Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

Cartography is the knowledge associated with the art, science, and technology of maps. Maps portray spatial relationships among selected phenomena of interest and increasingly are used for analysis and synthesis. Through digital cartography and web mapping, however, it is possible for almost anyone to produce a bad map in minutes. Although cartography has undergone a radical transformation through the introduction of digital technology, fundamental principles remain. Doing computer cartography well requires a broad understanding of graphicacy as a language (as well as numeracy and literacy). This course provides an introduction to the principles, concepts, software, and hardware necessary to produce good maps, especially in the context (and limitations) of geographic information systems (GIS) and the web.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of cartography projects to reinforce the material. Lastly, you will complete term projects. Please see the sequencing document for our suggestions as to the order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Geofabric Surface Cartography product provides a set of related feature classes to be used as the basis for the production of consistent hydrological cartographic maps. This product contains a geometric representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs, canals and other hydrographic features).

The product is fully topologically correct which means that all the stream segments flow in the correct direction.

This product contains fifteen feature types including: Waterbody, Mapped Stream, Mapped Node, Mapped Connectivity (Upstream), Mapped Connectivity (Downstream), Sea, Estuary, Dam, Structure, Canal Line, Water Pipeline, Terrain Break Line, Hydro Point, Hydro Line and Hydro Area.

Purpose

This product contains a geometric representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for the production of consistent hydrological cartographic map products, as well as the visualisation of surface hydrology within a GIS to support the selection of features for inclusion in cartographic map production.

This product can also be used for stream tracing operations both upstream and downstream however, as this is a mapped representation, streams may be represented as interrupted or intermittent features. In contrast, the Geofabric Surface Network product represents the same stream as a continuous connected feature, that is, the path that stream would take (according to the terrain model) if sufficient water were available for flow. Therefore, for stream tracing operations where full stream connectivity is required, the Geofabric Surface Network product should be used.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Geofabric Surface Cartography is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The source data input for the Geofabric Surface Cartography product is the AusHydro v1.7.2 (AusHydro) surface hydrology data set. The AusHydro database provides a seamless surface hydrology layer for Australia at a nominal scale of 1:250,000. It consists of lines, points and polygons representing natural and man-made features such as watercourses, lakes, dams and other water bodies. The natural watercourse layer consists of a linear network with a consistent topology of links and nodes that provide directional flow paths through the network for hydrological analysis.

This network was used to produce the GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3 of Australia (https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=66006).

Geofabric Surface Cartography is an amalgamation of two primary datasets. The first is the hydrographic component of the GEODATA TOPO 250K Series 3 (GEODATA 3) product released by Geoscience Australia (GA) in 2006. The GEODATA 3 dataset contains the following hydrographic features: canal lines, locks, rapid lines, spillways, waterfall points, bores, canal areas, flats, lakes, pondage areas, rapid areas, reservoirs, springs, watercourse areas, waterholes, water points, marine hazard areas, marine hazard points and foreshore flats.

It also provides information on naming, hierarchy and perenniality. The dataset also contains cultural and transport features that may intersect with hydrographic features. These include: railway tunnels, rail crossings, railway bridges, road tunnels, road bridges, road crossings, water pipelines.

Refer to the GEODATA 3 User Guide http://www.ga.gov.au/meta/ANZCW0703008969.html for additional information.

The second primary dataset is based on the GEODATA TOPO-250K Series 1 (GEODATA 1) watercourse lines completed by GA in 1994, which was supplemented by additional line work captured by the Australian National University (ANU) during the production of the DEM-9S to improve the representation of surface water flow. This natural watercourse dataset consists of directional flow paths and provides a direct link to the flow paths derived from the DEM. There are approximately 700,000 more line segments in this version of the data.

AusHydro uses the natural watercourse geometry from the ANU enhanced GEODATA 1 data, and the attributes (names, perenniality and hierarchy) associated with GEODATA 3 to produce a fully attributed data set with topologically correct flow paths. The attributes from GEODATA 3 were attached using spatial queries to identify common features between the two datasets. Additional semi-automated and manual editing was undertaken to ensure consistent attribution along the entire network.

AusHydro dataset includes a unique identifier for each line, point and polygon. AusHydro-ID will be used to maintain the dataset and to incorporate higher resolution datasets in the future. The AusHydro-ID will be linked to the ANUDEM streams through a common segment identifier and ultimately to a set of National Catchments Boundaries (NCBs).

Changes at v2.1

! New Water Storages in the WaterBody FC.

Changes at v2.1.1

! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



- Flow direction of Geometric Network set.

Processing steps:

1. AusHydro Surface Hydrology dataset is received and loaded into the Geofabric development GIS environment

2. feature classes from AusHydro are recomposed into composited Geofabric hydrography dataset feature classes in the Geofabric Maintenance Geodatabase.

3. re-composited feature classes in the Geofabric Maintenance Geodatabase Hydrography Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84)

4. feature classes from the Geofabric Maintenance Geodatabase hydrography dataset are extracted and reassigned to the Geofabric Surface Cartography Feature Dataset within the Geofabric Surface Cartography Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Cartography - V2.1.1. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/ce5b77bf-5a02-4cf8-9cf2-be4a2cee2677.

Learn the key factors to consider when planning a cartography project and preparing data that supports your map's purpose, audience, and format.

Foreground & background and Hierarchies pages from Cartography Period, by Kenneth Field, 2018. Graphics by Wesley Jones.These pages used with permission for How to make a map: A short course in Cartography from Learn ArcGIS.

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

The Cartographic Sign Detection Dataset (CaSiDD) comprises 796 manually annotated historical map samples, corresponding to 18,750 cartographic signs, like icons and symbols. Moreover, the signs are categorized into 24 distinct classes, like tree, mill, hill, religious edifice, or grave. The original images are part of the Semap dataset [1].

