RMDirectionalBerlin

A dataset of simulated path loss radio maps for training CNN. The dataset and experiments are described in "Radio Map Estimation - An Open Dataset with Directive Transmitter Antennas and Initial Experiments" (arXiv:2402.00878), the code can be found at https://github.com/fabja19/RML. For our updated results on RM prediction from images, please go to the latest version of this dataset: https://doi.org/10.5281/zenodo.10210088 RMBerlinDirectional.zip:This file contains the dataset used in our experiments, including the radio maps, corresponding city maps, Tx information and aerial images. The dataset class in the code shows how they can be used. Structure: path_gain - contains the target radio maps as .png images gis - nDSMs of buildings and vegetation for each map as gray-scale .png images tx_antennas - .npy files containing Tx locations and characteristics for all samples img - RGBIR aerial images in .tif format antenna_patterns - .npy files containing antenna patterns los - line-of-sight information for each sample in .npy file .json files containing lists of the sample ids and corresponding Tx locations and characteristics for the dataset class model_checkpoints.zip:Contains checkpoints and configs for some trained models. additional_gis_files.zip:Contains data that was used to prepare the simulations but that is not needed for running the code. nDSM_orig - original nDSM files for buildings and vegetation created from LiDAR data, in .tif format polygons - polygons with height values generated from the original nDSM files and simplified, used in the ray-tracing simulations, in .GeoJSON format