20260203
16010800
1.0
FALSE
002
Complete_Inventory_Photo_Pts
-970900.587722
940865.347269
370731.705582
1832722.930872
1
Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
WYDOT Albers
<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/3.6.0'><WKT>PROJCS["WYDOT Albers",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-107.5],PARAMETER["Standard_Parallel_1",42.0],PARAMETER["Standard_Parallel_2",44.0],PARAMETER["Latitude_Of_Origin",40.0],UNIT["Foot_US",0.3048006096012192]]</WKT><XOrigin>-51560100</XOrigin><YOrigin>-27992300</YOrigin><XYScale>3048.0060960121914</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808333333333335</XYTolerance><ZTolerance>2</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem>
FGDC
150000000
5000
20251202
11462400
20251202
11462400
dataset
Brian Robeson
Trihydro Corporation
GIS Analyst
307 745-7474
1252 Commerce Dr.
Laramie
WY
82070
US
brobeson@trihydro.com
20251202
ArcGIS Metadata
1.0
WYDOT, Winter Research Services
Contact WYDOT Winter Research Services
withheld
File Geodatabase Feature Class
Snow Fence Inventory Photo Points
2025-11-25T00:00:00
Trihydro Corporation
vector digital data
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This data set depicts photo points of snow fences in Wyoming. It contains locations of all photos taken since the snow fence inventory began in May 2005.</SPAN></P></DIV></DIV></DIV>
These data were collected during a snow fence inventory. The points show the location of photos taken of snow fences. The photos are intended to enhance knowledge of each snow fence.
WYDOT and Trihydro Corporation.
Brian Robeson
Trihydro Corporation
GIS Analyst
307 745-7474
1252 Commerce Dr.
Laramie
WY
82070
US
brobeson@trihydro.com
Lincoln County
Carbon County
Weston County
Niobrara County
Park County
Crook County
Teton County
United States
Sweetwater County
Washakie County
Uinta County
Fremont County
Sublette County
Rocky Mountain Region
Laramie County
Albany County
Hot Springs County
Sheridan County
Wyoming
Goshen County
Natrona County
Platte County
Campbell County
Converse County
Johnson County
Big Horn County
Winter
Snow
Highway
Road
Snow Fence
Fence
Transportation
Snowfence
Lincoln County
Carbon County
Weston County
Niobrara County
Park County
Winter
Crook County
Snow
Teton County
United States
Sweetwater County
Washakie County
Highway
Road
Uinta County
Fremont County
Sublette County
Snow Fence
Fence
Rocky Mountain Region
Laramie County
Albany County
Hot Springs County
Transportation
Sheridan County
Snowfence
Wyoming
Goshen County
Natrona County
Platte County
Campbell County
Converse County
Johnson County
Big Horn County
Access of these data is limited to WYDOT and Trihydro personnel associated with snowfence inventory, maintenance, and research.
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The user assumes the entire risk related to its use of this data. WYDOT is providing this data "as is," and WYDOT disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose. In no event will WYDOT be liable to you or to any third party for any direct, indirect, incidental, consequential, special or exemplary damages or lost profit resulting from any use or misuse of this data.</SPAN></P></DIV></DIV></DIV>
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Per WYDOT and Trihydro policy.</SPAN></P></DIV></DIV></DIV>
Esri ArcGIS 13.6.0.59527
-111.250853
-103.865034
40.962622
45.029785
0.000017
9704.991317
ground condition
2005-05-01T00:00:00
2025-10-31T00:00:00
0.000017
9704.991317
1
-111.250853
-103.865034
45.029785
40.962622
These data are logically consistent. All points are labeled.
This data set is currently in work. Not all snow fences have been inventoried. The un-inventoried fences were constructed after the inventory crew passed through.
Attributes have been individually field and office checked for accuracy. Accuracy is 100%. A data dictionary developed by Trihydro Corporation was used for data collection.
These data were collected with a Trimble GeoXH GPS receiver set to collect code data. Terrasync software version 4.1 was employed. Differential correction yielded accuracies of: 90% accurate to less than 1 meter, and 10% accurate to 1 to 2 meters.
Used a Trimble GeoXH GPS receiver set to collect code data. A new file was opened each day. At least one and as many as 10 positions were recorded for each photo location. At the end of each day, the data was transferred to a laptop running Pathfinder Office 4.1. Differential correction was performed at that time and the data exported to shapefile. The shapefile was then examined for attribute and positional accuracy. It is important to note that every feature was examined.
2009-07-01
Brian Robeson
Trihydro Corporation
GIS Specialist
307 745-7474
1252 Commerce Dr.
Laramie
WY
82070
US
brobeson@trihydro.com
Used a Trimble GeoXH GPS receiver set to collect carrier data. A new file was opened each day. At least one and as many as 10 positions were recorded for each photo location. At the end of each day, the data was transferred to a laptop running Pathfinder Office 5.0. Differential correction was performed at that time and the data exported to shapefile. The shapefile was then examined for attribute and positional accuracy. It is important to note that every feature was examined.
2010-09-01
The entire data set was reviewed for accuracy and consistencey.
2010-12-01
2010 field data were merged with the complete data set.
2010-12-01
13596
Complete_Inventory_Photo_Pts
Feature Class
13596
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
FENCEID
FENCEID
Integer
4
0
0
Unique number corresponding to each fence.
FENCENO
FENCENO
String
7
0
0
First 3 digits correspond to mile post. Last 4 digits correspond to snow fence number.
Milepost
Milepost
Integer
4
0
0
The mile in which the photo was taken.
PhotoNum
PhotoNum
Integer
4
0
0
The photo number. Automatically assigned by Terrasync.
Photo_ID
Photo_ID
String
20
0
0
Photo identification number. When combined with the FenceID number, it yields the unique name of every photo.
_1.jpg
_2.jpg
_3.jpg
_4.jpg, etc.
Comment
Comment
String
100
0
0
Free text field describing the photo.
Max_PDOP
Max_PDOP
Double
8
0
0
Indicates strengh of space vehicle geometry at data collection time.
1
6
na
0.1
Corr_Type
Corr_Type
String
36
0
0
The type of correction applied to the data.
SBAS Real-Time
Real time correction via Satellete Beacon Augmentation System. This is a differential correction technique.
Post-Processed
A differental correction technique applied at a later time.
Rcvr_Type
Rcvr_Type
String
36
0
0
The type of receiver used to collect the data.
GPS_Date
GPS_Date
Date
8
0
0
GPS_Time
GPS_Time
String
10
0
0
Horz_Prec
Horz_Prec
Double
8
0
0
Horizontal precision in feet. Exported from Pathfinder Office.
Shape
Feature geometry.
ESRI
Coordinates defining the features.
SHAPE
Geometry
0
0
0
FENCETYPE
FENCETYPE
String
3
0
0
Type of fence. See the snow fence metadata for a complete description of types.
LRS
LRS
String
30
0
0
Concatenation of Category, ID Number, and Direction. Matches the LRS_Name field in the snow fence feature class.
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
ESRIGNSS_H_RMS
Horizontal Accuracy (m)
Double
8
0
0
ESRIGNSS_FIXDATETIME
Fix Time
Date
8
0
0
GlobalID
GlobalID
GlobalID
38
0
0
Simple
FALSE
13596
FALSE
FALSE
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