (2020)
- [Online Informative Path Planning for Active Classification Using UAVs](https://mlsanity.com/paper/1609.08446/) (2016)
- [Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective](https://mlsanity.com/paper/1805.07182/) (2018)
- [Location-free Spectrum Cartography](https://mlsanity.com/paper/1812.11539/) (2018)
- [Informative Planning and Online Learning with Sparse Gaussian Processes](https://mlsanity.com/paper/1609.07560/) (2016)
- [Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2005.02432/) (2020)

## Built on by (most cited first, 12 of 12)

- [ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks](https://mlsanity.com/paper/2401.08976/) (2024)
- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [Radio Map Estimation: Empirical Validation and Analysis](https://mlsanity.com/paper/2310.11036/) (2023)
- [Bayesian-Driven Graph Reasoning for Active Radio Map Construction](https://mlsanity.com/paper/2508.09142/) (2025)
- [Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments](https://mlsanity.com/paper/2510.08015/) (2025)
- [Accelerating Regularized Attention Kernel Regression for Spectrum Cartography](https://mlsanity.com/paper/2604.25138/) (2026)
- [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026)
- [Deep Multi-Emitter Spectrum Occupancy Mapping that is Robust to the Number of Sensors, Noise and Threshold](https://mlsanity.com/paper/2212.10444/) (2022)
- [Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators](https://mlsanity.com/paper/2605.08211/) (2026)
- [Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm](https://mlsanity.com/paper/2605.28234/) (2026)
- [Active Learning for Low-Altitude Radio Map Construction via Plug-and-Play Flow Matching](https://mlsanity.com/paper/2609.27486/) (2026)

## Abstract (arXiv)

Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous techniques have been proposed to construct radio maps from spatially distributed measurements, the locations of such measurements are assumed predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.