The dataset is published in the context of R. Petitpierre's PhD thesis: Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration [2]. Details on annotation, and statistics on annotated cartographic signs are provided in the manuscript.

Organization of the data

To come soon.

Descriptive statistics

Number of distinct classes: 24 + hapaxes
Number of image samples: 796
Number of annotations: 18,750
Study period: 1492–1948.

Use and Citation

For any mention of this dataset, please cite :

@misc{casidd_petitpierre_2025,
author = {Petitpierre, R{\'{e}}mi and Jiang, Jiaming},
title = {{Cartographic Sign Detection Dataset (CaSiDD)}},
year = {2025},
publisher = {EPFL},
url = {https://doi.org/10.5281/zenodo.16278381}}

@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

85% of the data were annotated by RP. The remainder was annotated by JJ, a master's student from EPFL, Switzerland.

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, Gomez Donoso D, Krisel B (2025) Semantic Segmentation Map Dataset (Semap). EPFL. https://doi.org/10.5281/zenodo.16164782
2. Petitpierre R (2025) Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration. PhD thesis. École Polytechnique Fédérale de Lausanne.

Abstract: In Brazil, the LDB - Law of Guidelines and Bases nº. 9394(Brazil, 1996) and the PCN - National Curricular Parameters, determines that the Geography discipline is recognized as autonomous and should not be understood as a complement to other disciplines. In this way, the improvement in Geography teaching passes through cartographic literacy. The focus is on offering the student the capacity to carry out the appropriation, analysis, reflection and criticism on geographical space. In this way, this paper presents a resource that consisted of the development of the application called MapVoice. The purpose of the software is to enable Blind or visually impaired students, from basic education, in the learning of Cartography in Geography classes. MapVoice provides the understanding and interpretation of physical environments transformed into thematic maps based on data from the 2010 Brazilian Demographic Census executed by IBGE. The software used sound and image resources developed for Windows environment. The research concludes that it is necessary to prepare the infrastructure of the schools for the reception of these students, but mainly the continuing training of teachers and teaching assistants. Mapvoice was tested at the Institute of the Blind for validation, achieving a satisfactory result and making enthusiasm for the development of new researches.

CONABIO provides online cartography through cartographic metadata distributed following the guidelines in the Standards for Digital Geospatial Metadata of FGDC-NBII (Federal Geographic Data Committee – National Biological Information Infrastructure), 1996. The cartographic information is queried through a database that is organized based on themes (biotic, physical and social aspects, regionalization and others), scales, and geographic area. The metadata content is presented as basic information, reports of the information (methodology) and spatial data information. The cartography is available online at no charge in distinct formats like: export file for Arc/Info (.E00) and shape file (ESRI), and DXF (Drawing eXchange Format). Maps is presented in cartographic projections: Lambert Conic Conformal, UTM and geographic coordinates system. GIF format of map images can be obtained as well.

This layer contains the following map information at a scale of 1.1000 for the municipalities of Navarra: communication routes, buildings, hydrography, themed soil, altimetry, unique buildings, mapped area, infrastructure networks and other place names. The list of municipalities mapped is in the following url: https://idena.navarra.es/downloads/List_of_municipalities_map_1000.pdf

<<< 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.

aAgricultural components (i.e. crop fields, pasture) were combined into one class for general comparisons across the broader land cover classes.

The topographic object database (BDOT10k) has been developed in a degree of detail corresponding to the map on a scale of 1:10 000. The BDOT10k information scope includes 9 categories of object classes, which include: water network, communication network, land armament network, land cover, buildings, buildings and facilities, land use complexes, protected areas, territorial division units and other facilities. Objects are saved in 73 classes of objects. The data set concerns the following category of object classes: Communication network, covering the area of Głubczycki district.

This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. Esri created this vector tile basemap from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. This version of the map is rendered using OSM cartography. The OSM Daylight map will be updated every month with the latest version of OSM Daylight data.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site:www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this enhanced vector basemap available to the ArcGIS user and developer communities.

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

The datasets are the training data for recognizing typical buildings shapeswith YOLO detection models.

Training data conditions under which the classification algorithms were tested.

The topographic object database (BDOT10k) has been developed in a degree of detail corresponding to the map on a scale of 1:10 000. The BDOT10k information scope includes 9 categories of object classes, which include: water network, communication network, land armament network, land cover, buildings, buildings and facilities, land use complexes, protected areas, territorial division units and other facilities. Objects are saved in 73 classes of objects. The data set concerns the following category of object classes: Other facilities, covering the area of Opole county.

The topographic object database (BDOT10k) has been developed in a degree of detail corresponding to the map on a scale of 1:10 000. The BDOT10k information scope includes 9 categories of object classes, which include: water network, communication network, land armament network, land cover, buildings, buildings and facilities, land use complexes, protected areas, territorial division units and other facilities. Objects are saved in 73 classes of objects. The data set concerns the following category of object classes: Buildings, buildings and facilities, covering the area of the Namysłów district.

The topographic object database (BDOT10k) has been developed in a degree of detail corresponding to the map on a scale of 1:10 000. The BDOT10k information scope includes 9 categories of object classes, which include: water network, communication network, land armament network, land cover, buildings, buildings and facilities, land use complexes, protected areas, territorial division units and other facilities. Objects are saved in 73 classes of objects. The data set concerns the following category of object classes: Network of land armament, covering the area of the powiat namysłowski.

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.Units: (Thematic dataset)Cell Size: 30mSource Type: ThematicPixel Type: Unsigned 8 bitData Projection: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: 50 US States, District of Columbia, Puerto RicoSource: National Land Cover DatabasePublication date: June 30, 2023Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America Albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into Web Mercator projection, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief.MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover.51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is