Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall:Maintain an inventory of its enterprise datasets;Classify enterprise datasets by level of sensitivity;Regularly publish the inventory, including the classifications, as an open dataset; andStrategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://octo.dc.gov/page/district-columbia-data-policy. Previous years of EDI can be found on Open Data.

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall: maintain an inventory of its enterprise datasets; classify enterprise datasets by level of sensitivity; regularly publish the inventory, including the classifications, as an open dataset; and strategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelBefore using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input1. 8-bit, 3-band high-resolution (10 cm) imagery. The model was trained on 10 cm Vexcel imagery2. Building footprints feature classOutputFeature class containing classified building footprints. Classname field value 1 indicates damaged buildings, and value 2 corresponds to undamaged structuresApplicable geographiesThe model was specifically trained and tested over Maui, Hawaii, in response to the Maui fires in August 2023.Accuracy metricsThe model has an average accuracy of 0.96.Sample resultsResults of the models can be seen in this dashboard.

Enterprise Dataset Inventory as of March 2018. Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall:Maintain an inventory of its enterprise datasets;Classify enterprise datasets by level of sensitivity;Regularly publish the inventory, including the classifications, as an open dataset; andStrategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://octo.dc.gov/page/district-columbia-data-policy. The latest EDI can be found on Open Data.

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall:Maintain an inventory of its enterprise datasets;Classify enterprise datasets by level of sensitivity;Regularly publish the inventory, including the classifications, as an open dataset; andStrategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://octo.dc.gov/page/district-columbia-data-policy. Previous years of EDI can be found on Open Data.

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall:Maintain an inventory of its enterprise datasets;Classify enterprise datasets by level of sensitivity;Regularly publish the inventory, including the classifications, as an open dataset; andStrategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://octo.dc.gov/page/district-columbia-data-policy. Previous years of EDI can be found on Open Data.

Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelBefore using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input1. 8-bit, 3-band high-resolution (50 cm) imagery. The model was trained on 50 cm Airbus imagery2. Building footprints feature classOutputFeature class containing classified building footprints. Classname field value 1 indicates damaged buildings, and value 2 corresponds to undamaged structuresApplicable geographiesThe model was specifically trained and tested over Maui, Hawaii, in response to the Maui fires in August 2023.Accuracy metricsThe model has an average accuracy of 0.96.Sample resultsResults of the model can be seen in this dashboard.

The MS&R Plan identifies the general location and size of existing and proposed freeways, arterial and collector streets, future rights-of-way, setback requirements, typical intersections and cross sections, and gateway and scenic routes. The City’s Department of Transportation and the Planning and Development Services Department (PDSD) implement the MS&R Plan. The MS&R Plan is considered a Land Use Plan as defined in the Unified Development Code (UDC) Section 3.6, and, therefore, is subject to amendment in accordance with the standard Land Use Plan and Adoption and Amendment Procedures. The MS&R right-of-way lines are used in determining the setback for development through the MS&R Overlay provisions of the UDC. As stated in the current MS&R Plan, page 4, “The purpose of the Major Streets and Routes Plan is to facilitate future street widening, to inform the public which streets are the main thoroughfares, so that land use decisions can be based accordingly, and to reduce the disruption of existing uses on a property. By stipulating the required right-of-way, new development can be located so as to prepare for planned street improvements without demolition of buildings or loss of necessary parking.”PurposeThe major purposes of the Major Streets and Routes Plan are to identify street classifications, the width of public rights-of-way, to designate special routes, and to guide land use decisions. General Plan policies stipulate that planning and developing new transportation facilities be accomplished by identifying rights-of-way in the Major Streets and Routes Plan. The policies also aim to encourage bicycle and pedestrian travel, "minimize disruption of the environment," and "coordinate land use patterns with transportation plans" by using the street classification as a guide to land use decisions.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the Open Data portal and not for regular use in ArcGIS Online and ArcGIS Enterprise.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyAs needed

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall: maintain an inventory of its enterprise datasets; classify enterprise datasets by level of sensitivity; regularly publish the inventory, including the classifications, as an open dataset; and strategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

