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Service Description: Land cover image services displaying 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 2020 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 additional analyses.
Name: FR_URBAN_FORESTRY/FR_Urban_Landcover_Raster_2020
Description: Land cover image services displaying 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 2020 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 additional analyses.
Single Fused Map Cache: false
Extent:
XMin: 288797
YMin: 211884
XMax: 740964
YMax: 714397
Spatial Reference: 3071
(3071)
Initial Extent:
XMin: 288797
YMin: 211884
XMax: 740964
YMax: 714397
Spatial Reference: 3071
(3071)
Full Extent:
XMin: 288797
YMin: 211884
XMax: 740964
YMax: 714397
Spatial Reference: 3071
(3071)
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
Rendering Rule:
Min Scale: 0
Max Scale: 0
Copyright Text:
Service Data Type: esriImageServiceDataTypeGeneric
Min Values: 0
Max Values: 5
Mean Values: 0.2803587772546
Standard Deviation Values: 0.86735653331077
Object ID Field:
Fields:
None
Default Mosaic Method: Center
Allowed Mosaic Methods:
SortField:
SortValue: null
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:
Name: Raster Dataset
Description: Supports all ArcGIS Raster Datasets
Help:
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