National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

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.

The Historical Map and Chart Collection of the Office of Coast Survey contains over 35000 historical maps and charts from the mid 1700s up through the 2020s, including the final cancelled editions of NOAA's raster charts. These images are available for viewing or download through the image catalog at https://historicalcharts.noaa.gov/. The Collection includes some of the nation's earliest nauti...

MethodThis dataset includes a detailed example for using our method (described in paper linked to below) to digitize historical land-use maps in R.MapsWe also release all of the Swedish land-use maps that we digitized for this project. This includes the Economic Map of Sweden (Ekonomiska kartan) over Sweden's 15 southernmost counties (7069 25 km2 sheets), plus 11 sheets of the District Economic Map (Häradsekonomiska kartan - but see http://bolin.su.se/data/Cousins-2015 for more accurate manual digitization).SvenskaHär kan du ladda ner 7069 Ekonomiska kartblad som vi digitaliserade över södra Sverige. En kort beskrivning av metoden publicerades i tidningen Kart & Bildteknik (se länk nedan).--UpdatesVersion 2: The digitized Economic Maps have been resampled so that they are all at a 1m resolution. In the original version they were all very close to 1m but not exactly the same, which made mosaicking difficult. This should be easier now. We now also link to the published paper in Methods in Ecology and Evolution.For more information, please see the readme file. For help or collaboration, please contact alistair.auffret@natgeo.su.se. If you use the data here in your work or research, please cite the publication appropriately.

Various historical maps from Vantaa. More detailed information about the different maps can be found in the Layers descriptions section.

The material can be viewed in the City of Vantaa map service:

• The levels can be found in the map service by searching for 'Other historical maps'.

Coordination system(s):

• ETRS-GK25 (EPSG:3879)

Addresses for cross-border services:

• WMS: https://gis.vantaa.fi/geoserver/wms?request=GetCapabilities
• Description of the interface service: https://gis.vantaa.fi/interfaces/

Published levels:

• history:land species map_mv
• history:vantaantilat_1983
• history:Tikkurila_1950_construction plan
• history:longitudinal map_1933
• history:longitudinal map_1749
• history:Senate map_1872
• History: King's Map

Level descriptions

Name: Land species map (black and white)

In the soil species map, the soil is mapped to a depth of about one metre. For soil layers deeper than this, the soil type map does not provide information. The mapping of soil types has been carried out on a scale of 1:2,000 or 1:10,000, so the smallest soil types have not been presented. The soil species do not change unambiguously at the boundary line shown on the maps, but rather the boundary line represents the zone of change of soil species.

Timeliness of data: The soil species map was made mainly in the 1980s and represents the conditions of that time. The map has not been updated.

Explanation of abbreviations in the country map:

• Ka or dark raster = rock covered by an open or thin layer of loose soil
• Mr = moraine soil species
• Sr = gravel
• Hk = sand
• Si = silt (previously silt/sand)
• Sa = clay
• Lj = mud
• Tv = peat
• T = area where excavation or filling has taken place

Double marking means that the topsoil species changes to another at a depth of less than one metre from the ground surface. For example, Hk/Sa means that the surface layer is sand to a depth of no more than 0.9 m and the soil is clay to a depth of 1 m.

Name: Facilities in Vantaa

The dataset includes Vantaa's premises according to the time of 1983. In areas where there is no local detailed plan, construction is regulated by the local master plan. The provisions of the master plan tie the number of dwellings to the surface area of the premises at the time of the adoption of the 1983 master plan (6.6.1983). The regulations apply to the detached house areas A4, village areas AT, agricultural areas MT and areas M, which are dominated by agriculture and forestry.

Name: Construction plan for Tikkurila 1950

Map of the construction plan area of Tikkurila in the villages of Tikkurila, Suutarinkylä and Hakkila in the city of Helsinki and in the rural municipality of Uusimaa.

Construction plan surveyor Niilo Tarkka, surveyor in 1937-47. The survey was completed in 1947-50 by surveyor J. Rauniomäki.

