Search and Rescue Satellite Aided Tracking (SARSAT)A Beacon of Hope to Those in DistressNOAA satellites do more than just monitor the weather. They also detect and relay distress signals from emergency beacons to the appropriate search and rescue authorities. This tells them who is in trouble and, more importantly, where they are located.The NOAA–SARSAT program is part of COSPAS–SARSAT, an international satellite-based monitoring initiative to which 45 nations and independent search and rescue organizations belong. Using this system, authorities can locate beacons almost anywhere in the world at any time, and in almost any condition. COSPAS stands for "COsmicheskaya Sisteyama Poiska Avariynich Sudov," Russian for “Space System for the Search of Vessels in Distress.” A sailor being rescued at sea.SARSAT Tracking ApplicationEach icon on this map represents one rescue event within the U.S. Area of Responsibility (AOR) in 2021, though multiple saves may be involved with each event. The Search and Rescue Satellite Aided Tracking (SARSAT) system is able to detect three types of beacons: an individual’s Personal Locator Beacons (PLBs), maritime Emergency Position Indicating Radio Beacons (EPIRBs), and aircraft Emergency Locator Transmitters (ELTs). Who responds to the search and rescue are dictated by the location of the distress. For any beacon activation that occurs in the U.S. AOR, the U.S. is responsible for responding. If it is inland, U.S. Air Force responds*, if it is at sea, the U.S. Coast Guard. If a device registered to another country is activated within the U.S. AOR, the U.S. is still responsible for the rescue but their homeport country will be notified of the event. All areas of the world are covered by COSPAS-SARSAT.The COSPAS–SARSAT ProgramThis program consists of: Emergency beacons that transmit distress signalsSatellites that detect the distress signalsGround receiving stations that receive and process the satellite signals to generate distress alertsMission control centers that receive the alerts and forward them to rescue coordination centers, operated by the U.S. Coast Guard and the U.S. Air Force.The U.S. SARSAT system uses NOAA satellites in low-earth and geostationary orbits as well as GPS satellites in medium earth orbit to detect and locate aviators, mariners, and land-based users in distress. The satellites relay distress signals from emergency beacons to a network of ground stations and ultimately to the U.S. Mission Control Center (USMCC) in Suitland, Maryland.Types of search and rescue beacons. The Four Agencies Involved in the U.S. SARSAT ProgramNOAA: System Operation and representative to COSPAS-SARSATNASA: Research and Development U.S. Coast Guard: Maritime Search and Rescue U.S. Air Force: Inland Search and Rescue HistoryCredit: Arizona Daily StarThe first beacons used the existing 121.5/243 MHz emergency locator transmitters (ELTs) designed for military aircraft in the 1950s. After a small plane carrying Rep. Hale Boggs (D-La.) along with Rep. Nick Begich (D-Alaska) and others disappeared in Alaska in 1972, Congress mandated ELTs on all U.S. aircraft. Canada soon did the same. However, early models were not as easy to detect, and there was no way to identify a specific beacon or find its exact location. Thus, engineers began developing new, more robust digital beacons that operated at 406 MHz. Signals from these new beacons could be received from anywhere on the planet, located accurately and almost instantly, and rescue forces would know who and what to look for.The United States and Canada began looking for other international partners with the ability to launch satellites to achieve a truly global distress alerting satellite system. Russia and France soon signed on to help develop the system for humanitarian purposes. On June 30, 1982, Russia launched the first experimental COSPAS–SARSAT satellite. Before it was even officially declared operational, the first distress signal was detected—a downed Canadian aircraft. Within the first hundred days of the satellite’s operation, seven people were rescued using the system. Soon after, NASA launched their own SARSAT payload on NOAA-8. The program has continued to grow ever since. Today, with newer, more advanced beacons and a global network of next generation satellites, COSPAS–SARSAT strives to keep improving its ability to take the “search” out of “search and rescue” and ultimately save lives. Important InformationAll U.S. coded beacons MUST be registered with NOAA. Read our registration brochure to learn more. Safety NoticesAirworthiness DirectivesAmeri-King Corporation ELTsWarning regarding unapproved beacon batteriesKannad SAFELINK EPIRB recall

