This repository contains Spacecraft PosE Estimation Dataset (SPEED), which is used to train and evaluate the performance of deep learning models for pose estimation of noncooperative spacecraft. SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft’s 3D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a 7 degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft. Custom illumination devices simulate the Earth albedo and Sun light with high fidelity to emulate the illumination conditions present in space. SPEED was used in the international competition for spacecraft pose estimation co-hosted by the Advanced Concepts Team of the European Space Agency and the Space Rendezvous Laboratory of Stanford University.

SPEED+ is the next-generation dataset for spacecraft pose estimation with specific emphasis on the robustness of Machine Learning (ML) models across the domain gap. Similar to its predecessor, SPEED+ consists of images of the Tango spacecraft from the PRISMA mission. SPEED+ consists of three different domains of imageries from two distinct sources. The first source is the OpenGL-based Optical Stimulator camera emulator software of Stanford’s Space Rendezvous Laboratory (SLAB), which is used to create the synthetic domain comprising 59,960 synthetic images. The labeled synthetic domain is split into 80:20 train/validation sets and is intended to be the main source of training of an ML model. The second source is the Testbed for Rendezvous and Optical Navigation (TRON) facility at SLAB, which is used to generate two simulated Hardware-In-the-Loop (HIL) domains with different sources of illumination: lightbox and sunlamp. Specifically, these two domains are constructed using realistic illumination conditions using lightboxes with diffuser plates for albedo simulation and a sun lamp to mimic direct high-intensity homogeneous light from the Sun. Compared to synthetic imagery, they capture corner cases, stray lights, shadowing, and visual effects in general which are not easy to obtain through computer graphics. The lightbox and sunlamp domains are unlabeled and thus intended mainly for testing, representing a typical scenario in developing a spaceborne ML model in which the labeled images from the target space domain are not available prior to deployment. SPEED+ is made publicly available to the aerospace community and beyond as part of the second international Satellite Pose Estimation Competition (SPEC2021) co-hosted by SLAB and the Advanced Concepts Team (ACT) of the European Space Agency.

The construction of the TRON testbed was partly funded by the U.S. Air Force Office of Scientific Research (AFOSR) through the Defense University Research Instrumentation Program (DURIP) contract FA9550-18-1-0492, titled High-Fidelity Verification and Validation of Spaceborne Vision-Based Navigation. The SPEED+ dataset is created using the TRON testbed by SLAB at Stanford University. The post-processing of the raw images is reviewed by ACT to meet the quality requirement of SPEC2021.

Introduction

This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.

Content

Quadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.

Two layers are distributed as separate sets of files:

• mobile - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).
• fixed - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, Ethernet).

Quarter 1 refers to data from January to March. Quarter 2 refers to data from April to June. Quarter 3 refers to data from July to September. Quarter 4 refers to data from October to December. All the data is from the year 2020.

Citation

Speedtest® by Ookla® Global Fixed and Mobile Network Performance Maps. Based on analysis by Ookla of Speedtest Intelligence® data for 2020. Provided by Ookla and accessed February 15, 2021. Ookla trademarks used under license and reprinted with permission.

Image Credits: Unsplash - umby

Vehicle Speed Estimation

## Overview

Vehicle Speed Estimation is a dataset for object detection tasks - it contains Cars Trucks Bicycle Motorcycle annotations for 2,206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A dataset containing the traffic network information in Los Angeles city from March to Jun 2012. It is used in the traffic forecasting task in Graph Neural Networks.

The source from the paper: https://arxiv.org/abs/2206.09113

The METR-LA dataset contains 02 information: - A adj_METR-LA.pkl: is the graph that contains the physical connection of 207 loop detectors in the traffic network. This dictionary contains 03 elements: the real sensor ID, the mapped sensor to node ID, and the adjacency matrix. - A METR-LA.h5: is the time series that is collected from each sensor in the traffic network over time.

Speed

## Overview

Speed is a dataset for object detection tasks - it contains Speed annotations for 865 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Speed Estimation

## Overview

Speed Estimation is a dataset for object detection tasks - it contains Cars License Plate annotations for 330 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Speed Limit 30

## Overview

Speed Limit 30 is a dataset for object detection tasks - it contains Speed Limit 30 annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Please click here to view and download the data of average traffic speed of major roads (2nd Generation).

The mapping of maximum speeds reached in centennial flood represents four classes of uniform velocity in intensity. This is an informative map, but projects submitted to a preliminary study must refer to it in addition to the water level.