Name: Keeper's Map 1933

The National Land Survey of Finland prepared the parish map 1:20 000 between 1825 and 1950. The production took place by parish in 1825–1915 and by map sheet in 1916–1950. From 1927 onwards, the parish maps were published as 10 km x 10 km magazines in the so-called general magazine division.

Name: Keeper map 1749

Friedrich Johan Fonseen's map of the parish of Helsinki from 1749.

Source: Krigsarkivet (Sverige) - War Archive (Sweden). The city's spatial data team has put it into the current coordinate system.

Name: Senate map 1872

The map is based on surveys made by the topographical department of the Russian Ministry of War in 1870-1907 at a scale of 1:21,000 about the southern part of the Pori-Käkisalmi line.

Name: King's Map

The King's Atlas 1776-1805

In 1776-1805, an extensive military survey, the so-called recognition survey, was carried out in Finland. According to the work instructions, the maps had to be drawn up so accurately that no militarily significant terrain would be overlooked. With the help of maps, the warlord had to be able to plan both offensive and defensive actions without knowing the terrain. As a result, the maps depict e.g. roads (including winter roads), water routes, rustolles, crofts and vicarages.

The maps are based on older geometric maps, in which the mappers both supplemented and corrected the data while working in the field. In addition, the mappers have in some cases used the help of local residents to find out the roads, terrain and the name of the locality. The language used in the map is Swedish. The original hand-drawn maps are stored at the Swedish State Archives in Stockholm.

Source: Krigsarkivet, Finska rekognosceringsverket.

Dataset based on maps presented in the "Annales" and "Past & Present" journals, between 1950 and 2000, focusing on motion maps.The data are a set of organized maps but does not include the maps themselves (only data on the maps), which are in two different repositories: www.jstor.org and persee.fr. Every map registered in the table indicates a URL where the original map is available freely, at persee.fr, and by request at jstor.org.

This data publication contains multiple maps of Puerto Rico scanned at 600 dots per inch: full map scans, scans clipped to mapped areas only, and georeferenced scans of 1:10,000-scale land-use maps from 1950-1951 that were produced by the Rural Land Classification Program of Puerto Rico, a project led by Dr. Clarence F. Jones of Northwestern University. These historical maps classified land use and land cover into 20 different classes, including 13 different types of crops, two classes of forests, four classes of grasslands and other areas, which is a general class for non-rural areas. This package includes maps from 76 out of the 78 municipalities of Puerto Rico, covering 422 quadrangles of a 443-quadrangle grid for mainland Puerto Rico. It excludes the island municipalities of Vieques and Culebra, Mona Island and minor outlying islands.The Rural Land Classification Program of Puerto Rico produced 430 1:10,000-scale maps. That program also produced one island-wide land-use map with more generalized delineations of land use. Previously, Kennaway and Helmer (2007) scanned and georeferenced the island-wide map, and they converted it to vector and raster formats with embedded georeferencing and classification. This data publication contains the higher-resolution maps, which will provide more precise historical context for forests. It will better inform management efforts for the sustainable use of forest lands and to build resilience and resistance to various future disturbances for these and other tropical forest landscapes.

The maps were scanned and georeferenced to help with the planning and application process for the USDA Forest Service (USDA) Forest Legacy Program, a competition-based program administered by the USDA Forest Service in partnership with State agencies to encourage the protection of privately owned forest lands through conservation easements or land purchases. Geospatial products and maps will also be used by personnel at the Department of Natural and Environmental Resources and partners in Non-Governmental Organizations working with the Forest Stewardship Program. This latter program provides technical assistance and forest management plans to private landowners for the conservation and effective management of private forests across the US. The information will provide local historical context on forest change patterns that will enhance the recommendations of forest management practices for private forest landowners. These data will also be useful for urban forest professionals to understand the land legacies as a basis for planning green infrastructure interventions.