Land Cover Map 2021 (LCM2021) is a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2021. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2021. Land cover maps describe the physical material on the surface of the country. For example grassland, woodland, rivers & lakes or man-made structures such as roads and buildingsThis is a 10 m Classified Pixel dataset, classified to create a single mosaic of national cover. Provenance and quality:UKCEH’s automated land cover classification algorithms generated the 10m classified pixels. Training data were automatically selected from stable land covers over the interval of 2017 to 2019. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the pixel classification into a land parcel framework (the LCM2021 Classified Land Parcels product). The classified land parcels were compared to known land cover producing confusion matrix to determine overall and per class accuracy.View full metadata information and download the data at catalogue.ceh.ac.uk

A PDF map that shows the health areas in England and Wales as at April 2021. The map shows the health geographies: clinical commissioning groups that became operative in England as at April 2021 and the local health boards that became operative in Wales as at April 2019. (File Size - 1,004 KB)

Flown in March/April 2021. The ground sampling distance (imagery resolution) is 3 inch. Data compiled to meet or exceed a horizontal accuracy of +/- 2.5 feet (75 cm) RMSE. Imagery provided by Nearmap.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

This map package references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis map package consists of tree canopy data covering the following categories:50-acre HexagonsCouncil DistrictsSDOT Urban Forestry Management UnitsManagement Units - Dissolved with ROWParcels Right of WayBlock GroupsRSE Census TractsPublic SchoolsBasinsFor more information, please see the 2021 Tree Canopy Assessment.

The Mobility Annual Plan, or MAP, is an annual process designed to offer a transparent and flexible means of delivering a very dynamic, interdependent, and complex set of Corridor, Regional, and Local Mobility Bond funded projects. Review the draft 2021 MAP in narrative form and projects via the online interactive map. The comment period for the MAP was open from December 16, 2020 through February 7, 2021. The final MAP will be published in the coming months. Coordination among the 2016 Mobility Bond programs will continue through the project development and delivery process, as well as in the development of the 2022 MAP. Once projects are fully developed and have dedicated funding, they will appear on the Capital Projects Explorer (CPE) site.

ICDAR 2021 Competition on Historical Map Segmentation — Dataset

This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”).
This competition ran from November 2020 to April 2021.
Evaluation tools are freely available but distributed separately.

Official competition website: https://icdar21-mapseg.github.io/

The competition report can be cited as:

Joseph Chazalon, Edwin Carlinet, Yizi Chen, Julien Perret, Bertrand Duménieu, Clément Mallet, Thierry Géraud, Vincent Nguyen, Nam Nguyen, Josef Baloun, Ladislav Lenc, and Pavel Král, "ICDAR 2021 Competition on Historical Map Segmentation", in Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21), September 5-10, 2021, Lausanne, Switzerland.

BibTeX entry:

@InProceedings{chazalon.21.icdar.mapseg,
 author  = {Joseph Chazalon and Edwin Carlinet and Yizi Chen and Julien Perret and Bertrand Duménieu and Clément Mallet and Thierry Géraud and Vincent Nguyen and Nam Nguyen and Josef Baloun and Ladislav Lenc and and Pavel Král},
 title   = {ICDAR 2021 Competition on Historical Map Segmentation},
 booktitle = {Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21)},
 year   = {2021},
 address  = {Lausanne, Switzerland},
}

We thank the City of Paris for granting us with the permission to use and reproduce the atlases used in this work.

The images of this dataset are extracted from a series of 9 atlases of the City of Paris produced between 1894 and 1937 by the Map Service (“Service du plan”) of the City of Paris, France, for the purpose of urban management and planning. For each year, a set of approximately 20 sheets forms a tiled view of the city, drawn at 1/5000 scale using trigonometric triangulation.

Sample citation of original documents:

Atlas municipal des vingt arrondissements de Paris. 1894, 1895, 1898, 1905, 1909, 1912, 1925, 1929, and 1937. Bibliothèque de l’Hôtel de Ville. City of Paris. France.

Motivation

This competition aims as encouraging research in the digitization of historical maps. In order to be usable in historical studies, information contained in such images need to be extracted. The general pipeline involves multiples stages; we list some essential ones here:

• segment map content: locate the area of the image which contains map content;
• extract map object from different layers: detect objects like roads, buildings, building blocks, rivers, etc. to create geometric data;
• georeference the map: by detecting objects at known geographic coordinate, compute the transformation to turn geometric objects into geographic ones (which can be overlaid on current maps).

Task overview

• Task 1: Detection of building blocks
• Task 2: Segmentation of map content within map sheets
• Task 3: Localization of graticule lines intersections

Please refer to the enclosed README.md file or to the official website for the description of tasks and file formats.

