Reference map

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea)., 1.     INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limi..., , # Satellite images and road-reference data for AI-based road mapping in Equatorial Asia

https://doi.org/10.5061/dryad.bvq83bkg7

1. INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).  2. FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, cited below:  Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Remote Sensing. 16(5): 839. [https://doi.org/10.3390/rs16050839](https://doi.org/10.3...

Structures data contains name and location data for selected manmade facilities. These data are designed to be used in general mapping and analysis of structure related activities using a geographic information system (GIS) For mapping purposes, structures can be used with other GIS data themes to produce general reference maps as a base map dataset.

Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019.

Number of sample sites: 500

Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos.

Tool for data collection: Geo-Wiki

This dataset contains raw eye-tracking data collected during a study "Students’ strategies of familiarisation with a general-reference map of an unknown area" investigating how upper-secondary students familiarise themselves with a general-reference map of an unfamiliar area. The study involved 20 participants (aged 18–20), who were given 60 seconds to explore a specially prepared map using a screen-based eye tracker (SMI RED 250, 250 Hz).The dataset includes number of fixations as well as fixation in predefined Areas of Interest (AOIs), such as the map face, legend, hypsometric tints, and the graphic scale. The data were exported from SMI BeGaze and converted using the SMI2OGAMA converter for further processing. Format: txt.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The INDIGO Change Detection Reference Dataset

Description

This graffiti-centred change detection dataset was developed in the context of INDIGO, a research project focusing on the documentation, analysis and dissemination of graffiti along Vienna's Donaukanal. The dataset aims to support the development and assessment of change detection algorithms.

The dataset was collected from a test site approximately 50 meters in length along Vienna's Donaukanal during 11 days between 2022/10/21 and 2022/12/01. Various cameras with different settings were used, resulting in a total of 29 data collection sessions or "epochs" (see "EpochIDs.jpg" for details). Each epoch contains 17 images generated from 29 distinct 3D models with different textures. In total, the dataset comprises 6,902 unique image pairs, along with corresponding reference change maps. Additionally, exclusion masks are provided to ignore parts of the scene that might be irrelevant, such as the background.

To summarise, the dataset, labelled as "Data.zip," includes the following:

• Synthetic Images: These are colour images created within Agisoft Metashape Professional 1.8.4, generated by rendering views from 17 artificial cameras observing 29 differently textured versions of the same 3D surface model.
• Change Maps: Binary images that were manually and programmatically generated, using a Python script, from two synthetic graffiti images. These maps highlight the areas where changes have occurred.
• Exclusion Masks: Binary images are manually created from synthetic graffiti images to identify "no data" areas or irrelevant ground pixels.

Image Acquisition

Image acquisition involved the use of two different camera setups. The first two datasets (ID 1 and 2; cf. "EpochIDs.jpg") were obtained using a Nikon Z 7II camera with a pixel count of 45.4 MP, paired with a Nikon NIKKOR Z 20 mm lens. For the remaining image datasets (ID 3-29), a triple GoPro setup was employed. This triple setup featured three GoPro cameras, comprising two GoPro HERO 10 cameras and one GoPro HERO 11, all securely mounted within a frame. This triple-camera setup was utilised on nine different days with varying camera settings, resulting in the acquisition of 27 image datasets in total (nine days with three datasets each).

Data Structure

The "Data.zip" file contains two subfolders:

• 1_ImagesAndChangeMaps: This folder contains the primary dataset. Each subfolder corresponds to a specific epoch. Within each epoch folder resides a subfolder for every other epoch with which a distinct epoch pair can be created. It is important to note that the pairs "Epoch Y and Epoch Z" are equivalent to "Epoch Z and Epoch Y", so the latter combinations are not included in this dataset. Each sub-subfolder, organised by epoch, contains 17 more subfolders, which hold the image data. These subfolders consist of:
  • Two synthetic images rendered from the same synthetic camera ("X_Y.jpg" and "X_Z.jpg")
  • The corresponding binary reference change map depicting the graffiti-related differences between the two images ("X_YZ.png"). Black areas denote new graffiti (i.e. "change"), and white denotes "no change". "DataStructure.png" provides a visual explanation concerning the creation of the dataset.
    
