This version utilizes a generalized boundary along the coast, which is sometimes necessary for analysis in which it is important to encompass segments of roadways that travel over water. Roadways on bridges or causeways that span intracoastal waterways are not covered by detailed polygons that precisely follow the coastline, therefore a generalized boundary is needed for some types of analysis where it is important to preserve such relationships.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/17]

This dataset was created by TxDOT for internal purposes. TxDOT is not the authority for county boundary data for the state. These features were digitized by TxDOT from georeferenced USGS topo maps to enable the classification of roadway attributes for the purposes of satisfying federal and state reporting requirements, and to serve as a base layer for TxDOT's cartographic products. This version utilizes a generalized boundary along the coast, which is sometimes necessary for analysis in which it is important to encompass segments of roadways that travel over water. Roadways on bridges or causeways that span intracoastal waterways are not covered by detailed polygons that precisely follow the coastline, therefore a generalized boundary is needed for some types of analysis where it is important to preserve such relationships.Use at your own risk. Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

GeoJSON and TopoJSON files (defined with .json file extensions) used in the dashboard's map layers.

Some of these layers are sourced from the original Boerne water supply dashboard. Other sources should be specified in the map layer resource description.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

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 updated results on images and applications to coverage optimization, see the updated version of the repo https://github.com/fabja19/RML_v2_img and arxiv:2410.17264.

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.

model_checkpoints_v2_img.zip:
Contains updated checkpoints and configs for some trained models for the prediction from images.

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