Esri ArcGIS Online (AGOL) Hosted Feature Layer for accessing the MDOT SHA Roadway Administrative Classifications (State Classifications) data product.MDOT SHA Roadway Administrative Classifications (State Classifications) data consists of linear geometric features which specifically show State-maintained roadways included in the State Primary & State Secondary systems throughout the State of Maryland. Roadway Administrative Classifications data is primarily used for general planning & funding purposes by showcasing the State Primary vs. State Secondary highway systems. The Maryland Department of Transportation State Highway Administration (MDOT SHA) currently reports this data only on the inventory direction (generally North or East) side of the roadway. Roadway Administrative Classification is not a complete representation of all roadway geometry.MDOT SHA Roadway Administrative Classifications data is maintained & updated by the MDOT SHA Office of Planning & Preliminary Engineering (OPPE) Data Services Division (DSD). Roadway Administrative Classifications data is used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Roadway Administrative Classification data is key to understanding which State-maintained roadways are included in the State Primary & State Secondary systems throughout Maryland.MDOT SHA Roadway Administrative Classifications data is updated & published on an annual basis for the prior year. This data is for the year 2022For more information related to the data, contact MDOT SHA OPPE Data Services Division (DSD):Email: DSD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall:Maintain an inventory of its enterprise datasets;Classify enterprise datasets by level of sensitivity;Regularly publish the inventory, including the classifications, as an open dataset; andStrategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

An area depicted as surface ownership parcels dissolved on the same ownership classification. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics, giving superior results.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputRaster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified raster with the same classes as in Corine Land Cover (CLC) 2018.Applicable geographiesThis model is expected to work well in Europe and the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 82.41% with Level-1C imagery and 84.0% with Level-2A imagery, for CLC class level 2 classification (15 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassLevel-2A ImageryLevel-1C ImageryPrecisionRecallF1 ScorePrecisionRecallF1 ScoreUrban fabric0.810.830.820.820.840.83Industrial, commercial and transport units0.740.650.690.730.660.7Mine, dump and construction sites0.630.520.570.690.550.61Artificial, non-agricultural vegetated areas0.700.460.550.670.470.55Arable land0.860.900.880.860.890.87Permanent crops0.760.730.740.750.710.73Pastures0.750.710.730.740.710.73Heterogeneous agricultural areas0.610.560.580.620.510.56Forests0.880.930.900.880.920.9Scrub and/or herbaceous vegetation associations0.740.690.720.730.670.7Open spaces with little or no vegetation0.870.840.850.850.820.84Inland wetlands0.810.780.800.820.770.79Maritime wetlands0.740.760.750.870.890.88Inland waters0.940.920.930.940.910.92Marine waters0.980.990.980.970.980.98This model has an overall accuracy of 90.79% with Level-2A imagery for CLC class level 1 classification (5 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassPrecisionRecallF1 ScoreArtificial surfaces0.850.810.83Agricultural areas0.900.910.91Forest and semi natural areas0.910.920.92Wetlands0.770.700.73Water bodies0.960.970.96Sample ResultsHere are a few results from the model. To view more, see this story.

