The QMUL underGround Re-IDentification (GRID) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Reference ids for research institutions around the glob for name and acronym disambiguation. 97,795 institutions as of 3-15-2020.

The Montgomery County-Owned Facilities Map with Alpha Numeric Grid offers an enhanced visualization of county-owned facilities along with a custom grid system and facility table. Key features of the map include:Custom Alpha Numeric Grid: An alphanumeric grid surrounding the map frame, with letters along the top and numbers along the left side, providing a reference for locating facilities on the map.Facility Table: A table adjacent to the map frame, listing each county-owned facility by name, address, and grid ID. Users can quickly search for a facility by name in the table and locate its position on the map using the corresponding grid ID.Facility Labels: Each county-owned facility is labeled on the map with its corresponding grid ID, enhancing spatial awareness and navigation.Map Insets: Map insets for areas with a high concentration of facilities within a small area, ensuring clarity and detail for densely populated regions.The Montgomery County-Owned Facilities Map with Alpha Numeric Grid is optimized for printing at Arch E size (36x48 inches) and is available in Adobe PDF format. Users may need Adobe Acrobat for viewing and printing.Data Sources:Facility Locations: Montgomery County Property Management DepartmentGrid System: Customized grid system developed by Montgomery County GIS DepartmentAccess Requirements: Access to the Montgomery County-Owned Facilities Map with Alpha Numeric Grid is open to the public and stakeholders interested in county-owned properties and facilities.

n the Great Lakes, grid systems defined by latitude and longitude minutes have been used for a number of decades as a fishery standard for data reporting. The Michigan Department of Natural Resources (MDNR) Fisheries Division created this GIS layer in 2019 to depict the MDNR Michigan Great Lakes grid (v3.2) standard for Fisheries Division applications for the waters of the State of Michigan, and for Canadian waters in the St. Clair Detroit River System (SCDRS), as of June 2019. This GIS layer was created by incorporating grid boundaries and ID values from a number of existing grid standards. This layer is a composite grid that incorporates grid boundaries and ID values from the following GIS data standards for different areas of the Great Lakes: 1) 10-minute grids for Lakes Huron, Michigan, Superior and the St. Mary’s River came from the Institute for Fisheries Research (IFR) version 1 (v1) of the 10-minute grids. 2) 10-minute grids for Lake Erie came from version 2 of the IFR 10-minute grids created by the Great Lakes GIS (GLGIS) project, and 3) 5-minute grids for SCDRS came from 5-minute grids developed by the GLGIS project. Version 1 of the IFR 10-minute grids were created in 1998, and only covered Lakes Huron, Michigan, and Superior, and the St. Mary’s River. This is the GIS dataset that was used as the grid standard for the 2000 consent decree. The grid boundaries and ID values in v1 were based off of the paper maps depicted in the 1989 Status of the Fisheries Resource Report (Technical Review Committee, 1989) with some very minor grid boundary differences likely caused by bringing the paper map into a digital GIS format. In v1, 10-minute grids do not always have rectangular boundaries where each side represents 10-minutes of latitude and longitude, especially near the shoreline. In order to create the v3.2 composite layer, some features in the v1 GIS dataset that were missing ID values were assigned ID values based on historical creel maps or nearby grid ID values. Version 2 of the IFR 10-minute grids was created in 2006 and provides coverage forall five Great Lakes, but only partial coverage in the connecting channels, with no coverage in SCDRS. In contrast to v1, 10-minute grids in this dataset are true 10-minute grids with rectangular sides that strictly follow 10-minute latitude and longitude lines (along with some cases where two true 10-minute grids were combined into one grid cell with one ID value). Due to the differences in grid boundaries, there are some different ID values across v1 and v2. The GLGIS 5-minute grid GIS dataset was created in 2006 at IFR. This layer contains rectangular 5-minute grids that are true 5-minute grids, with each side of every grid representing 5-minutes of latitude or longitude. 5-minute grids were used for SCDRS in v3.2 to align with historical data reporting standards in the region, and because there are no 10-minute grids that fully cover SCDRS. 5-minute grids created by the GLGIS only exist for SCDRS, Lake Erie, and Lake Huron, and the reason for this is unknown. SCDRS grids on the Canadian side of the basin are included in v3.2 for Fisheries Division data reporting needs that may include Canadian areas of SCDRS, but these grids in Canadian waters may not represent the standard that is actively used by Canadian agencies. In order to create the v3.2 composite, grid cells from the various GIS data sources were merged together for water bodies as specified above. In v3.2 the grids are almost exactly as they appear in the source data (with minor edits such as edge matching) except where 10-minute grids 602 and 603 in Lake Erie from Version 2 were replaced with GLGIS 5-minute grids. These grids cover the transition between the Detroit River and Lake Erie, where 10-minute grids are too large for some fisheries data reporting purposes. Therefore in v3.2, the two 10-minute grids were replaced with four 5-minute grids from the GLGIS 5-minute grid dataset. ID values were kept from the 5-minute grids for the two northern cells but the two southern grids cells retain ID values from the GLGIS v2 10-minute grids (602 and 603). This was done to allow continuity with historical data that has been recorded for 10-minute grids 602 and 603, but users need to be aware that these ID values in v3.2 are now associated with 5-minute grids instead of 10-minute grids. Version 3.2 was subsequently slightly altered to create Version 3.3, which replaced the shoreline in Northern Lake Huron and slightly altered the shoreline near the Soo Locks in the St. Mary's River to match zones, closures, etc. described in the Consent Decree that were depicted with a more detailed shoreline than the v3.2 shoreline. This was done so that those zones, closures, etc. could be depicted along with the Michigan Great Lakes Grids and have alligning shoerline depictions (see Figures 13, 12 & 16 for examples of the more detailed shoreline). GIS layer was last updated 10/01/2019. Metadata last updated 10/02/2019. REFERENCE: 1) Technical Fisheries Review Committee. 1989. Status of the fishery resource – 1988. A report by the Technical Fisheries Review Committee on the assessment of lake trout and lake whitefish in treaty-ceded waters of the upper Great Lakes: State of Michigan, Technical Fisheries Review Committee.

