Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

This research paper presents the design and development of an indigenous low cost Mobile Mapping System (MMS) for urban surveying applications. The MMS is comprised of economical Hokuyo-30LX 2D laser scanners, vision sensors, Global Positioning System (GPS) and various odometric sensors that can be installed on car like moving platform. The run time sensorial data is interfaced, processed and recorded using Robot Operating System (ROS). The live laser scan is utilized for the pose estimation using Simultaneous Localization and Mapping (SLAM) technique. In absence of valid SLAM estimation and frequent GPS outages, a multimodal sensor fusion framework for the enhanced pose correction has been developed using Kalman Filter (KF) by incorporating the Inertial Measurement Unit (IMU) and wheel odometric data along with SLAM and GPS data. The corrected pose is utilized for the 3D point cloud mapping by incorporating laser scans perceived periodically from various 2D laser scanners mounted on the MMS. The custom-made installation scheme has been followed for mounting three 2D laser scanners at horizontal, vertical and inclined orientations. The efficacy of the developed map has employed for extraction of road edges and associated road assets by establishing the lucrative classification technique of the point cloud using Split and Merge segmentation and Hough transformation. The surveying to map development time has significantly reduced and the mapping results have found quite accurate when matched with the ground truths. Furthermore, the comparison of the developed maps with ground truths and GIS tools reveals the highly acceptable accuracy of the generated results which have found very nearly aligned with the actual urban environment features. In comparison to the existing global MMS variants, the presented MMS is quite affordable solution for limited financial resourced business entities.

We have generated 2D dust extinction maps with spatial resolutions ranging from 30 arcsec to 300 arcsec using the UKIDSS/GPS photometric catalog. These maps cover the entire about 1800 deg2 area of the Galactic plane surveyed by UKIDSS/GPS. The maps were produced utilizing the XPNICER technique, an advancement of the previous PNICER and Xpercentile methods. Here we released all extinction maps and associated uncertainty and number density maps at different spatial resolutions and X0 configurations.

ALK — Automated Property Map in Saarland The ALK is the digitally-run property map, also known as the hallway or cadastral map, as a representing part of the real estate register. This is a nationwide and leaf-free representation of the geometry, location and shape of the parcels/lots and buildings with uniform spatial reference: Other objects include land use, trade names, street names, house numbers, classification of road and water bodies, public regulations such as water, nature and monument conservation areas, surveying and border points. Coordinate dimension: 2D (no heights) Coordinate unit: Meters, even-cartographic.

Description of the INSPIRE Download Service (predefined Atom): ALK - Automated Property Map in the Saarland The ALK is the digitally managed property map, also called floor or cadastral map, as a representative part of the property cadastre. It is a nationwide and sheet-free representation of the geometry, location and shape of the plots / plots and buildings with a uniform spatial reference: Other objects include land use, names, street names, house numbers, classification of roads and waters, public regulations such as water, nature and monument protection areas, surveying and border points. Coordinate dimension: 2D (no heights) Coordinate unit: Meters, even-cartographic. - The link(s) for downloading the dataset(s) is/are dynamically generated from a metadata set

This map shows the subcrop pattern of the Mesozoic rock units present at the top Mesozoic unconformity (also commonly referred to as the base Tertiary unconformity) in Cook Inlet basin, Alaska. The subcrop is projected onto the top Mesozoic unconformity depth surface of Cook Inlet basin, Alaska, published by Shellenbaum and others (2010). Publicly available geologic and geophysical data from multiple sources were collected, interpreted, and integrated into the subcrop map. Formation picks at the top Mesozoic unconformity were determined for 109 wells. Mesozoic horizons from two regional marine two-dimensional (2-D) seismic datasets (approximately 3,300 miles) were interpreted. Eight map units were established for the Mesozoic subcrop map: Kaguyak-Matanuska Formations, undivided; Naknek Formation; Chinitna Formation-Tuxedni Group, undivided; Talkeetna Formation; Talkeetna-Border Ranges ultramafic and mafic complex (BRUMC), undivided; Pogibshi-Port Graham formations, undivided; plutonic rocks, undivided; and metamorphic rocks, undivided. This map was prepared as part of a multi-year effort by the Alaska Department of Natural Resources.