Data depict the rural areas of Puerto Rico around 1951 and how they were classified by geographers then. Having it georeferenced allows managers, teachers, students, the public and scientists to compare how these classifications have changed throughout the years. It will allow more precise identification and mapping of the past land use of present forests, forest stand age, and the past juxtaposition of different land uses relative to each other. These factors can affect forest species composition, biodiversity and ecosystem services. Forest stand age, past land-use type and past disturbance type, forest example, help gauge current forest structure, carbon storage, or rates of carbon accumulation. Another example of how the maps are important is for understanding how watersheds have changed through time, which helps assess how forest ecosystem services related to hydrology evolve. These maps will also help gauge how the forests of Puerto Rico are responding to recent disturbances, and how past disturbances over a range of scales relate to these responses.For more information on the Rural Land Classification Program of Puerto Rico, generated maps, and the island-wide land-use map, please see Jones (1952), Jones and Berrios (1956), as well as Kennaway and Helmer (2007).

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows a series of redrawn reproductions of historical maps. The first three maps deal with the eastern interior of Canada, the first of them showing the work of Champlain, Canadas first great explorer. His work was extended into the Great Lakes, often by missionaries such a Dollier and Galinee, and later recorded by Franquelin, the first great map compiler of Canada. The next three maps are concerned with the western interior where, at first, geographical knowledge was very conjectural. This is indicated on the combined maps of Delisle and Buache - the western portion being the work of Delisle and the eastern portion and the inset the work of Buache. But gradually, as the fur-trading companies extended their operations, more precise knowledge was obtained of the area between Hudson Bay, the Rocky Mountains and the Beaufort Sea, as the Pond map of 1787 illustrates. Later, geographical knowledge was gained of the Cordillera itself which extended to the shores of the Pacific Ocean. The map of 1814 is a faithful portrait of the west by the great pioneer surveyor David Thompson. By Confederation, what is now Canada was mapped from sea to sea, although to show this adequately, two maps of the period have been combined. But while broad outlines of Canada were being drawn, detailed topographical surveys were also being made in the settled areas. The last two maps on this plate are examples of this. The Delabat map is part of the Plan du Cours de la Riviere du Dauphin, et du Fort du Port Royal a la Cadie, that is, the area around Annapolis Royal and Annapolis River, Nova Scotia, of today. The Duberger map is part of a Plan of the Town and Fortifications of Quebec.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The UrbanOccupationsOETR_Generalkarte_DCNN_dataset is the dataset that contains sample material for Road Type Automatic Feature Extraction analysis based on the Generalkarte Historical Transport Map. We provide 500 images per road type divided into a separate training and validation folder. In addition, all images and labels are divided by their respective labelled road type sub-category. If you would like to use the dataset below in further publication, please use the credentials specified below: Can, Yekta Said, Petrus Johannes Gerrits, and M. Erdem Kabadayi, ‘Automatic Detection of Road Types From the Third Military Mapping Survey of Austria-Hungary Historical Map Series With Deep Convolutional Neural Networks’, IEEE Access 9 (2021): 62847–56, https://doi.org/10.1109/ACCESS.2021.3074897. Please contact mkabadayi@ku.edu.tr for questions regarding the DCNN dataset.

These are military topographic maps (scale 1:25,000) from the years 1944, series GSGS 4427 and GSGS 4414 (GSGS = Geographical Section General Staff). During the Second World War, maps of strategically located areas in the occupied territories were produced on the initiative of the American Army Map Service (AMS) in Washington DC and the British War Office in London. The work of the military services includes maps of cities and map series of France, Belgium and the Netherlands, among others. This includes the map series "Holland, 1:25.000" which was known to the Americans under the code AMS M831 and to the British under the code GSGS 4427. The 215 sheets in series GSGS 4427 contain most of the Netherlands, and were published in 1943. , 1944 or 1945 printed. From series GSGS 4414 there are 263 maps of the eastern part of the Netherlands and a large part of Germany. Older sheets available in Washington DC and London were used to make the maps. Sometimes it was even necessary to refer to information printed by the Dutch Topographical Service from the end of the nineteenth century. If the Allies had more recent magazines, they were of course used. In most cases, information was taken from map sheets from the 1920s and 1930s. In addition, information was also taken from, for example, Michelin maps and map sheets of the Koninklijke Nederlandsche Automobiel Club (KNAC).