This service displays polygons for current land use records and features used to inventory land use patterns. Data is updated, maintained and published from the enterprise GIS database to reflect the most recent information for the City of Las Cruces. Data is organized by activity, structure, or function. Layer Type: PolygonData Owner: Community DevelopmentAuthoritative: YesDownloadable: N/AInitial Dataset Creation: UnknownLast update: 2018 Update Frequency: As necessary Status: CurrentReason for Updates: Classify and inventory land use patternsSource data: N/AReference Source: Land Based Classification Standards (LBCS)Projected Coordinate System: N/AReference information: The classification is a snapshot at one particular time. Uses, businesses, and new construction occur on a daily basis. Also, there may be several parcels that make up a particular site. The classification used was the predominant use of that parcel (e.g., a residential condo plat has a parcel for common area that is mostly parking and parcels for each residential unit, the common area parcel was classed as parking and the parcels for the units as residential). It is important to note that parcel information will change if not updated. The Land-Based Classification System (LBCS) is the industry standard for classification developed by the American Planning Association. It is not an ideal system in that classification codes for certain dimensions do not exist, multiple classes may fit for any one dimension, and a level of subjectivity occurs during classification. LBCS consists of five major categories called “dimensions”: Web site can be found at: https://www.planning.org/lbcs/Five Dimensions for Classifying Land-Use DataActivity1000: Residential activities2000: Shopping, business, or trade activities3000: Industrial, manufacturing, and waste-related activities4000: Social, institutional, or infrastructure-related activities5000: Travel or movement activities6000: Mass assembly of people7000: Leisure activities8000: Natural resources-related activities9000: No human activity or unclassifiable activityActivity refers to the actual use of land based on its observable characteristics. It describes what actually takes place in physical or observable terms (e.g., farming, shopping, manufacturing, vehicular movement, etc.). An office activity, for example, refers only to the physical activity on the premises, which could apply equally to a law firm, a nonprofit institution, a court house, a corporate office, or any other office use. Similarly, residential uses in single-family dwellings, multi-family structures, manufactured houses, or any other type of building, would all be classified as residential activity.Activity Note:The five fields of Activ_20, Activ_40, Activ_60, Activ_80, and Activ_100 were used to identify different actual uses noted on a parcel. The intent was when multiple activity classes exist to determine visually the area taken up by each use (e.g., a parcel has a restaurant and an office, the office takes up 60% of the building to class the restaurant under Activ_40 and the office under Activ_60). This worked for some parcels, but many parcels had more than five possible classes or determination of square footage was difficult to determine because floor plan-site plan information was not readily available. Some pointers on using the activity field include:>On parcels having multiple activity classes an overall activity class was put under Activ_100 in order to extract data more readily. The ‘12’ class represents mixed use. Mixed use for this inventory meant a residential use existed on the same parcel with a non-residential use. It does not assess non-residential mixed use or the type of mixed use (e.g., vertical in same building or different uses in different locations on same parcel). The Activ_100 class for multiple activities used was the highest percentage class by area, except for undeveloped (9990) where the highest percentage class by area was used if 9990 area appeared to be less than 50% of the parcel area. >For contractor yards the 2013 Inventory used either 3000, Industrial-Manufacturing, as a catch-all if the activity was not very clear. It used 3300, Construction Activities, for activities related to construction contractors which is different than the APA Classification. 3300 in the APA Classification is actually describing the stage the parcel would be in physical construction.Function1000: Residence or accommodation functions2000: General sales or services3000: Manufacturing and wholesale trade4000: Transportation, communication, information, and utilities5000: Arts, entertainment, and recreation6000: Education, public admin., health care, and other inst.7000: Construction-related businesses8000: Mining and extraction establishments9000: Agriculture, forestry, fishing and huntingFunction refers to the economic function or type of enterprise using the land. Every land use can be characterized by the type of enterprise it serves. Land-use terms, such as agricultural, commercial, industrial, relate to enterprises. The type of economic function served by the land use gets classified in this dimension; it is independent of actual activity on the land. Enterprises can have a variety of activities on their premises, yet serve a single function. For example, two parcels are said to be in the same functional category if they belong to the same enterprise, even if one is an office building and the other is a factory.Function Note:The five fields of Function, Funct_40, Funct_60, Funct_80, and Funct_100 were used to identify different economic types noted on a parcel. The intent was to indicate the percentage of the building on the parcel related to that function. The function field chosen mimics the activity field in most cases. Unlike Active_100, an overall function class was not put under Funct_100 on parcels with multiple functions. Structural Character1000: Residential buildings2000: Commercial buildings and other specialized structures3000: Public assembly structures4000: Institutional or community facilities5000: Transportation-related facilities6000: Utility and other non-building structures7000: Specialized military structures8000: Sheds, farm buildings, or agricultural facilities9000: No structureStructural character refers to the type of structure or building on the land. Land-use terms embody a structural or building characteristic, which suggests the utility of the space (in a building) or land (when there is no building). Land-use terms, such as single-family house, office building, warehouse, hospital building, or highway, also describe structural characteristic. Although many activities and functions are closely associated with certain structures, it is not always so. Many buildings are often adapted for uses other than its original use. For instance, a single-family residential structure may be used as an office.Structural Note:The predominant structural type class was selected when multiple structures existed on a parcel. Some pointers on using the structural field include:>1130, Accessory Units, in the APA Classification is for secondary units. The 2013 Inventory used this class to identify accessory structures like sheds, etc. Secondary units on the same parcel are noted in the Units field.>1140, townhouse, and 1121, duplex, were sometimes used interchangeably. Townhouse for the APA classification is three or more attached dwelling units. Efforts were made to correct errors, but several likely were not caught. >1150, manufactured home, should be fairly accurate. NM does allow a double-wide manufactured home set on a foundation in a single-family zone. Several instances in the 2008 inventory classed this as 1100 or 1110, single-family site built unit. Efforts were made to class these as 1150 in the 2013 inventory. >1350, Temporary Structures, was used for RV Parks that appear to be more transitory. Otherwise, 1150, Manufactured Home, was used. Site Development Character1000: Site in natural state2000: Developing site3000: Developed site -- crops, grazing, forestry, etc.4000: Developed site -- no buildings and no structures5000: Developed site -- non-building structures6000: Developed site -- with buildings7000: Developed site -- with parks8000: Not applicable to this dimension9000: Unclassifiable site development characterSite development character refers to the overall physical development character of the land. It describes "what is on the land" in general physical terms. For most land uses, it is simply expressed in terms of whether the site is developed or not. But not all sites without observable development can be treated as undeveloped. Land uses, such as parks and open spaces, which often have a complex mix of activities, functions, and structures on them, need categories independent of other dimensions. This dimension uses categories that describe the overall site development characteristics.Site Note:All efforts were made to follow the site classification. Some pointers on using the site field include:>2000, Developing Site, was used if the site was under construction. The entire Metro Verde South Phase 1C plat was used for this class. A lot of home building activity was occurring in this area, but many lots were not under construction at time of site check. Ownership1000: No constraints--private ownership2000: Some constraints--easements or other use restrictions3000: Limited restrictions--leased and other tenancy restrictions4000: Public restrictions--local, state, and federal ownership5000: Other public use restrictions--regional, special districts, etc.6000: Nonprofit ownership restrictions7000: Joint ownership character--public entities8000: Joint ownership character--public, private, nonprofit, etc.9000: Not applicable to this dimensionOwnership refers to the relationship between the use and its land rights. Since the function of most land uses is either public or