Evaluation metrics and tools

Evaluation metrics are described in the competition report and tools are available at https://github.com/icdar21-mapseg/icdar21-mapseg-eval and should also be archived using Zenodo.

Dataset contains a series of maps that are used in the 2021 Republic of the Marshall Islands State of the Environment Report. Resources within this dataset may be sued for other reporting purposes.

Development Maps (2021)Series of Maps and Statistics detailing Proposed, Approved, Under Construction, and Completed Development in the City of Jersey City, Hudson County, New Jersey.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. NLCD 2021 adds an additional year to the map products produced for NLCD 2019, with a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The dataset includes data on number of adults without an account (in millions) in countries all over the world during 2017.

Globally, 1.7 billion adults lacked a formal financial account in 2017

Map Note: Data are not displayed for economies in which the share of adults without an account is 5 percent or less.

To create the 2021 Suisun Marsh vegetation map, vegetation was interpreted from a mosaic of the true color imagery that was flown in June 2021. Polygons were delineated using heads-up digitizing (i.e., a photo interpreter manually drew polygons around each stand of vegetation) in Esri’s ArcGIS Pro 3.1.2, and polygon attributes were recorded within a file geodatabase. All attributes were interpreted using the Suisun Marsh 2021 imagery as the base imagery. The photo interpreters obtained information primarily from the 2018 map and 2021 reconnaissance points, which were used during mapping to determine vegetative signatures and the appropriate mapping type for each polygon. Several other imagery sources were used as ancillary data, including 2021 NAIP, 2021 NAIP Color Infrared, all imagery available through Google Earth (including street view), and the 2018 NAIP imagery. Minimum mapping unit (MMU): Typically, the minimum mapping size is 0.25 acres. However, the photo interpreters use their best judgment to determine if a stand below 0.25 acre should be separately delineated. For example, a smaller polygon would be appropriate for any new visible occurrence of a non-native species of concern, such as Phragmites australis, Arundo donax, Carpobrotus edulis, Eucalyptus spp., and Lepidium latifolium. Minimum mapping width: There are many long and narrow polygons within the Suisun Marsh study area, most of which are roads, ditches, levees, and sloughs. The minimum mapping width is typically 10 feet; however, if small sections of a stand fell below the minimum width, the polygon was not split. More information can be found in the project report, which is bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3100_3199/ds3187.zip.

A PDF map that shows the Fire and Rescue Authorities in England and Wales as at April 2021. (File Size - 251 KB)

N100 Map data as of mid-December 2021. N100 Map data is cartographically adapted to scale 1:100 000. The data set covers mainland Norway limited by the national border with neighbouring countries and the territorial border in the sea.

The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 15 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 Radar and Sentinel-2 Optical Satellite images during the period late October 2020 – September 2021. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. The data has been split into the Ordnance Survey Ceremonial Counties and each county is given a three letter code. Please refer to the CROME specification document to see which county each CODE label represents.

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

Data focused on the regulation of midwives globally was collected for the 2021 State of the World's Midwifery Report by the International Confederation of Midwives, with support by Direct Relief, and can be accessed and downloaded in the Open Data Portal of the Global Midwives' Hub. This map supports the Regulation of Midwives Story map: https://directrelief.maps.arcgis.com/home/item.html?id=31e0b498ecc145e2b320481119a82d6eData collected on the state of midwifery regulation throughout the world for the 2021 State of the World's Midwifery Report. The data was collected via a survey that was sent to midwives' associations, who filled it out for their country and shared it with their Ministry of Health for validation. Data was collected by the International Confederation of Midwives with the support of UNFPA, WHO, and Direct Relief. This data visualization is just one of the many data products on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services.

A PDF map that shows the Combined Authorities and Regions in England as at December 2021. (File Size - 454 KB)

Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

Calculated trend values ​​for the horizontal position of the coastline in relation to the Base Coastline for the year 2021. Figures and maps are processed annually in the coastline map book published by RWS WVL. The supplementation schedule for the year 2 years after measurement is determined on the basis of this book. The trend in deviation of the position of the coastline to be tested (TKL) is shown in relation to the Base Coastline. In the underlying table all test parameters that are calculated by testing software MorphAn. The coloring indicates the direction of the trend (seaward/landward) and indicates the location of the tkl (seaward/landward).