20240320 10582900 1.0 FALSE Street_Signs 001 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_StatePlane_Maryland_FIPS_1900 <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.1.0'><WKT>PROJCS[&quot;NAD_1983_StatePlane_Maryland_FIPS_1900&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic&quot;],PARAMETER[&quot;False_Easting&quot;,400000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-77.0],PARAMETER[&quot;Standard_Parallel_1&quot;,38.3],PARAMETER[&quot;Standard_Parallel_2&quot;,39.45],PARAMETER[&quot;Latitude_Of_Origin&quot;,37.66666666666666],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26985]],VERTCS[&quot;NAVD88_height_(ftUS)&quot;,VDATUM[&quot;North_American_Vertical_Datum_1988&quot;],PARAMETER[&quot;Vertical_Shift&quot;,0.0],PARAMETER[&quot;Direction&quot;,1.0],UNIT[&quot;Foot_US&quot;,0.3048006096012192],AUTHORITY[&quot;EPSG&quot;,6360]]</WKT><XOrigin>-36747100</XOrigin><YOrigin>-29091500</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>26985</WKID><LatestWKID>26985</LatestWKID><VCSWKID>105703</VCSWKID><LatestVCSWKID>6360</LatestVCSWKID></ProjectedCoordinateSystem> 20231006 08435400 20231006 08435400 500000 5000 ItemDescription Esri ArcGIS 13.1.3.41833 Street_Signs 2022-12-15T00:00:00 2022-12-18T00:00:00 2022-12-18T00:00:00 Jonathan Austin, GISP, PMP GPI Geospatial, Inc. Senior Geospatial Project Manager Comprehensive sign inventory for roadway signs within the city boundary of Salisbury, Maryland. Initial data collected and provided by GPI Geospatial, Inc. Subsequent updates performed by Department of Field Operations. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The inventory was derived from lidar data and imagery collected using a mobile lidar system during May, June, and July 2022. All features and attributes were located and measured using TopoDOT, then exported to a shapefile. The shape file was then imported into the geodatabase named "Salisbury_Sign_Inventory_2022". The inventory is not complete without the associated folder named "Photos" containing photographs of each sign feature where available.</SPAN></P></DIV></DIV></DIV> GPI Geospatial, Inc. and City of Salisbury, Department of Information Services: GIS Division Sign Inventory Asset Inventory LiDAR Street-level imagery City of Salisbury IS: GIS Field Operations FO Utilities Traffic Open Data Public -75.6609461 75.4837628 38.4268113 38.3253021 0 Boundary of the City of Salisbury, Maryland 2022-05-16T00:00:00 2022-07-29T00:00:00 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The Requested Data is being provided "as is", "with all faults" and without guarantee or warranty of any kind, express or implied, including, without limitation, fitness for a particular purpose and merchantability, which are hereby expressly disclaimed. Further, The City of Salisbury makes no warranties or guarantees related to the accuracy, precision or completeness of the requested Data. In no event shall City of Salisbury, any of its affiliates or subsidiaries, or any of their respective shareholders, officers, directors, employees or agents be liable to requestor or any other person or entity for any activity involving the use or misuse of the requested Data. Requestor waives and releases each of the foregoing persons from any such use resulting in any and all loss or damage. Requestor expressly assumes all risk of use of the Requested Data and understands and agrees that it shall not rely on any portion of the Requested Data without independently confirming the same first. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the City of Salisbury in the metadata.</SPAN></P></DIV></DIV></DIV> Enterprise Geodatabase Feature Class dataset EPSG 4.5(3.0.1) 0 Simple FALSE 0 TRUE TRUE Street_Signs Feature Class 11866 Position and attribute information for the City of Salisbury sign inventory completed in 2022 GPI Geospatial, Inc. OBJECTID_1 OBJECTID_1 OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. sby.SDE.Street_Signs.FID sby.SDE.Street_Signs.FID String 38 0 0 OBJECTID ObjectID Integer 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 8 0 0 Feature geometry. Esri Coordinates defining the features. X X Double 8 38 8 Maryland State Plane Easting coordinate, US survey feet GPI Geospatial, Inc. Numerical coordinate Y Y Double 8 38 8 Maryland State Plane Northing coordinate, US survey feet GPI Geospatial, Inc. Numerical coordinate Z Z Double 8 38 8 North American