Includes: Storm Assets

The Sonoma County fine scale vegetation and habitat map is an 83-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. This layer package includes the symbology used for cartography.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

ESRI layer of bathymetry of Glacier Bay that is used as a boundary layer for restricting movement of sea otters to marine environments and for limiting inference to Glacier Bay. Covariates including depth, distance to shore, slope of ocean floor, and shoreline complexity are derived from the bathymetry shapefile. The detailed content is defined in SOP 20 of the protocol package.

Proposed Action route network for the Kingman Field Office Travel Management Plan.

Wellington City 3D Buildings, captured 5 May 2017. To assist in download and processing the coverage area has been divided into a number of sub areas. The following links are to esri .slpk files. Further formats will become available as the files are converted. Kaiwharawhara - scene layer package - scene layer Lower Wadestown - scene layer package - scene layer Pipitea North - scene layer package - scene layer Pipitea South - scene layer package - scene layer Thorndon East - scene layer package - scene layer Thorndon North West - scene layer package - scene layer Kelburn North scene layer package - scene layer Kelburn South scene layer package - scene layer CBD North scene layer package - scene layer CBD South scene layer package - scene layer Aro Valley scene layer package - scene layer Te Aro West scene layer package - scene layer Te Aro Centre scene layer package - scene layer Te Aro East scene layer package - scene layer Mt Victoria South scene layer package - scene layer Mt Victoria North scene layer package - scene layer Oriental Bay - Roseneath scene layer package - scene layer Hataitai scene layer package - scene layer Mt Cook West scene layer package - scene layer Mt Cook East scene layer package - scene layer Newtown East scene layer package - scene layer Newtown South scene layer package - scene layer Newtown Central scene layer package - scene layer Melrose scene layer package - scene layer Lyall Bay West scene layer package - scene layer Lyall Bay East scene layer package - scene layer Rongotai scene layer package - scene layer Kilbirnie North scene layer package - scene layer Kilbirnie Central scene layer package - scene layer

Renowned for its natural and man-made beauty, the historic city of Venice spans a series of islands in a shallow lagoon. Venice’s unique geography has a downside, however. Tidal patterns mix with low elevation to cause acqua alta (high water), a periodic flooding that, although not dangerous to human life, impedes transportation and endangers Venice’s priceless architecture.This layer package includes three layers. The Structures layer contains building footprint data. The Canals layer contains Venice's canals. The Landmarks layer contains famous landmarks throughout the city. The data was acquired from Comune di Venezia - Portale dei servizi in 2014.This layer package contains feature class data on Venice's landmarks, canals, and structures for the tutorial Map Venice in 2D. The data will be used to visualize the landscape of Venice.

Displays Vision Zero High Injury Network Score data maintained by Seattle Department of Transportation. Refresh Cycle: None, Static, Manually updated as required. Contact: Vision Zero team Create Date: 4/15/2024 Updated Date: 5/29/2025 via layer package file Updated Date: 10/14/2025 via layer package file Updated Date: 10/20/2025 Manually deleted record for 1st Ave S E Updated Date: 1/28/2026 via layer package files

This layer provides the water bodies for geographic display and analysis at regional and national levels. It represents the water feature areas (for example, glaciers, lakes, reservoirs, and swamps) of the United States.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA National Atlas Water Feature Areas Water Bodies.

World Regions represents the boundaries for 25 commonly recognized world regions. This layer is best viewed out beyond a maximum scale (zoomed in) of 1:5,000,000. The sources of this dataset are Esri, Global Mapping International (GMI), and U.S. Central Intelligence Agency (The World Factbook). It is updated as country boundaries coincident to regional boundaries change. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World Regions.

Displays Vision Zero High Injury Network Score data maintained by Seattle Department of Transportation.

Even though there are a lot of sources of data that can help with a departments pre-incident planning efforts, there still is a need for firefighters to go and conduct pre-incident surveys. However with a lot of the leg work complete in developing the foundation of a preplan, the focus can be on the operation, access, and hazard information that is an important part of a preplan. A part of the preplanning process is identifying locations that firefighters are likely to operate. Operational features are locations that are used to fix, access, shutoff, or indicate something useful for responders. This includes • Key access location such as stairwell and main doorways ○ Some features map be co located at these areas such as area of refuge, intercom, standpipe discharge • Fire suppression features such as fire department connections and sprinkler valves • Utility shutoff such as electrical switches, natural gas shutoff, or water shutoff • Control panels such as fire alarm control panels, building intercom, HVAC or air management control panels • Key box whether it is stand alone or attached to existing This feature can also include locations with a bias toward emergency operation such as a stairwell with an area of refuge, standpipe discharge, and sprinkler control valve

Explore detailed Stretch Film import data of Layers Packaging in the USA—product details, price, quantity, origin countries, and US ports.

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Surface_Classification/FeatureServer

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Base_Layers/MapServer

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Land_Estate_Layers/MapServer

This map is derived from pct_baloss, the comprehensive version of the final results of the 2006 National Insect and Disease Risk Map (NIDRM) Project. Specifically, its pixel values range from 0 to 100 percent, representing the predicted percent of basal area loss over the next 15 years due to insects and diseases.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

Web maps are available for the following nine Kansas federal reservoirs considered for study in this project:Cheney ReservoirClinton LakeEl Dorado LakeHillsdale LakeMarion ReservoirMelvern LakeMilford LakePerry LakeTuttle Creek Lake

Mobile map packages are available for the following nine Kansas federal reservoirs considered for study in this project:Cheney ReservoirClinton LakeEl Dorado LakeHillsdale LakeMarion ReservoirMelvern LakeMilford LakePerry LakeTuttle Creek Lake

Layer Package File containing CRMP Coastal Flood Hazard Floodplain Vectors at 2020, 2040, 2060, and 2080 for MLW, MHW, 1.5xMTR, 2-yr, 5-yr, 10-yr, 25-yr, 50-yr, 100-yr, and 500-yr recurrence intervals.

Additional information can be found at https://www.dcr.virginia.gov/crmp/

🔗 Related Datasets

• Coastal Flood Hazard (2020, 2040, 2060, 2080, 2100) — Virginia Department of Conservation and Recreation
• Depth Grid(2020,2040,2060,2080) — Virginia Department of Conservation and Recreation

This layer presents the census block groups of the United States in the 50 states, the District of Columbia, and Puerto Rico. It provides detailed boundaries that are consistent with the tract, county, and state data sets and are effective at regional and state levels. This layer can be used for visualization and spatial analysis. This layer will be updated on an annual basis with the latest available data from TomTom.Source Data: access the source data for this layer to use or publish and share.USA Block Groups Layer Package