A 2 kilometer terrace-density grid for the Idaho batholith study area. Number of columns is 331 and number of rows is 285. The order of the data is from the lower left to the right and then up one row.

The Gridded Population of the World, Version 3 (GPWv3): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same country or territory have the same value. Note that the countries and territories are not official representations of countries boundaries; rather, they represent the area covered by the statistical data as provided. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

# FiN: A Smart Grid and Powerline Communication Dataset

Within the Fühler-im-Netz (FiN) project 38 BPL modems were distributed in three different areas of a German city with about 150.000 inhabitants. Over a period of 22 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to face the increasing complex challenges in smart grids.

~~ For detailed information we would like to refer to the full paper. ~~

Attributs | FiN 1
-------- | --------
SNR measurements | 3.3 Mio
Timespan | ~2.5yrs
*Metadata* |
Sleeve count per section | ☑
Cable length, typ, cross section | ☑
Number of conductors | ☑
Year of installation | ☑
Weather by openweather | ☑

## Paper abstract
The increasing complexity of low-voltage networks poses a growing challenge for the reliable and fail-safe operation of power grids. The reasons for this are, for example, a more decentralized energy generation (photovoltaic systems, wind power, ...) and the emergence of new types of consumers (e-mobility, domestic electricity storage, ...). At the same time, the low-voltage grid is largely unmonitored and local power failures are sometimes detected only when consumers report the outage. To end the blind flight within the low voltage network, the use of a broadband over power line (BPL) infrastructure is a possible solution. In addition to the purpose of establishing a communication infrastructure, BPL also offers the possibility of evaluating the cables themselves, as well as the connection quality between individual cable distributors based on their Signal-to-Noise-Ratio (SNR). Within the Fühler-im-Netz pilot project 38 BPL modems were distributed in three different areas of a German city with about 100.000 inhabitants. Over a period of 21 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to react agilely to the increasingly complex challenges.

# FiN-Dataset release 1.0

### Content
- 68 data .npz files
- 3 weather csv files
- 2 metadata csv files
- this readme

### Summary
The dataset contains ~3.7B SNR measurements divided into 68 1-to-1 connections. Each of the 1-to-1 connections can split into additional segments, e.g. if part of a cable was replaced due to a cable break.
All 68 connections are formed by 38 different nodes distributed over three different locations. Due to data protection regulations, the exact location of the nodes cannot be given. Therefore, each of the 38 nodes is uniquely identified by an ID.

### Data
The filename specifies the location, the ID of the source node and the destination ID.
Example: "loc03_from26_to27.npz"
-> Node is in lcation 3
-> Source node is 26
-> Destination node is 27

The .npz file contains a Python dict that is structured as follows:

data_dict = {"timestamps": np.array(...), --> Nx1 Timestamps
"spectrum_rx": np.array(...), --> Nx1536 SNR assesments on 1536 channels in RX directions. Range is 0.00dB...40.00dB
"tonemap_rx": np.array(...), --> Nx1536 Tonemaps in RX directions. Range is 0...7
"tonemap_tx": np.array(...)} --> Nx1536 Tonemaps in TX directions. Range is 0...7

### Weather
In addition to the measured data, we add weather data provided by https://openweathermap.org for all three locations. The weather data is stored in CSV format and contains many different weather attributes. Detailed information on the weather data can be found in the official documentation: https://openweathermap.org/history-bulk

### Metadata
--> nodes.csv
Contains in overview of all nodes, their id, corresponding location and voltage level.

--> connections.csv
Contains all available metadata for the 68 1-to-1 connections and their individual segements.

