FR_URBAN_FORESTRY/FR_Urban_Landcover_Raster

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Service Description: Image service displaying land cover data for all urban areas in the State of Wisconsin, as identified in the 2010 Census, as well as adjacent lands. Pixels have a 1-m2 resolution and use the below values: The classification was derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2022 using a supervised machine learning classifier. The land cover data was produced by ensembling two models that classified each pixel based on its spectral signature. The full landcover model was designed to capture all landcover classes whereas the second model was specifically tuned to identify only trees. The second model was created to improve the full landcover model accuracy because it tended to overpredict trees. The ensemble model classified ‘indeterminable’ pixels where the tree model predicted trees not included in the tree-specific model. A statewide training and test dataset composed of tree, turf, building, road, and water data was utilized to build the model. Quality analysis of the results showed that there were no systematic biases based on geography or community size. Users should be aware that, because of the wide geographic spread of this classification and the need to mosaic together photographs from different times of the year, times of the day, and from different angles, there are many sources of error inherent in the underlying source imagery. These issues include significant discrepancies in shadow angle, direction and intensity, and wide variations in phenology or climate based on the underlying geography or habitat. The use of the “indeterminable” value reflects this uncertainty for many pixels. It is not recommended to use these and similar data for change detection; that is – the comparison from one time period to another. There is too much uncertainty within a given year’s imagery, and that uncertainty is exacerbated when comparing multiple years. Furthermore, this classification was trained on urban elements – trees, grass, and impervious surfaces in highly developed spaces; the fields and wetlands that are sometimes on urban peripheries were not part of this study’s focus and thus there may be discrepancies in those types of spaces between the model and reality. Use caution before conducting any additional analyses.

Name: FR_URBAN_FORESTRY/FR_Urban_Landcover_Raster_2022

Description: Image service displaying land cover data for all urban areas in the State of Wisconsin, as identified in the 2010 Census, as well as adjacent lands. Pixels have a 1-m2 resolution and use the below values: The classification was derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2022 using a supervised machine learning classifier. The land cover data was produced by ensembling two models that classified each pixel based on its spectral signature. The full landcover model was designed to capture all landcover classes whereas the second model was specifically tuned to identify only trees. The second model was created to improve the full landcover model accuracy because it tended to overpredict trees. The ensemble model classified ‘indeterminable’ pixels where the tree model predicted trees not included in the tree-specific model. A statewide training and test dataset composed of tree, turf, building, road, and water data was utilized to build the model. Quality analysis of the results showed that there were no systematic biases based on geography or community size. Users should be aware that, because of the wide geographic spread of this classification and the need to mosaic together photographs from different times of the year, times of the day, and from different angles, there are many sources of error inherent in the underlying source imagery. These issues include significant discrepancies in shadow angle, direction and intensity, and wide variations in phenology or climate based on the underlying geography or habitat. The use of the “indeterminable” value reflects this uncertainty for many pixels. It is not recommended to use these and similar data for change detection; that is – the comparison from one time period to another. There is too much uncertainty within a given year’s imagery, and that uncertainty is exacerbated when comparing multiple years. Furthermore, this classification was trained on urban elements – trees, grass, and impervious surfaces in highly developed spaces; the fields and wetlands that are sometimes on urban peripheries were not part of this study’s focus and thus there may be discrepancies in those types of spaces between the model and reality. Use caution before conducting any additional analyses.

Single Fused Map Cache: false

Extent:

Initial Extent:

Full Extent:

Pixel Size X: 1.0

Pixel Size Y: 1.0

Band Count: 1

Pixel Type: U8

RasterFunction Infos: {"rasterFunctionInfos": [{ "name": "None", "description": "", "help": "" }]}

Mensuration Capabilities: Basic

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

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Max Scale: 0

Copyright Text:

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: 0

Max Values: 5

Mean Values: 0.27513024214794

Standard Deviation Values: 0.83934986996735

Object ID Field:

Fields: None

Default Mosaic Method: Center

Allowed Mosaic Methods:

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Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Bilinear

Max Record Count: null

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: null

Max Mosaic Image Count: null

Allow Raster Function: true

Allow Copy: true

Allow Analysis: true

Allow Compute TiePoints: false

Supports Statistics: false

Supports Advanced Queries: false

Use StandardizedQueries: true

Raster Type Infos:

Has Raster Attribute Table: false

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources: Info Histograms Statistics Key Properties Legend Raster Function Infos

Supported Operations: Export Image Identify Measure Compute Histograms Compute Statistics Histograms Get Samples Compute Class Statistics Query Boundary Compute Pixel Location Compute Angles Validate Project

FR_URBAN_FORESTRY/FR_Urban_Landcover_Raster_2022 (ImageServer)