For 36 galaxies within the MUSE Atlas of Disks (MAD) survey (Erroz-Ferrer et al. 2017), the results of using HIIdentify (hiidentify.readthedocs.io/) on the Halpha maps to identify HII regions within the galaxies are provided, as presented in Easeman et al. (2023). The fits files have 4 extensions: 1) 2D map where each pixel has a Region ID for the HII region within the galaxy to which it belongs. 2) 2D map of the distance of each pixel from the pixel designated as the peak of that HII region, in kpc. 3) 2D map showing the pixels designated as the peak of each HII region, with the associated Region ID. 4) Ha map for pixels belonging to an identified region within the galaxy.

This map showcases a 2D sea level change feature layer can be transformed to 3D with the sea level increase value extruded to the average elevation of each line segment. The original CanCoast 2099 sea level change polyline is divided into segments of ~300 meters from the longer segments. 200 meters landward buffer was created from the modified polyline feature. Sea level increase value is extruded to the 2D polygon to the lowest elevation. This map is for demonstration rather than scientific research purposes. Data Sources:coastline 200m buffer minHeightmodified from Manson, G.K., Couture, N.J., and James, T.S., 2019. CanCoast Version 2.0: data and indices to describe the sensitivity of Canada's marine coasts to changing climate; Geological Survey of Canada, Open File 8551, 1 .zip file. https://doi.org/10.4095/314669EsriOpenStreet 3D buildings: https://www.arcgis.com/home/item.html?id=ca0470dbbddb4db28bad74ed39949e25WorldElevation3D/Terrain3D https://elevation3d.arcgis.com/arcgis/rest/services/WorldElevation3D/Terrain3D/ImageServer Topographic basemapthumbnail image: https://irc.inuvialuit.com/about-irc/community/tuktoyaktuk

This map showcases a 2D sea level change feature layer can be transformed to 3D with the sea level increase value extruded to the average elevation of each line segment. There is expected to be a vertical difference between the base elevation of line segments and that of the 3D terrain. In order to get more accurate visualization, higher resolution data are needed. This map is for demonstration rather than scientific research purposes. Data Sources:CanCoast Sea Level Change towns 3D scenemodified from Manson, G.K., Couture, N.J., and James, T.S., 2019. CanCoast Version 2.0: data and indices to describe the sensitivity of Canada's marine coasts to changing climate; Geological Survey of Canada, Open File 8551, 1 .zip file. https://doi.org/10.4095/314669EsriOpenStreet 3D buildings: https://www.arcgis.com/home/item.html?id=ca0470dbbddb4db28bad74ed39949e25WorldElevation3D/Terrain3D https://elevation3d.arcgis.com/arcgis/rest/services/WorldElevation3D/Terrain3D/ImageServer Topographic basemapthumbnail image: https://irc.inuvialuit.com/about-irc/community/tuktoyaktuk

Map Service showing geographic extents of geophysical data sets maintained by the Department of Resources. The data sets are organised by layers including:Airborne Geophysical Survey (0)Airborne Exploration Survey - Available (1)Airborne Exploration Survey - Unavailable (2)Airborne Open Range Survey (3)Airborne Multi-client Survey (4)Airborne State Survey (5)Airborne Federal Survey (6)Gravity Survey (7)Gravity Base Station (8)Gravity Data Station (9)Regional Gravity Survey (10)Hyperspectral Survey (11)Magnetotelluric Survey (12)Seismic Survey (13)Seismic Survey 2D (14)Seismic Survey 3D (15)Seismic Survey Deep (16)Seismic Survey C Horizon (17)Qld Gravity Image (18)Qld Magnetic Image (19)Qld Radiometric Image (20)

