This table is tied to the San Luis Obispo County Assessor parcel database and provides planning information related to the parcels. It is utilized in mapping applications including PermitViewMap and GeoView. This table is created by overlaying the parcel featureclass with several planning layers including Land Use, Flood Hazard, etc. The overlaying properies are tagged with codes to indicate that they are within the various overlaying layers.

Boundaries of the County Service Areas Spheres of Influence in San Luis Obispo County. These boundaries have been delineated using the County Parcel data and are not based on the legal description.

Boundaries of the Sphere of Influence (SOI) of each incorporated city in San Luis Obispo County. These boundaries have been delineated using the County Parcel data and are not based on the legal description.

Highway corridor design standards used to identify which parcels lay partially or completely within the visual corridor. Digitized Highway Corridor Areawide Standards using USGS topo quads, parcel boundaries and the original paper maps as reference.

Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

This shapefile contains tax rate area (TRA) boundaries in San Luis Obispo County for the specified assessment roll year. Boundary alignment is based on a 2013 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

The FIRM is the basis for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). Insurance applications include enforcement of the mandatory purchase requirement of the Flood Disaster Protection Act, which "... requires the purchase of flood insurance by property owners who are being assisted by Federal programs or by Federally supervised, regulated or insured agencies or institutions in the acquisition or improvement of land facilities located or to be located in identified areas having special flood hazards," Section 2 (b) (4) of the Flood Disaster Protection Act of 1973. In addition to the identification of Special Flood Hazard Areas (SFHAs), the risk zones shown on the FIRMs are the basis for the establishment of premium rates for flood coverage offered through the NFIP. The DFIRM Database presents the flood risk information depicted on the FIRM in a digital format suitable for use in electronic mapping applications. The DFIRM database is a subset of the Digital FIS database that serves to archive the information collected during the FIS.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a 12,000 scale.

Designated Coastal Zone of San Luis Obispo County. Lands identified on the official maps of the Land Use Element as being located within the Local Coastal Plan (LCP) Combining Designation. The California Coastal Zone of San Luis Obispo County was established by the California Coastal Act of 1976. The Coordinates for this dataset are State Plane Coordinate System, Zone 5, NAD 1983 Feet.Required for planning purposes under the jurisdiction of the Local Coastal Plan - the land use polices are different between the coast and the inland areas of the County. This data provides suitable land use designation information for many mapping applications. This data is appropriate for use at a regional scale and is intended as a reference. The original 1977 Coastal Zone Boundary maps were mylar (drafting film) copies of 161 USGS 7.5 minute topographic quadrangles with an inked boundary added. This digital version of the boundary was developed to provide a georeferenced, attributed (to explain the basis of the mapped Coastal Zone), cadastral (parcel-based) depiction of the adopted Coastal Zone Boundary for the planning and regulatory activities of the Coastal Commission, local governments and others. However, it does not represent "survey" accuracy information, and may not eliminate the need for a formal boundary determination. Public Resources Code (PRC) Section 30103(a) specifically defines California's Coastal Zone as that land and water area of the State of California from the Oregon border to the border of the Republic of Mexico depicted on maps identified and set forth in Section 17 of that chapter of the Statutes of the 1975-76 Regular Session enacting PRC Division 20 (the Coastal Act of 1976). PRC Section 30103(b) directed the Coastal Commission to prepare and adopt more detailed 1:24,000 scale Coastal Zone Boundary (CZB) maps, which occurred March 1, 1977. These 161 adopted maps provide the official basis for all other representations of the landward CZB. The digital version of the CZB created by developing this shapefile is a conformed copy of the official boundary, and in some locations reflects legislative changes and Coastal Commission minor adjustments adopted from time to time since March 1977.