Vertical Datum 1988, feet GPI Geospatial, Inc. Numerical coordinate Clearance Clearance Double 8 38 8 Vertical difference from ground to bottom of sign feature GPI Geospatial, Inc. Numerical measurement MUTCD MUTCD Code String 254 0 0 Federal Highway Administration and Maryland Department of Transportation Manual on Uniform Traffic Control Devices code GPI Geospatial, Inc. Textual code from MUTCD documentation PanelW PanelW Double 8 38 8 Sign panel width in US survey feet GPI Geospatial, Inc. Numerical measurement PanelH PanelH Double 8 38 8 Sign panel height in US survey feet GPI Geospatial, Inc. Numerical measurement TravelDir TravelDir String 254 0 0 Direction of travel along associated roadway GPI Geospatial, Inc. Text cardinal direction PanelDir PanelDir String 254 0 0 Direction sign panel is facing GPI Geospatial, Inc. Text cardinal direction SideOfRoad SideOfRoad String 254 0 0 Position of sign in relation to the roadway, left or right GPI Geospatial, Inc. Binary text, left or right DayCond DayCond String 254 0 0 Condition of sign as visible in natural daylight conditions (Good, Fair, Poor) GPI Geospatial, Inc. Text options, Good, Fair, Poor SupportX SupportX Double 8 38 8 Maryland State Plane Easting Coordinate of support feature, US survey feet GPI Geospatial, Inc. Numerical coordinate SupportY SupportY Double 8 38 8 Maryland State Plane Northing Coordinate of support feature, US survey feet GPI Geospatial, Inc. Numerical coordinate SupportZ SupportZ Double 8 38 8 North American Vertical Datum 1988 of support feature, feet GPI Geospatial, Inc. Numerical coordinate Road Name String 254 0 0 Road name City of Salisbury Text road name Type Type String 254 0 0 Support type GPI Geospatial, Inc. Text type or material Condition Condition String 254 0 0 Support condition (Good, Fair, Poor) GPI Geospatial, Inc. Text, Good, Fair, Poor PanelW_Inc PanelW_Inc Double 8 38 8 Sign panel width in inches GPI Geospatial, Inc. Numerical measurement PanelH_Inc PanelH_Inc Double 8 38 8 Sign panel height in inches GPI Geospatial, Inc. Numerical measurement SignID SignID Integer 4 10 0 Automatically derived sign feature identifier GPI Geospatial, Inc Sequential unique whole numbers that are automatically generated. Photo Photo String 254 0 0 Filename of photo associated with sign feature GPI Geospatial, Inc. Text file name Photolink Photolink String 254 0 0 Relative filepath of photo associated with sign feature GPI Geospatial, Inc. Text file path FID_1 FID Double 8 38 8 MUTCD_Code MUTCD Code String 255 0 0 Description Description String 255 0 0 SupportID SupportID Integer 4 10 0 Automatically derived support feature identifier GPI Geospatial, Inc. Sequential unique whole numbers that are automatically generated. GlobalID GlobalID GlobalID 38 0 0 created_user created_user String 255 0 0 created_date created_date Date 8 0 0 last_edited_user last_edited_user String 255 0 0 last_edited_date last_edited_date Date 8 0 0 Pole_Type Pole_Type String 255 0 0 Comments Comments String 500 0 0 Date_Inspected Date_Inspected Date 8 0 0 Sign_Type Sign_Type String 255 0 0 20231006 PROJECT APPROACH & EXECUTION Survey/Collection Date (s): 05-17-2022, 05-18-2022, 05-19-2022, 05-20-2022, 05-23-2022, 05-25-2022, 06-03-2022, 06-04-2022, 06-05-2022, 06-07-2022, 06-24-2022, 07-22-2022 Sensor: VMX 450 Platform: Ford Transit Van Collection Software: RiAquire Number of Drive Lines: 572 Speed: 35 mph Point Density: 150+ ppsm Scan Rate: 30M 30KMH 550kHz 180lps Mobile LiDAR Mission: GPI collected the LiDAR point cloud data and digital imagery utilizing a Riegl VMX-450 mobile laser scanning system. The mobile scanning mission was planned and performed to meet the project accuracy specifications. On-site calibration, collection of Mobile LiDAR data, and any simultaneous Global Navigation Satellite System (GNSS) base station occupations are included as part of the project scanning mission. Any portions of the project corridors that could not be measured from the LiDAR system due to line-of-sight obstructions or conditions that would prevent adequate trajectory solutions were not included in our final LiDAR point cloud deliverables. Any potentially obscured or inaccessible areas were identified by GPI and communicated to the ground surveyor prior to the scanning mission and/or after the initial calibration process is completed. Smoothed Best Estimate of Trajectory (SBET) Processing The GNSS data collected