    The filenames follow the following pattern:
    • X - Is the ID number of the synthetic camera. In total, 17 synthetic cameras were placed along the test site
    • Y - Corresponds to the reference epoch (i.e. the "older epoch")
    • Z - Corresponds to the "new epoch"
      
• 2_ExclusionMasks: This folder contains the binary exclusion masks. They were manually created from synthetic graffiti images and identify "no data" areas or areas considered irrelevant, such as "ground pixels". Two exclusion masks were generated for each of the 17 synthetic cameras:
  • "groundMasks": depict ground pixels which are usually irrelevant for the detection of graffiti
  • "noDataMasks": depict "background" for which no data is available.
    

A detailed dataset description (including detailed explanations of the data creation) is part of a journal paper currently in preparation. The paper will be linked here for further clarification as soon as it is available.

Licensing

Due to the nature of the three image types, this dataset comes with two licenses:

• Synthetic images:
  • These come with an In Copyright license (for the rights usage terms, see https://rightsstatements.org/page/InC/1.0/?language=en).
  • The copyright lies with:
    • the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
    • the TU Wien (https://d-nb.info/gnd/55426-1)
    • One or more anonymous graffiti creator(s) upon whose work these images are based.
  • The first two entities are also the licensor of these images.
    
• Change maps and masks:
  • These are openly licensed via CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)
  • In this case, the copyright lies with:
    • the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
    • the TU Wien (https://d-nb.info/gnd/55426-1)
  • Both institutes are also the licensor of these images.

Every synthetic image, change map and mask has this licensing information embedded as IPTC photo metadata. In addition, the images' IPTC metadata also provide a short image description, the image creator and the creator's identity (in the form of an ORCiD).

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If there are any questions, problems or suggestions for the dataset or the description, please do not hesitate to contact the corresponding author, Benjamin Wild.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Dataset contains framework layers compiled for representation on state reference map, scale 1:1.5 million. Line and polygon features only. Road, rail, waterbody and watercourse themes included. State coastline not included.

Purpose

Can be used as a framework layer for whole of state mapping or for a generalised framework for regional mapping. Not suitable for analysis.

Dataset History

Information was compiled and digitised in generalised form from 1:250 000 scale hard copy maps. The individual CAD files were combined into seamless form and converted to Lambert Conformal Conic projection, standard parallels 29 degrees and 35 degrees S, central meridian 135 degrees E. Subsequently the information was converted to GIS format and re-projected to the state standard LCC projection.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Topography - State Refence Map - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/b6f2d7af-7fbb-4bf5-9051-b725d51b270a.

These boundaries define the regions based on terrestrial and marine areas. These are intended to be used in by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

The reference rating corresponds to the altitude of the body of water modeled at the right of an existing project or construction. It is displayed in m NGF-IGN 69, i.e. in meters in the official grading network in metropolitan France which is attached to the Marseille tide gauge. The reference rating therefore does not correspond to a water height but rather to the altimetry of the water body. The water height corresponds to the difference between the reference rating and the altimetry rating of the natural terrain.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Dataset contains framework layers compiled for representation on state reference map, scale 1:1.5 million. Line and polygon features only. Road, rail, waterbody and watercourse themes included. State coastline not included.

Purpose

Can be used as a framework layer for whole of state mapping or for a generalised framework for regional mapping. Not suitable for analysis.

Dataset History

Information was compiled and digitised in generalised form from 1:250 000 scale hard copy maps. The individual CAD files were combined into seamless form and converted to Lambert Conformal Conic projection, standard parallels 29 degrees and 35 degrees S, central meridian 135 degrees E. Subsequently the information was converted to GIS format and re-projected to the state standard LCC projection.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Topography - State Refence Map layers - PED. Bioregional Assessment Source Dataset. Viewed 12 October 2016, http://data.bioregionalassessments.gov.au/dataset/431f9e07-7436-4820-9bfd-c90a0a6cc293.