<<< This dataset is not released yet. Release date: 1st September, 2025. >>>

The Semantic Segmentation Map Dataset (Semap) contains 1,439 manually annotated map samples. Specifically, the dataset compiles 356 image patches from the Historical City Maps Semantic Segmentation Dataset (HCMSSD, [1]), 78 samples extracted from 19th century European cadastres [2–4], three from Paris city atlases [5], and 1,002 newly annotated samples, drawn from the Aggregated Dataset on the History of Cartography (ADHOC Images, [6]).

Additionally, it comprises 12,122 synthetically generated image samples and related labels.

Both datasets are part of the R. Petitpierre's PhD thesis [7]. Extensive details on annotation, and synthetical generation procedures are provided in the context of that work.

Organization of the data

To come soon.

Descriptive statistics

Number of semantic classes: 5 + background
Number of manually annotated image samples: 1,439
Number of synthetically-generated samples:
Image sample size:
min: 768 × 768 pixels
max: 1000 × 1000 pixels

Use and Citation

For any mention of this dataset, please cite :

@misc{semap_petitpierre_2025,
author = {Petitpierre, R{\'{e}}mi and Gomez Donoso, Damien and Kriesel, Ben},
title = {{Semantic Segmentation Map Dataset (Semap)}},
year = {2025},
publisher = {EPFL},
url = {https://doi.org/10.5281/zenodo.16164782}}

@phdthesis{studying_maps_petitpierre_2025,
author = {Petitpierre, R{\'{e}}mi},
title = {{Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration}},
year = {2025},
school = {EPFL}}

Corresponding author

Rémi PETITPIERRE - remi.petitpierre@epfl.ch - ORCID - Github - Scholar - ResearchGate

Work ethics

80% of the data were annotated by RP. The remainder were annotated by DGD and BK, two master's students from EPFL, Switzerland. The students were paid for their work using public funding, and were offered the possibility to be associated with the publication of the data.

License

This project is licensed under the CC BY 4.0 License.

Liability

We do not assume any liability for the use of this dataset.

References

1. Petitpierre, R. (2021). Historical City Maps Semantic Segmentation Dataset. V1.0. https://doi.org/10.5281/zenodo.5513639
2. di Lenardo I, Barman R, Pardini F, et al. (2021) Une approche computationnelle du cadastre napoléonien de Venise. Humanités numériques 3.
3. Petitpierre R, Rappo L and di Lenardo I (2023) Recartographier l’espace napoléonien. In: Humanistica 2023, Genève, Switzerland, June 2023. Géographie. Association francophone des humanités numériques. Available at: https://hal.science/hal-04109214.
4. Li S, Cerioni A, Herny C, et al. (2024) Vectorization of historical cadastral plans from the 1850s in the Canton of Geneva. Geneva, Switzerland: Swiss Territorial Data Lab. Available at: https://tech.stdl.ch/PROJ-CADMAP/.
5. Chazalon J, Carlinet E, Chen Y, et al. (2021) ICDAR 2021 Competition on Historical Map Segmentation. arXiv:2105.13265 [cs].
6. To come soon
7. Petitpierre R (2025) Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration. PhD thesis. École Polytechnique Fédérale de Lausanne.

The area of the Ville in western Germany is of particular importance for studying anthropogenic induced relief changes, as it belongs to the largest and oldest historic lignite mining areas worldwide. Comparison of topographic data from the first geodetic mapping in 1893 to 2015 allows the quantification of relief changes in a completed example of a post-mining landscape. The dataset "Digital Elevation Model "Ville" from 1893" is computed based on the digitized contour lines of the historic map Preußische Neuaufnahme, which is the first geodetic mapping in the area. The DEM has a spatial resolution of 30 m.