by the LiDAR unit was processed using Applanix POSPac Mapping Suite version 8.5. A fixed bias carrier phase solution was computed for a forward and reverse vector relative to the occupied GPS base stations. During collection the Positional Dilution of Precision (PDOP) was typically 3.0 or less. Temporary PDOP spikes above 3.0 that occurred during collection were noted and/or recorded. The IMU information was incorporated with the GNSS solution utilizing the Applanix POSPAC software to counter the effect of the GNSS loss of lock occurrences. The resulting Smoothed Best Estimate of Trajectory (SBET) was exported to be used in post processing the mobile LiDAR data. See Appendix C POSPAC GRAPHS for SBET accuracy solutions for this project. Mobile LiDAR Processing: The raw laser data from each scan pass was merged with a SBET solution using Riegl’s RiProcess software version 1.8.5 to compute the laser mass point positions to the initial coordinate values (XYZ). RiWORLD software version 5.1.4 was utilized to convert the initial mass point positions into a point cloud for each scan line. Transformation / Adjustment: This includes transformation and adjustment of LiDAR point cloud to surveyed project control. A trajectory processing report documenting the results of the mobile LiDAR data collection accuracy analysis relative to the project transformation control point network will be prepared and submitted to verify that the project positional accuracy requirements have been achieved. This report will include NSSDA reporting of validation points and trajectory solutions and a comparison analysis of field surveyed cross sections, if applicable. Upon review and approval of the results included in the processing report, the adjusted point cloud data will be created and delivered in digital LAS file format. Mobile Photography Processing: Digital photography collected during mobile LiDAR data collection was closely calibrated to the point cloud using Riegl RiProcess. The images were exported in JPG format along with CSV files containing the geographic position and orientation information for each. Mobile Video Processing: Digital video collected during mobile LiDAR data collection was exported in MP4 format along with the corresponding trajectory. The video is delivered in GPI’s Hyroad video viewer which displays the video and geographic position of the vehicle at the current location in the trajectory. EQUIPMENT 2015 Ford Transit mobile LiDAR vehicle VMX 450 (see Appendix F for calibration report) 2 Leica GS14 GNSS Receivers ISSUES AND PROBLEMS Some roads were inaccessible during data acquisition and therefore are not included in the final delivery. QUALITY ASSURANCE/QUALITY CONTROL The project’s progress was closely monitored throughout the entire period of work. LiDAR data was checked for deviations, data separation, pitch, completeness, and other items that could compromise the quality of the data. The deliverables were checked for completeness, appropriate file name(s) and format, and usability. The LAS review consists of reviewing separation within the point cloud on both vertical and horizontal surfaces. In the event separation is found effected scan lines are then re-adjusted to meet accuracy standards. See Appendix E for line-to-line z comparison maps. Positional Accuracy m 1 Duplicate features were removed. SupportID matches signs located on the same support. All sign features were mapped as visible within the lidar data and imagery. Sign feature location and attributes were derived using TopoDOT software from mobile lidar and imagery data. Mobile lidar data and imagery Boundary of the City of Salisbury, Maryland Sign feature extraction: Sign features were located in the lidar data using a combination of automated and manual tools in the TopoDOT software package. Each sign was then attributed in TopoDOT. The final feature set was exported to a shapefile, then imported into the geodatabase named "Salisbury_Sign_Inventory_2022". 2022-12-16T00:00:00 Jonathan Austin, GISP, PMP GPI Geospatial, Inc. Senior Geospatial Project Manager Jonathan Austin, GISP, PMP 11000 Broken Land Parkway, Suite 500 Columbia Maryland 21044 US 443.753.5515 /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK CwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAaQBpADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD8qqKK 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