Speed

## Overview

Speed is a dataset for object detection tasks - it contains Mobil Truk Motor annotations for 218 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This layer contains the average download speed (mbps) per census block. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_BroadbandSpeedTest/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

About this dataset

Context

The dataset tabulates the Speed population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Speed. The dataset can be utilized to understand the population distribution of Speed by age. For example, using this dataset, we can identify the largest age group in Speed.

Key observations

The largest age group in Speed, NC was for the group of age 65 to 69 years years with a population of 27 (33.33%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Speed, NC was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Speed is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Speed total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Speed Population by Age. You can refer the same here

The FDOT GIS Maximum Speed Limits provides spatial information Maximum Speed Limits on Florida Roadways. It is required for all designated roadways on the SHS and HPMS samples. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 09/13/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/maxspeed.zip

Average broadband download speed (Mb/s) *This indicator has been discontinued

According to a study carried out between late 2022 and early 2023, KPN provided users with the best overall mobile data download speed of the three major national operators in the Netherlands at **** Mbps. Rivals T-Mobile and Vodafone were reported to have delivered speeds of **** and **** Mbps respectively.

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Speed, Volume and Classification Counts conducted across the City since 1993. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Turning Movement Count data, comprised of motor vehicle, bicycle and pedestrian movements through intersections, can be found here. Speed / Volume / Classification Counts are collected using pneumatic rubber tubes installed across the roadway. This dataset is a critical input into transportation safety initiatives, infrastructure design and program design such as speed limit changes, signal coordination studies, traffic calming and complete street designs. Each Speed / Volume / Classification Count is comprised of motor vehicle count data collected over a continuous 24-hour to 168-hour period (1-7 days), at a single location. A handful of non-standard 2-week counts are also included. Some key notes about these counts include: Not all counts have complete speed and classification data. These data are provided for locations and dates only where they exist. Raw data are recorded in 15-minute intervals. Raw data are recorded separately for each direction of traffic movement. Some data are only available for one direction, even if the street is two-way. Within each 15 minute interval, speed data are aggregated into approximately 5 km/h increments. Within each 15 minute interval, classification data are aggregated into vehicle type bins by the number of axles, according to the FHWA classification system attached below. The following files showing different views of the data are available: Data Dictionary (svc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (svc_summary_data): Provides metadata about every Speed / Volume / Classification Count available, including information about the count location and count date, as well as summary data about each count (total vehicle volumes, average daily volumes, a.m. and p.m. peak hour volumes, average / 85 percentile / 95 percentile speeds, where available, and heavy vehicle percentage, where available). Most Recent Count Data (svc_most_recent_summary_data): Provides metadata about the most recent Speed / Volume / Classification Count data available at each location for which a count exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data: Raw data is available in 15-minute intervals, and is distributed into one of three different file types based on the count type: volume-only, speed and volume, or classification and volume. If you’re looking for 15-minute data for a specific count, identify the count type and count date, then download the raw data file associated with the count type and period. If you’re looking for volume data for all count types, you will need to download and aggregate all three file types for a given period. Volume Raw Data (svc_raw_data_volume_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data in 15-minute intervals, for each direction separately. You will find the raw data for volume-only counts (ATR_VOLUME) here. Speed and Volume Raw Data (svc_raw_data_speed_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into speed bins in approximately 5 km/h increments. Speed data are not available for all counts. You will find the raw data for speed and volume counts (ATR_SPEED_VOLUME) here. Classification and Volume Raw Data (svc_raw_data_classification_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into vehicle type bins by the number of axles, according to the FHWA classification system. Classification data are not available for all counts. You will find the raw data for classification and volume counts (VEHICLE_CLASS) here. FHWA Classification Reference (fhwa_classification.png): Provides a reference for the FHWA classification system. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

Based on advertised download speeds.

Original source

Electronic communications market indicators collected by Commission services, through National Regulatory Authorities, for the Communications Committee (COCOM) - January and July reports.:

http://ec.europa.eu/digital-agenda/about-fast-and-ultra-fast-internet-access

Parent dataset

This dataset is part of of another dataset:

http://digital-agenda-data.eu/datasets/digital_agenda_scoreboard_key_indicators

This interactive map from the Washington State Broadband Office, Department of Commerce, displays broadband download and upload speeds (maximum and average) by location, based on 1-minute tests conducted by residents. The map is intended to identify gaps in high-speed internet service and broadband infrastructure. The COVID-19 pandemic required many residents to work or study from home, and heightened public attention on the internet service needed to support such activities.