Name: SDCounty_AOI_Classification.tif

Display Field: Value

Type: Raster Layer

Geometry Type: null

Description: This land cover classification product contains 5 classes (values of ‘0’ are NoData):Tree Canopy (elevated vegetation (>= 15 ft) not described in the forb and shrub layer)Low Vegetation / Grass (shrub and forb layer)Bare Earth & Impervious Surfaces (any and all manmade and natural)Open WaterOtherMethods: The classification method for the primary, contiguous portions of the unincorporated land was based upon a neural network classificaiton using the NAIP imagery and LIDAR-derived feature height data. A convolution neural network (CNN) incorporating the 4-band NAIP imagery and the feature height data was utilized to classify the categories of interest to the County of San Diego. The process included extracting training data from the NAIP imagery and feature height data sets, feature training, image classification, and two accuracy checks. One of the accuracy checks was based on visual review of the high-spatial resolution NAIP imagery and the other was based on cross-tabulation with existing image classification data set (described below). The accuracy check ensured that the product met accuracy expectations (at least 85%-90% accuracy for tree canopy estimate). Tree canopy was classified based upon spectral-radiometric information in the NAIP imagery, as well as the feature height being 15 ft or greater. The tree canopy classification was the primary focus of the project. The final deliverable product has the same spatial resolution as the NAIP imagery (i.e., 0.6 m). For the island polygons in the western portion of the study area, the land cover classification was derived using data already available from the 2014 Urban Tree Canopy Assessment (UTCA), and the neural network classification approach was not used. The existing UTCA classification product was recoded using the below crosswalk, and also merged/aggregated into the same raster grid as the SD County image classification product provided by Imagis.UTCA "Tree Canopy" = CNN classification "Tree Canopy" (elevated vegetation (>= 15 ft) not described in the forb and shrub layer)UTCA "Grass/Shrub" = CNN classification "Grass/Vegetation" (shrub and forb layer)UTCA "Bare Earth" = CNN classification "Bare Ground" (any and all manmade and natural)UTCA "Water" = CNN classification "Open Water"UTCA "Building" = CNN classification "Other"UTCA "Roads" = CNN classification "Bare Ground" (any and all manmade and natural)UTCA "Other Paved Surfaces" = CNN classification "Bare Ground" (any and all manmade and natural)The neural network model (DeepLab 3+ in TensorFlow) was trained on 7 NAIP/nDSM combination TIFF tiles, with the raster-based labels for those areas derived from the 2014 UTCA classification product.The 2016 NAIP 4-band imagery and LiDAR acquisitions of San Diego County were used for this classification product, as mentioned above. DSMs were extracted from the LiDAR data by Imagis, and normalized DSMs (nDSMs) were generated by subtracting the DEMs (provided by the County) from the DSMs. The nDSMs data tiles with feature height information were combined with the NAIP imagery and utilized with the trained TensorFlow model and perform the image classification on the primary, conginuous portions of the unincorporated land.

Service Item Id: ef8991eaf6524ddea96d52e84f2549c1

Copyright Text:

Default Visibility: true

MaxRecordCount: 0

MaxSelectionCount: 0

Supported Query Formats: JSON, geoJSON, PBF

Min Scale: 0.0

Max Scale: 0.0

Supports Advanced Queries: false

Supports Statistics: false

Has Labels: false

Can Modify Layer: false

Can Scale Symbols: false

Use Standardized Queries: true

Supports Datum Transformation: true

Extent:

XMin: -1.3081134768582994E7
YMin: 3835282.460278409
XMax: -1.2921984728783254E7
YMax: 3963403.502974601
Spatial Reference: 102100  (3857)  LatestVCSWkid(0)

Drawing Info:

N/A

Advanced Query Capabilities:

Supports Statistics: false
Supports OrderBy: false
Supports Distinct: false
Supports Pagination: false
Supports TrueCurve: true
Supports Returning Query Extent: true
Supports Query With Distance: true
Supports Sql Expression: false
Supports Query With ResultType: false
Supports Returning Geometry Centroid: false
Supports Binning LOD: false
Supports Query With LOD Spatial Reference: false
Supports Percentile Statistics: false
Supports Having Clause: false
Supports Count Distinct: false
Supports Time Relation: true
Supports Sql Format: false
Supports Query Analytic: false
Supports Query With Current User: true

HasZ: false

HasM: false

Has Attachments: false

HTML Popup Type: esriServerHTMLPopupTypeNone

Type ID Field: null

Fields:

• OID ( type: esriFieldTypeOID, alias: OID )
• Value ( type: esriFieldTypeInteger, alias: Value )
• Count ( type: esriFieldTypeDouble, alias: Count )

Supported Operations:   Query   Query Attachments   Query Analytic   Generate Renderer   Return Updates

  Iteminfo   Thumbnail   Metadata