Report of Investigation 2018-6, Geologic map of the Umiat-Gubik area, central North Slope, Alaska, provides a 1:63,360-scale geologic map of the hydrocarbon-bearing Umiat-Gubik area of the central North Slope, Alaska, spans approximately 2,100 km2 at the northern extent of the Brooks Range foothills fold-and-thrust belt in the Colville foreland basin. This geologic map was prepared through assimilation of field observations, aerial and satellite imagery, seismic-reflection data, and well logs. Near-surface formation picks were available or derived for most of the area's 24 exploration wells, and two cross-sections were constructed along lines of section that are constrained at depth by our interpretations of publicly available two-dimensional seismic data. The mapped area hosts exposures of Upper Cretaceous strata in the Nanushuk, Seabee, Tuluvak, Schrader Bluff, and Prince Creek Formations, constituting an approximately 2-km-thick succession that crops out discontinuously in the low-relief, tundra-mantled region. This part of the siliciclastic Brookian megasequence stratigraphy comprises principally shallow-marine deposits. Our work benefits from and reflects recent sequence-stratigraphic advances that better constrain how this part of the Colville basin continued to fill by a northeastward prograding clastic wedge during Late Cretaceous time, with the exposed stratigraphy recognized as basin-scale topset units. A series of east- to east-southeast-trending, km-scale wavelength, gentle folds are mapped in the area. Anticlines are locally breached by thrusts and interpreted to be folded above faulted and penetratively deformed mid-Cretaceous Torok Formation. Undeveloped, sub-commercial (as of this writing) petroleum accumulations occur along doubly plunging anticlinal traps at three long-recognized fields in the map area: Umiat (mostly oil), Gubik (gas), and East Umiat (gas). The Umiat oil field structural culmination is modified by thrust faults that breach the surface, and the East Umiat gas field is associated with a north-dipping back-thrust that is evident in seismic data and cuts across the Upper Cretaceous stratigraphy; thrust faults near the Gubik gas field lie within and below the Torok Formation. Various interpretations have previously been published for some of the area's structures, with important implications for petroleum trap geometries in the gas-prone foothills region. We present new data and interpretations that support the inference of a principal, south-dipping thrust fault that breaches the north limb of Umiat anticline near Umiat. The complete report, geodatabase, and ESRI fonts and style files are available from the DGGS website: http://doi.org/10.14509/30099.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This dataset contains 248 benthic habitat maps, that were created from 31 underwater hyperspectral images captured with the HyperDiver device in 8 reef sites across the western coastline of Curacao (see https://doi.org/10.3390/data5010019 for information on the acquisition of the transects). The maps were produced by 8 combinations of two semantic labelspaces (detailed and reefgroups), two machine learning classifiers (patched and segmented), and two spectral signals (radiance and reflectance). Maps in the detailed labelspace have each pixel assigned to one of 43 labels, which are taxonomic labels at family, genus and species levels for biotic components of the reef (corals, sponges, macroalgae, etc.), as well as substrate labels (sediment, cyanobacterial mats, turf algae) and survey material labels (transect tape, reference board, etc.). The set of maps in the reefgroups labelspace cluster the labels in the detailed labelspace into 11 classes that describe reef functional groups (i.e. corals, sponges, algae, etc.). All habitat maps were produced with high accuracy (Fbeta 87%), by two different machine learning methods: a random forest ensemble classifier (segmented method) and a deep learning neural network classifier (patched method). The maps are further divided by the signal type from the hyperspectral image that was used, either radiance or reflectance (the latter was calculated with a reference board located at the beginning and end of each transect). These benthic habitat maps can be used to obtain accurate descriptions of the benthic community and habitat structure of coral reef sites in Curacao. The dataset also contains: an assessment of the accuracy and data efficiency of the machine learning methods, a consistency assessment of the mapped regions, a comparison of habitat metrics (class coverage, biodiversity indices, composition and configuration) between habitat maps produced by each method, and an effort-vs-error analysis of sparse sampling techniques on the densely classified maps. The files for the habitat maps are in the form: habitat_maps_dataset/habitat_maps/transects/transect_/habitat_map.where the parameters can have the following values:num: 005, 006, 019, 024, 026, 028, 031, 043, 044, 046, 054, 080, 081, 082, 084, 085, 086, 090, 091, 095, 097, 102, 107, 114, 118, 125, 129, 130, 132, 134, 141labelspace: detailed, reefgroupsspectrum: radiance, reflectancemethod: patched, segmentedext: nc or jpgFor each transect, the following files are available:habitatmap_.nc in netCDF4 format: contains the habitat map data for the given combination of semantic labelspace, signal type (or spectrum) and machine learning method used. The map data contains a 2D (Y, X) dataarray classmap which has an integer in each position. The integers are a code for each class. To decode the class integers into the class labels, a lookup table for each labelspace is provided in the attributes 'label' and 'label_id' of the data array for each class map.habitat_map_.jpg: an image file that visualizes the habitat map with a corresponding color for each class.