This template is used to compute urban growth between two land cover datasets, that are classified into 20 classes based on the Anderson Level II classification system. This raster function template is used to generate a visual representation indicating urbanization across two different time periods. Typical datasets used for this template is the National Land Cover Database. A more detailed blog on the datasets can be found on ArcGIS Blogs. This template works in ArcGIS Pro Version 2.6 and higher. It's designed to work on Enterprise 10.8.1 and higher.References:Raster functionsWhen to use this raster function templateThe template is useful to generate an intuitive visualization of urbanization across two images.Sample Images to test this againstNLCD2006 and NLCD2011How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual representation of urban sprawl across two images. Applicable geographiesThe template is designed to work globally.

Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS

Esri ArcGIS Online (AGOL) Hosted Feature Layer which provides access to the MDOT SHA Roadway Functional Classification data product.MDOT SHA Roadway Functional Classification data consists of linear geometric features which showcase the functional classification of roadways throughout the State of Maryland. Roadway Functional Classification is defined as the role each roadway plays in moving vehicles throughout a network of highways. MDOT SHA Roadway Functional Classification data is primarily used for general planning purposes, and for Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) annual submission & coordination. The Maryland Department of Transportation State Highway Administration (MDOT SHA) currently reports this data only on the inventory direction (generally North or East) side of the roadway. MDOT SHA Roadway Functional Classification data is not a complete representation of all roadway geometry.The State of Maryland's roadway system is a vast network that connects places and people within and across county borders. Planners and engineers have developed elements of this network with particular travel objectives in mind. These objectives range from serving long-distance passenger and freight needs to serving neighborhood travel from residential developments to nearby shopping centers. The functional classification of roadways defines the role each element of the roadway network plays in serving these travel needs. ​ Over the years, functional classification has come to assume additional significance beyond its purpose as a framework for identifying the particular role of a roadway in moving vehicles through a network of highways. Functional classification carries with it expectations about roadway design, including its speed, capacity and relationship to existing and future land use development. Federal legislation continues to use functional classification in determining eligibility for funding under the Federal-aid program. Transportation agencies describe roadway system performance, benchmarks and targets by functional classification. As agencies continue to move towards a more performance-based management approach, functional classification will be an increasingly important consideration in setting expectations and measuring outcomes for preservation, mobility and safety.MDOT SHA Roadway Functional Classification data is developed as part of the Highway Performance Monitoring System (HPMS) which maintains and reports transportation related information to the Federal Highway Administration (FHWA) on an annual basis. HPMS is maintained by the Maryland Department of Transportation State Highway Administration (MDOT SHA), under the Office of Planning & Preliminary Engineering (OPPE) Data Services Division (DSD). This data is used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Roadway Functional Classification data is key to understanding the role each roadway plays in moving vehicles throughout the State of Maryland's network of highways.MDOT SHA Roadway Functional Classification data is owned & maintained by the MDOT SHA Office of Planning & Preliminary Engineering (OPPE). This data product is updated & published on an annual basis for the prior year. This data product is for the year 2023.For more information related to the data, contact MDOT SHA OPPE Data Services Division (DSD):Email: DSD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov