RSSD

## Overview

RSSD is a dataset for instance segmentation tasks - it contains Railway annotations for 471 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).

Main Purpose

The main purpose is to localize target nodes based on the collection of the Received Signal Strength (RSS) values (WiFi RSS Fingerprintss) from several reference points, along with the corresponding 2D coordinates, and then saved into a database file (csv file). https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6473214%2F3c759b119230791dae9e4ab73fe5d3d9%2FOffline%20Phase.png?generation=1677248162929233&alt=media" alt="">

Implementation (Hardware and Environment testbed)

A set of XBee Series 2 has been employed in this study, using the ZigBee communication protocol. The experiment testbed consists of a ZigBee network with 5 sensor nodes (4 router and 1 coordinator nodes). The router nodes act as stationary sensor nodes (reference nodes) with known locations, whereas the coordinator node acts as a mobile target node with unknown locations. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6473214%2Fe1d20e546cd253a70058a33ced320226%2FXBee.png?generation=1677248181444819&alt=media" alt=""> On the other hand, the the reference nodes were deployed in indoor environment, which is a research lab located in the Industrial Innovation and Robotics Center (IIRC) lab at the University of Tabuk with the following dimension size (21.20 m × 7.60 m), as presented in the Figure below, where the IIRC lab includes different benches, devices, robots, equipment, and offices. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6473214%2Fb4c85e5a3e9e2bc1208f286ff6e2dcb0%2FArea.png?generation=1677248238727332&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6473214%2F78b33caaf5f8474c88c0aaaf931c63a9%2FArea_dataset.png?generation=1677247149635636&alt=media" alt="">

Structure of the WiFi RSS Fingerprint Dataset

The structure of the collected RSS dataset is presented in the Figure below, where the collected data consists of 6 attributes (4 features and 2 labels). The features set includes the RSS values from 4 different reference nodes, whereas the labels set is the corresponding location of the mobile target node. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6473214%2F3cb916d455c20f439d89a22360e98e53%2FStructure%20of%20RSS.png?generation=1677247239464295&alt=media" alt="">

WiFi RSS Fingerprint Datasets

There are 3 dataset files: 1. RSSISensors_Small 2. RSSISensors_Medium 3. RSSISensors_Large

The 3 dataset values were collected from the same experiment testbed. However, they differ in the number of reference points that have been stored in each dataset. The RSSISensors_Small dataset composed of 68 references points collected from evenly distributed reference points. The RSSISensors_Medium consists of 126 records (reference points). And finally, the RSSISensors_Large combines the data values of RSSISensors_Small and RSSISensors_Medium datasets, with a total number of 194 records (reference points).

The illustration of data attributes in the RSSISensors datasets, are as follows: r1: refers to the RSS value from reference node 1. r2: refers to the RSS value from reference node 2. r3: refers to the RSS value from reference node 3. r4: refers to the RSS value from reference node 4. x: is the actual x-coordinate for the mobile target node. y: is the actual y-coordinate for the mobile target node.

For More Details about the presented Dataset, Please Read and Cite the following Paper: Alhmiedat, T., 2023. Fingerprint-Based Localization Approach for WSN Using Machine Learning Models. Applied Sciences, 13(5), p.3037.

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Update: New version includes additional samples taken in November 2022.

Dataset Description

This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.

The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.

Dataset Description Sample Count Receiver Count

No-Tx Samples 46 10 to 25

1-Tx Samples 4822 10 to 25

2-Tx Samples 346 11 to 12

The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:

(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) )

Measurement Parameters Description

Frequency 462.7 MHz

Radio Gain 35 dB

Receiver Sample Rate 2 MHz

Sample Length N=10,000

Band-pass Filter 6 kHz

Transmitters 0 to 2

Transmission Power 1 W

Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.

Usage Instructions

Data is provided in .json format, both as one file and as split files.

import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f)

The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:

rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.

tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude.

metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords

File Separations and Train/Test Splits

In the separated_data.zip folder there are several train/test separations of the data.

all_data contains all the data in the main JSON file, separated by the number of transmitters.

stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.

train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json.

The random split is a random 80/20 split of the data.

special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.

The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split.

The seasonal split contains data separated by the month of collection, in April, July, or November

The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data.

campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json.

Digital Surface Model

The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.

To read the data in python:

import rasterio as rio import numpy as np import utm

dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T')

Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.

