City of benchmarks as outlined by the Department of Public Works.

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

The benchmark data contains name, type, material, coordinates, elevations and vertical order. All benchmarks were conventionally leveled through in accordance with the procedures setup in the Brevard County Vertical Control Manual (October 2012). The elevations of the bench marks are based on the North American Vertical Datum of 1988 (NAVD88). The horizontal coordinates are from a handheld GPS unit and are fro reference purposes only.

The dataset

The dataset is produced within the SafeLog project and it is used for benchmarking of multi-agent path planning algorithms. Specifically, the dataset consists of a set of 21 maps with increasing density and a set of 500 random assignments, each for a group of 100 agents for planning on each of the maps.

All of the maps, in the form of a graph G = {V, E}, are built on the same set of 400 vertices V. The sets of edges Ej, where j ∈ (0; 20), in the maps then form a set ranging from a spanning tree to a mostly 4-connected graph. These maps were created by generating a complete square graph with the size of 20*20 vertices. The graph was then simplified to a spanning tree, and, finally, approximately 50 random edges from the complete graph were added 20 times, to create the set of 21 maps of density ranging from 800 to 1500 edges in the graph.

Content and format

The following files are included in the dataset

test_nodes.txt - 400 nodes of a 20*20 square map in the form "id x y"
testAssignment.txt - 50499 random pairs of nodes ids from test_nodes.txt
test_edgesX.txt - pairs of adjacent nodes ids from test_nodes.txt forming edges
- X = 0 - tree
- X = 20 - full graph
- created starting at a full graph and repeatedly erasing edges until a tree remains

To illustrate the maps in the dataset, we provide three images (1008.png, 1190.png, and 1350.png) showing maps with 1008 (1190, 1350) edges.

Citation

If you use the dataset, please cite:

[1] Hvězda, J., Rybecký, T., Kulich, M., and Přeučil, L. (2018). Context-Aware Route Planning for Automated Warehouses. Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

@inproceedings{Hvezda18itsc,
author = {Hvězda, Jakub and Rybecký, Tomáš and Kulich, Miroslav and Přeučil, Libor},
title = {Context-Aware Route Planning for Automated Warehouses},
booktitle = {Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC)},
publisher = {IEEE Intelligent Transportation Systems Society},
address = {Maui},
year = {2018},
doi = {10.1109/ITSC.2018.8569712},
}

[2] Hvězda, J., Kulich, M., and Přeučil, L. (2019). On Randomized Searching for Multi-robot Coordination. In: Gusikhin O., Madani K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham.

@inbook{Hvezda19springer,
author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},
title = {On Randomized Searching for Multi-robot Coordination},
booktitle = {Informatics in Control, Automation and Robotics},
publisher = {Springer},
address = {Cham, CH},
year = {2019},
series = {Lecture Notes in Electrical Engineering},
language = {English},
url = {https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18},
doi = {10.1007/978-3-030-31993-9},
}

[3] Hvězda, J., Kulich, M., and Přeučil, L. (2018). Improved Discrete RRT for Coordinated Multi-robot Planning. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2).

@inproceedings{Hvezda18icinco,
author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},
title = {Improved Discrete RRT for Coordinated Multi-robot Planning},
booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2)},
publisher = {SciTePress},
address = {Madeira, PT},
year = {2018},
language = {English},
url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1},
doi = {10.5220/0006865901710179},
access = {full}
}

We are glad to release the benchmark dataset in our Siggraph Asia 2021 paper Optimizing Global Injectivity for Constrained Parameterization. The dataset is used to test various methods on their ability to recover an injective mapping from a non-injective initial mapping while keeping a group of positional constraints in place. The dataset includes 1791 triangle mesh examples. Each example consists of an input rest mesh, an initial mesh, several constrained vertices (called handles), and a ground truth injective mapping obtained from the Locally Injective Mappings Benchmark.

For reference, we also share our method's results on the examples in Constrained-Injective-Mappings-Result.zip.

We hope that our dataset offers a benchmark for future research in this area.

This data was developed to represent Temporary Benchmarks in the City of Cape Coral and their associated attributes for the purpose of mapping, and planning. The mapped location is for reference, only see the point description for better physical locations. The Elevations are based on a level loop closure resulting in a plus or minus of .02 feet times the square root of the distance for each closed loop in miles. It is incumbent on the user to verify all data shown hereon.

Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

Benchmarks are survey markers that provide a point of particular elevation used as a reference for determining elevations of other points in a survey. They are used by surveyors, engineers, planners, and contractors for establishing elevations for planning, designing, and/or construction of various projects.The City of Portland is responsible for establishing and maintaining a network of benchmarks throughout the city, each having a known elevation expressed in feet in the City of Portland’s own datum (established in 1896). Most of the City of Portland’s Benchmarks are brass disks about 2 ½" in diameter, and all are marked "City of Portland Bench Mark No. nnnn " and usually set in the curbs of streets. Occasionally you’ll see larger disks of 3 ¾" diameter which are "Class A" monuments, and some benchmarks are set in retaining walls, bridge wing walls, culvert headwalls, concrete steps, or wherever the most stable and accessible placement was determined to be for a specific location. You might also see benchmarks similar to those of the City of Portland’s but with another governmental agency’s or private firm’s name stamped on it. Occasionally the City of Portland Benchmark Book will refer to these monuments and provide elevations, but more often you’ll have to contact the appropriate institution for more information. The benchmark numbers the City assigned to outside agency monuments are for indexing purposes only; PDOT does not stamp a City of Portland benchmark number on other agencies' monuments.-- Additional Information: Category: Survey Purpose: For mapping and analysis related to the maintenance of survey benchmarks. Update Frequency: As needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=53238

The ubiMap dataset is comprised of 3,530 map images collected from the Bing image search service (1,730 maps) and Geo-Journal (1,800 maps). Each image has been manually labeled with 22 types of map elements, including their boundary shapes and category properties, resulting in an average of 5.92 elements per map. ubiMap-l is built uopon ubiMap by removing maps that contained only one element, which results a total of 3,515 maps for map layout retrieval test. We first opensourced 703 maps in ubiMap-l that we used for testing our map layout representation learning framework, MapLayNet. Besides 703 map images and their layout label data, embedding of MapLayNet and its baseline model is provided along with the python codes for embedding visualizaiton. The dataset will be open access in late 2025.

Overview

The EuroSDR RPAS benchmark datasets were aqcuired in August 2021 as part of the EuroSDR benchmark initiative. This aims to evaluate the true geometric quality of real-world survey data generated from Remotely Piloted Aircraft System (RPAS) photogrammetry and lidar under different control configurations, focussing primarily on the geometric quality of data generated in the absence of ground control and local GNSS base station information.

Guided by a task force of National Mapping and Cadastral Agencies (NMCAs) experts and academics, in August 2021 Newcastle Geospatial Engineering team have established and surveyed a coordinated test field of independent checkpoints (CPs), test surfaces and profiles at the disused Wards Hill Quarry near Morpeth, Northumberland, UK. The 350 x 250 m study area was simultaneously surveyed using the following RPAS mounted instruments, each limited to a single flight to represent “real-world” operation:

• DJI Phantom 4 RTK
• DJI ZENMUSE P1
• DJI ZENMUSE L1
• Routescene LidarPod
• Riegl MiniVUX

More information can be found here.

To facilitate and encourage wider use of the EuroSDR RPAS dataset, it has been made open access.

About us

We are the Geospatial Engineering research group in the School of Engineering at Newcastle University with a long history of research and teaching across geospatial disciplines. EuroSDR is a not-for-profit organisation linking National Mapping and Cadastral Agencies with Research Institutes and Universities in Europe for the purpose of applied research in spatial data provision, management and delivery.

SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.SDNist is available via pip install: pip install sdnist==1.2.8 for Python >=3.6 or on the USNIST/Github. The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.

Project Page

https://open-earth-map.org/

Paper

https://arxiv.org/abs/2210.10732

Overview

OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.

Reference

@inproceedings{xia_2023_openearthmap,
  title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping},
  author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month = {January},
  year = {2023},
  pages = {6254-6264}
}

License

Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data here. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Note for xBD data

The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from https://xview2.org/dataset and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.

Code

Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available here.

Leaderboard

Performance on the test set can be evaluated on the Codalab webpage.

City of Eugene Benchmarks locate surveyed benchmarks installed in the ground.

OGC Web Map Service — Ground benchmark map. It shows the urban area in an overview map and the city centre area in a detail map (only visible from the scale of 1:25 000). The soil benchmark is the average position value of the soil in euro/m². The soil benchmark card is adopted annually by the Stuttgart Review Committee on the reporting date 31.12. This WMS is available in versions 1.1.1 and 1.3.0.

Lygame dataset contains two subsets (lygame1and ygame2). The dataset is specifically designed for click-through rate (CTR) prediction under sparse and structure-constrained conditions. Compared with existing datasets like Movielens and Criteo, this is a rather small dataset due to the nature of available resources in the simulation scenario. Unlike many-to-many user-item interactions, Lygame exhibits a strict one-to-many structure. This results in an asymmetric and highly sparse interaction matrix, posing unique challenges for recommendation models. (Due to privacy considerations, we only release a small portion of the original data. The full dataset will be made available upon acceptance.)

Analysis of ‘Elevation Benchmarks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/70702d43-697b-469c-ba43-514abe474f03 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

--- Original source retains full ownership of the source dataset ---

Map showing benchmark locations within the City of Sugar Land.

Contains 70 maps of size 512x512 and benchmark problem sets. These maps are algorithm-generated by blocking grid cells. Maps contain 10%, 15%, 20%, 25%, 30%, 35%, or 40% blocked cells. There are 10 maps and problem sets for each percentage.