BOEM's deepwater Gulf of Mexico bathymetry grid was created by mosaicing over 100 3D seismic surveys. XY grid size is 40ft and depth is in feet. Hillshaded rasters showcase the dynamic bathymetric relief of the seafloor and were created using parameters of sun direction from due North at an angle of 45 degrees, with vertical exaggeration of 5. Pyramid files in ArcMap have been generated to allow users to quickly visualize the data at varying scales.Depth is accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200ft) and maximum (-11,000ft) depth extents, due to the nature of the depth transformation method used. The Bureau of Ocean Energy Management makes publically available a new deepwater bathymetry grid of the northern Gulf of Mexico, created by utilizing 3D seismic data which covers more than 90,000 square miles. The grid provides enhanced resolution compared to existing public bathymetry maps over the region, delivering 10 to 50 times increased horizontal resolution of the salt mini-basin province, abyssal plain, Mississippi Fan, and the Florida Shelf/Escarpment. To create the grid the seafloor was interpreted on over one-hundred 3D seismic time-migrated surveys, then mosaicked together and converted to depth in feet. The grid consists of 1.4 billion, 40-by-40 ft defined cells covering water depths –130 to –11,087 ft (–40 to –3,379 m). The average error is calculated to be 1.3 percent of water depth.BOEM has the responsibility of issuing permits for the acquisition of geophysical data in U.S. Federal waters as designated under the Outer Continental Shelf (OCS) Lands Act. Regulations at 30 CFR 551 allow BOEM to obtain a digital version of any post-processed, post-migrated two-dimensional (2D) and three-dimensional (3D) seismic survey acquired within the OCS. BOEM now maintains a confidential library of approximately 1,700 time and depth 2D/3D seismic surveys for the Gulf of Mexico (GOM), with survey vintages dating back to the early 1980s. These data provide geoscientists a world-class repository of subsurface digital data to interpret and utilize in achieving our regulatory missions.Since 1998, BOEM has used the largest, highest quality 3D time surveys to interpret the seafloor. Time surveys were used because the primary objective was not bathymetry but to identify seafloor acoustic amplitude anomalies indicative of authigenic carbonate hardgrounds and natural hydrocarbon seepage; those areas which may be suitable habitat for communities of chemosynthetic, coral, and other benthic organisms [Roberts, 1996, Roberts et al., 1992 and 2000]. The acoustic amplitude response of the seafloor is better resolved in time-migrated surveys rather than depth-migrated, allowing for increased accuracy in the identification of potential benthic habitats and seeps. While this new bathymetry grid does not include acoustic amplitude data for the seafloor, BOEM does publish polygon shapefiles which outline areas of anomalously high and low seafloor acoustic reflectivity, which can be downloaded at www.boem.gov/Seismic-Water-Bottom-Anomalies-Map-Gallery.Roberts, H.H., (1996), Surface amplitude data: 3D-Seismic for interpretation of seafloor geology (Louisiana slope): Gulf Coast Association of Geological Societies Transactions, v. 46, p. 353–362.Roberts, H.H., D.J. Cook, and M.K. Sheedlo, (1992), Hydrocarbon seeps of the Louisiana continental slope: Seismic amplitude signature and seafloor response: Gulf Coast Association of Geological Societies Transactions v. 42, p. 349–362.Roberts, H.H., J. Coleman, J. Hunt Jr., and W.W. Shedd, (2000), Surface amplitude mapping of 3D-seismic for improved interpretations of seafloor geology and biology from remotely sensed data, Gulf Coast Association of Geological Societies Transactions, v. 50, p. 495–503.

The PA Department of Conservation and Natural Resources (DCNR) and PA Game Commission (PGC) have teamed up to create an interactive map specifically for hunters. Collectively, State Forest Land and Gamelands comprise over 3.7 million acres of public forest open to hunting in Pennsylvania. Hunters can use this map to:View public forests open to hunting.Search hunting seasons and bag limits across different parts of the state.Display hunting hours (starting/ending times) across different parts of the state.Add personal GPS data to the map (waypoints and tracklogs).View different types of wildlife habitat across public forest lands, including mature oak forests, meadows, food plots, openings, winter thermal (coniferous) cover, and young aspen forest.See where recent timber harvests have occurred on public forest lands.Get deer management assistance program (DMAP) information for state forest lands.Add map layers associated with chronic wasting disease (CWD).Identify where bear check stations are located and get driving directions.Display the elk hunting zones and get information about them.Get the location of gated roads opened for hunters on public forest lands and when those gates will be opened.Analyze graphs and trends in antlerless/antlered deer harvests and antlerless license allocations from 2004 to the present.