DSM DOI: https://doi.org/10.5069/G9TH8JNQ

The Cassini Radio Science Saturn Ring and Atmospheric Occultation experiments (SROC25) Raw Data Archive is a time-ordered collection of radio science raw data acquired on April 6, 20, and on May 28, 2017, during the Cassini Extended Extended Mission.

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This is an RSS Feed of Food Safety information that’s produced in real-time by the CDC. This RSS feed is the integration of two other XML feeds, one from the USDA's Food Safety Inspection Service (FSIS) - http://www.fsis.usda.gov/RSS/usdarss.xml - and one from the FDA's Food Safety Recalls - http://www.fda.gov/AboutFDA/ContactFDA/StayInformed/RSSFeeds/FoodSafety/.... Most users will prefer the CDC feed linked above, but developers may prefer the individual XML feeds.

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The Cassini Radio Science Solar Corona Characterization Experiment (SCC6) Raw Data Archive is a time-ordered collection of radio science raw data acquired from September 18 to September 30, 2010, during the Cassini Extended Mission.

This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558792 for the second batch.

Please do use version 2 for better quality.

We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

1.Building Floor

This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.

1. Office

Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP).

3.Apartment

Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed.

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.

Only RTT-enabled APs were included in the dataset.

The features of the datasets

The features of the building floor dataset are as follows:

Testbed area: 92 × 15 m2

Grid size: 0.6 × 0.6 m2

Number of AP: 13

Number of reference points: 642

Samples per reference point: 120

Number of all data samples: 77040

Number of training samples: 57960

Number of testing samples: 19080

Signal measure: WiFi RTT, WiFi RSS

Collection time interval: 3 days

The features of the office dataset are as follows:

Testbed area: 4.5 × 5.5 m2

Grid size: 0.455 × 0.455 m2

Number of AP: 3

Reference points: 37

Samples per reference point: 120

Data samples: 4,440

Training samples: 3,240

Testing samples: 1,200

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: A LOS scenario

The features of the apartment dataset are as follows:

Testbed area: 7.7 × 9.4 m2

Grid size: 0.48 × 0.48 m2

Number of AP: 4

Reference points: 110

Samples per reference point: 120

Data samples: 13,200

Training samples: 9,720

Testing samples: 3,480

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: Contains an AP with NLOS paths for most of the RPs

Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample.

Column 'Y': the Y coordinates of the sample.

Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.

Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.

Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:

The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.

Citation request

When using this dataset, please cite the following two items:Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558192@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor & Francis} }

The Cassini Radio Science Saturn Atmosphere and Ring Occultation Experiment (SROC8) Raw Data Archive is a time-ordered collection of radio science raw data acquired on July 7, August 4, 19, 26, and September 10, 2008, during the Cassini Extended Mission.

Serbia Banks: Assets data was reported at 6,766,932.300 RSD mn in Mar 2025. This records a decrease from the previous number of 6,788,226.863 RSD mn for Feb 2025. Serbia Banks: Assets data is updated monthly, averaging 3,146,156.555 RSD mn from Aug 2001 (Median) to Mar 2025, with 284 observations. The data reached an all-time high of 6,862,423.731 RSD mn in Dec 2024 and a record low of 279,709.000 RSD mn in Aug 2002. Serbia Banks: Assets data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.KB008: Balance Sheet: Banks.

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This is a dataset of RSS measurements collected by Mica2 sensor nodes deployed inside and outside a lab room, with anomaly patterns occurring when students walked into and out of the lab. A web camera recorded the activity that could be matched with detected anomalies. ; hero@eecs.umich.edu

The Cassini Radio Science Saturn and Ring Occultation Experiment (SROC6) Raw Data Archive is a time-ordered collection of radio science raw data acquired on Jan 15, 27, Feb 8, and March 9, 2008, during the Tour subphase of the Cassini mission.

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jacobbieker/eumetsat-cloudmask-rss dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached.

The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied.

The detailed dataset description can be found in the dataset_description.pdf file.

When using the dataset, please consider citing the original paper, in which the data were used:

M. Kolakowski, “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”, Sensors , vol. 21, 6270, Sep. 2021 https://doi.org/10.3390/s21186270

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The Cassini Radio Science Rhea Gravity Experiment (RHGR1) Raw Data Archive is a time-ordered collection of radio science raw data acquired on November 25, 26, 27, 2005 during the Tour subphase of the Cassini mission. DATA_SET_DESC =