The public land reference framework allows a mapping of the built and unbuilt land of public owners.It is structured according to two different orientations:• a comprehensive census of State property, local authorities and public institutions;• a refocused census on the land of the State and national public institutions with a view to implementing the mobilisation of public land for housing

Libraries data contains name and location data for selected manmade facilities. These data are designed to be used in general mapping and analysis of structure related activities using a geographic information system (GIS) For mapping purposes, structures can be used with other GIS data themes to produce general reference maps as a base map dataset.

Libraries data were updated for Maine as part of a data stewardship agreement with the USGS to provide infrastructure data for the National Structures Dataset. These data are intended for use in general mapping alone or with other data themes to produce general reference maps. Maps may serve as base maps for special purpose mapping

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The European Directive 2002/49/EC of 25 June 2002 on the assessment and management of environmental noise aims to assess the exposure to noise in the Member States in a harmonised manner. It defines them as data representations describing a sound situation according to a noise indicator, indicating exceedances of limit values, the number of people exposed. (Article 3 of the Decree of 24 March 2006 and Article 7 of the Decree of 4 April 2006). Noise cards have no prescriptive character. These are informational documents which are not enforceable at the legal level. As graphic elements, on the other hand, they can complement a local urban planning plan (PLU). As part of an urban travel plan (UDP), maps can be used to establish reference states and target areas where better traffic management is needed. To quantify the level of noise emitted by an infrastructure during an average day, two indices are used, the Lden index and the Ln index, recommended for all modes of transport at European level: — Lden: indicator representative of the average level over all 24 hours of the day, — LN: representative indicator of the average noise level for the period 22:00-6:00. (average night equivalent noise)

Noise levels are evaluated using numerical models (computer software) integrating the main parameters that influence noise and its propagation (traffic data, terrain topology, meteorological data,...). The noise maps thus produced are then cross-referenced with the demographic data of the areas concerned in order to estimate the population exposed to noise pollution. The sound level indicated on the noise maps is derived from a calculation method that gives approximate values and often higher than reality (maximalists) in a noise zone considered critical. An “in situ” noise control can precisely determine the noise to which a construction and its occupants may be exposed. The content and format of these maps meet the regulatory requirements of the European Directive 2002/49/EC on the management of noise in the environment.

Noise cards shall include, in accordance with the regulations:

• Sound level maps for a “reference situation” (so-called type a maps), showing equivalent noise-level curves in the territory. These are the layers Agregation_N_BRUIT_ZBR_R_A_LD_S_064.shp Agregation_N_BRUIT_ZBR_R_A_LN_S_064.shp

• Maps of areas affected by noise related to the noise classification of roadways in force (type b maps). Agregation_N_BRUIT_ZBR_R_B_00_S_064.shp

• Exceedance maps, representing areas likely to contain buildings with a modelled sound level above regulatory thresholds (type c maps). Agregation_N_BRUIT_ZBR_R_C_LD_S_064.shp Agregation_N_BRUIT_ZBR_R_C_LN_S_064.shp

(There is also the layer of noise sector B on motorways A63 and A64 SECTOR_BR_B_AUTOROUTE.shp)

The roads concerned were selected in accordance with the Prefectural Decree approving strategic noise maps of the Land Transport Infrastructures with an annual traffic of more than 3 million vehicles in the department of Pyrénées-Atlantiques. (Prefectural decree of 12 October 2018 n°64-2018-10-12-001).

This decree lists the main road infrastructure of the department of the Atlantic Pyrenees: — national motorways granted A63 and A64 — national N134 — departmental D2 D6 D9 D33 D37 D281 D309 D501 D635 D802 D810 D811 D817 D834 D911 D912 D918 D932 D936 D938 D943 D947 — several communal roads of the communes of Anglet, Bayonne, Biarritz, Billère, Bizanos, Gelos, Hendaye, Idron, Jurançon, Lescar, Lons, Oloron-Sainte-Marie, Pau, Saint-Jean-de-Luz

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

The reference rating corresponds to the rating of the water body modelled to the right of an existing project or construction. Therefore, the reference rating does not correspond to a water level but to the altimetry of the water body. The height corresponds to the difference between the reference and the altimeter dimensions of the natural terrain.