Dataset Summary:

The Monterey, San Benito and San Luis Obispo County Impervious Surfaces map is a 5-class fine-scale polygon vector representation of artificial impervious surfaces in the region. There are 1,253,729 features in the dataset. Non-impervious areas are not mapped and are not covered by polygons. The impervious map represents the state of the landscape in summer, 2022. This technical mapping work for this product was conducted by the impervious mapping team at the University of Vermont Spatial Analysis Lab and EarthDefine. Table 1 lists download locations for the dataset.Table 1. Monterey, San Benito, and San Luis Obispo Counties counties impervious surfaces data

Description

Link

File Geodatabase Feature Class

https://vegmap.press/central_coast_impervious_fgdb

Vector Tile Service

https://vegmap.press/central_coast_impervious_vt

Detailed Dataset Description: The impervious map was created using a combination of AI techniques and “expert systems” rulesets developed in Trimble eCognition. Initial impervious polygons for populated areas were produced using AI techniques by EarthDefine. These were refined and classified into impervious types by the UVM Spatial Analysis Lab using eCognition. Impervious surfaces in less populated areas were produced entirely by the UVM Spatial Analysis Lab in eCognition. ECognition rulesets combine automated image segmentation 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 impervious mapping included: high resolution (0.6 meter) 4-band NAIP imagery (2022), the unified lidar point cloud, which is an amalgamation of the most recent/best available lidar data for the three-county area, and lidar derived rasters from the unified point cloud such as the canopy height model (CHM) and normalized digital surface model (nDSM).

After production in AI and eCognition, the preliminary impervious map product was manually edited by a team of UVM’s photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results.

The impervious map has 5 classes representing different types of impervious features, which are described below:

·
Building – Structures including homes, commercial buildings, outbuildings, and other human-made structures such as water tanks and silage silos. Structures fully occluded by vegetation will not be mapped.

·
Paved Road – Roads that are paved and wide enough for a vehicle.

· Dirt/Gravel Road – Dirt or gravel roads wide enough for a vehicle. Non-ephemeral fire roads, ranch roads and long driveways. Polygons representing narrow unpaved (single track) trails are not included in this data product.

· Other Dirt/Gravel Surface – Dirt or gravel surfaces that are highly compacted and used by humans and equipment, such as parking lots, road pull-offs, some dirt or gravel paths, and highly compacted areas around commercial activities. This class DOES NOT include natural turf playing fields, very lightly used dirt roads, livestock areas, naturally occurring bare soil or rock, or bare areas around ponds.

· Other Paved Surface – Includes parking lots, sidewalks, paved walking paths, swimming pools, tennis courts.

Miscellaneous quality control and processing notes:·
Zoom level used during manual quality control was no finer than 1 to 500.·
Vector data was created with no overlapping polygons.

Data Limitations: This is not a planimetric data product and was created using semi-automated techniques. It provides a reasonable and useful depiction of impervious surfaces for planners and managers but does not have the accuracy or precision to support engineering applications. No formal accuracy assessment was conducted for this dataset. Users should apply caution when using the data for applications requiring high positional or classification accuracy. Appropriate uses of the data product include:· As an input to storm water models· For planners to assess % imperviousness in a parcel/watershed· To help identify areas of human infrastructure for fuels and fire management· As an input to fuel models that are used in fire behavior and fire spread models· For cartography and mapping· Generally for use at scales 1:1,000 and smaller

Inappropriate uses of this product include:· Measuring exact square footage of structures or impervious features for building projects· Using the impervious polygons as geographically precise information for transportation and public works engineering projects· Determining ownership or maintenance responsibility of a particular feature, such as a paved or dirt road· Identifying publicly accessible areas for recreation or other uses· Confirming the suitability of a surface for any use including driving, hiking, bicycling, etc.

Common errors in this dataset are inter-class confusion and errors of commission to impervious. These are discussed in more detail below:

·
Inter-Class Confusion: The accuracy of the map for impervious versus pervious is very high (although no quantitative assessment of accuracy was funded for this product). However, the accuracy for individual impervious classes will be much lower. For example, confusion exists between the ‘Other Paved’ and ‘Other Dirt/Gravel Surface,’ classes, even though these are both mapped correctly as impervious surfaces.

· Errors of Commission: The most widespread error in this map are areas mapped as impervious that are actual pervious surfaces of dried out herbaceous land cover. Some dried-out herbaceous cover may be mistakenly classified as impervious due to spectral similarity. Manual editing minimized but did not completely eliminate these errors.