Inventory of historical architecture. Historical architecture refers to 'immovable property'. Examples are: farms, houses, factories, mills, churches and castles. But also: bridges, border markers, statues, etc. The Cultural-Historical Values ​​Map was finalized on September 26, 2006 by GS van Noord-Brabant. Criteria for inclusion on the Cultural-historical value map are scientific and/or cultural-historical significance, beauty and age. For the age criterion, a connection has been sought with the period of 50 years, which is also included in the Monuments and Historic Buildings Act.

This is a copy of another layer - see original source: https://www.arcgis.com/home/item.html?id=e02b85c0ea784ce7bd8add7ae3d293d0OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimetersFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send emailLayers

Flood maps calculated from space-borne remote sensing Synthetic Aperture Radar (SAR) VV backscatter data during the extreme hydro-meteorological events occurred along the Panaro River . Sentinel 1/TerraSarX SAR data has been processed by a method combining thresholding and segmentation (CThS method). The main idea of CThS is to find some samples which are definitely seeds of the flood water areas. In doing so, a statistical measure of randomness, i.e. entropy filtering, is applied to characterize the texture of the input image. It tries to find locally some pixels, which contain the entropy values of the 3-by-3 neighborhood around the corresponding pixel in the input image. What the local filtering identifies is areas with a significant difference with the surrounding areas. These areas could contain different ground targets, which have the same signature as water. Then histogram thresholding is performed. The histogram of all pixels extracted by filtering is reasonably bimodal so that a suitable threshold value can be determined by fitting a curve to the histogram to separate water and non-water pixels. Having separated water seed points, an active contour segmentation method is used to delineate the full flood extent. The dataset contains flood maps for the dates: 12 and 13 December 2017 and water maps on 20 January 2014 and 8 December 2020. You are not authorized to view this dataset. You may email the responsible party OPERANDUM to request access. Flood maps of historical flood events (Panaro)

At the end of the 90s a collaboration between the Institute for Artistic, Cultural and Natural Heritage and the Geographic Information Systems Service of the RER allowed the construction of a historical-regional cartography derived from the pre-unification productions from 1828 to 1853. The regional territory it is largely covered by the Austrian Topographic Map (scale 1:86.400) and, for a limited portion, by the Topographic Map of the mainland states of His Majesty the King of Sardinia of 1853 (scale 1:50.000). The Austrian Topographical Map is actually made up of various cartographies made in several stages, all homogeneous in scale, design and symbols, which can be perfectly assembled in a single grid, and are: Topographical Map of the Duchies of Parma, Piacenza and Guastalla from 1828; Charter of the Kingdom of Lombardy-Venetia of 1833; Topographic Map of the Duchy of Modena and Reggio of 1849; Topographic map of the Papal State and the Grand Duchy of Tuscany from 1851. The mosaic of the various cartographies was scanned at 1016 dpi, georeferenced through the recognition of trigonometric points and known points, finally subdivided according to the size of the modern 1:50,000 sheets of the Military Geographical Institute. he legends are quite similar and comparable to each other, the roads and settlements are rigorously classified; administrative boundaries define scopes up to the district; all the parish churches, oratories and chapels, mills, post stations, quarries and mines, fountains and springs are reported; bridges, fords and river crossings are classified. Agricultural activity is identified through very detailed conventional signs. The orography is dotted with zenithal highlights; the quoted points are very limited. The four sheets that make up this printed example are taken from the Charter of the Papal State and the Grand Duchy of Tuscany of 1851.

Raster files corresponding to all the editions of each map of the regional series, from the scan of the paper maps kept in the IGN Map Library. The distribution unit is a zip file per autonomous community, which includes three types of files: jpg without georeferenced with 250 dpi resolution; ecw georeferenced with 400 ppp resolution (in longitude and latitude geographic coordinates, without map projection) and prj containing the georeferencing data. The name structure of each file is as follows: Autonomy-Year-yyyy.jpg. The last three letters of the file name respond to the following code: c.- Grid; e.- Special Edition; r.- Reprint; s.- Shading; g.- Map of the war; n.- There is no attribute. For example: cns corresponds to a map that has a grid, is not a special edition, and has shading.

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

no abstract provided