+ year_of_installation -> year in which the cable was installed
+ year_approximated -> Indicates whether the year was approximated or not (e.g. due to missing records)
+ cable_section -> identifies the segment or section described by the metadata
+ length -> length in meters
+ number_of_conductors -> identifier for the conductor structure in the cable
+ cross-section -> cross-section of the conductors
+ voltage_level -> identifier for the voltage level (MV=mid voltage; LV=low voltage)
+ t_sleeves -> number of T-sleeves installed within a section
+ type -> cable type
+ src_id -> id of the source node
+ dst_id -> id of the destination node

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.

This dataset represents the Census data source used to produce the GPW v4.11 populations estimates. Pixels that have the same value reflect the same data source, most often a country or territory. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) grid cells. Population is distributed to cells using proportional allocation of population from census and administrative units. Population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for each modeled year.

A 2 kilometer Bouguer gravity anomaly grid for the Idaho batholith study area. Number of columns is 331 and number of rows is 285. The order of the data is from the lower left to the right and then up one row.

This geospatial dataset consists of points corresponding to the center of 50 x 50 m grid cells which were surveyed for a finite set of target invasive plant species on Ridgefield National Wildlife Refuge Complex. Each point corresponds to a single Survey123 form which was used to record the outcomes for a grid cell. Attributes include the grid cell id, surveyor's name, the date and time the survey was conducted and species-level survey results. For each of the target species data indicate whether that species was present or not present. If a weed species is observed within a grid cell, it is present, it is assigned to one of four abundance categories: Single individual, Scattered plants, Scattered dense patches, Dense monoculture, or Unknown/Not surveyed.

The Key Map Grid dataset contains square features representing 0.75-mile by 0.75-mile grids within Montgomery County, Texas. These grids are organized and referenced according to the Key Map Grid Index, with each grid corresponding to a specific location within the county. The grids are assigned unique identifiers combining the index page number and letter, with 24 grids fitting within a single index page. The letters "I" and "O" are excluded to avoid confusion with numbers. The Key Map Grid was created by the Houston Map Company, which covers multiple counties in the Houston metropolitan area including Harris, Fort Bend, Galveston, Brazoria, Liberty, Waller, and Montgomery Counties. More information can be found on the Houston Map Company's website at www.keymaps.com.Data Fields Included:Grid ID: Unique identifier assigned to each grid (combination of index page number and letter)Boundary Polygon: Square representing the 0.75-mile by 0.75-mile grid

Power Grid Inspection

## Overview

Power Grid Inspection is a dataset for object detection tasks - it contains Insulators Spacers Dampers annotations for 1,621 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).

Dr. Chwalowski, We just generated a modified version of the coarse node centered grid with split walls. Here the red and green sections you highlighted in the email have been merged. Please let me know if it is good so we can generate the rest of the grids and upload them. Thanks, Rajiv

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets.

The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains grids of the 5th, 50th and 95th percentile of drawdown in the model layers as well as grids of the probability of exceeding 0.2, 2 and 5 m drawdown as estimated with the GAL AEM model.

Dataset History

In dataset GAL_AEM_dmax_v01 the drawdown at each model layer is summarised by the 5th, 50th and 95th percentile and the probability of exceeding 0.2m, 2m and 5m. This information is contained in spreadsheets 'GAL_AEM_dmax_ExcProb_Alluvium.csv', 'GAL_AEM_dmax_ExcProb_Clematis.csv' and 'GAL_AEM_dmax_ExcProb_BCB.csv'.

The information in these spreadsheets is interpolated using QuantumGIS to create a regular grid to visualise the spatial drawdown trends. The interpolated grids are stored in this dataset.

Dataset Citation

Bioregional Assessment Programme (2016) Galilee drawdown grids. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/b106283c-2347-4024-8fa7-c582292bee65.

Dataset Ancestors

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition

• Derived From Galilee model HRV receptors gdb

Dataset

This dataset was created by Hà Lê Thu

Released under CC0: Public Domain

Contents

This dataset series contains geographical grids of 1km grid cell size for Malta.The main attributes are a Grid ID coding the East and North coordinate of the lower left corner of each grid cell plus the hierarchy level plus the quad-tree level. And the INSPIREID of the lower grid cell corner. The grid has been created following the requirements of COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services and the INSPIRE Specification on Geographical Grid Systems - Guidelines. The grid can be used to represent all kinds of statistical and scientific values. The main application is the representation of demographic data (population). This 1km grid was provided by the Eurostat European Commission

Grid

## Overview

Grid is a dataset for object detection tasks - it contains Food annotations for 2,294 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).

During the period between October, 2019 and September, 2021 some organizations were given GRIDs (and RORs) that later disappeared from the GRID database and the ROR database. These identifiers currently exist in metadata, but they can not be resolved. This dataset was created by finding RORs that existed in some version(s) of the GRID database but were dropped in some subsequent version. The dataset includes ~900 RORs that existed in some versions of the GRID database, but not in other, more recent versions.

The dataset has four columns separated by commas (CSV):

found: the last GRID version to include the ROR

notFound: the first GRID version without the ROR

affiliation: the organization name associated with the GRID

ror: the ROR

The dates of the GRID versions are approximate.