1.0
DCPLUS
20200422
20360300
20200609
20360300
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7da06f54cc3747e9958d1aa267cfafdd
Feature Service
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Linear Surficial Features of Canada (Canadian Geoscience Map 195)
2020-04-22T20:36:03
2020-06-09T21:52:41
surficial
esker
moraine
Surficial geology of Canada (Canadian Geoscience Map 195, 2014) - Linear features (eskers, moraines)
Linear features (eskers, moraines) extracted from the new surficial geology map product (Geological Survey of Canada, <b>Canadian Geoscience Map 195</b>, 2014, 1 sheet, <a href='https://doi.org/10.4095/295462' rel='nofollow ugc' target='_blank'><b>https://doi.org/10.4095/295462</b></a>) that represents the conversion of the map "Surficial Materials of Canada" (Fulton, 1995) and its legend, using the Geological Survey of Canada's Surficial Data Model (SDM version 2.0) which can be found in Open File 7631 (Deblonde et al., 2014). All geoscience knowledge and information from map 1880A that conformed to the current SDM were maintained during the conversion process. However, only terrestrial units are depicted on this map. Map units below modern sea level or major lake levels are not shown but are maintained in the digital data of this publication. Where additional information was required in certain regions of the Arctic and Cordillera, legacy geology map data were used. These maps are listed in the digital "Map Information" document. All other source maps used in map 1880A are not relisted here. The purpose of converting legacy map data to a common science language and common legend is to enable and facilitate the efficient digital compilation, interpretation, management and dissemination of geologic map information in a structured and consistent manner. This provides an effective knowledge management tool designed around a geo-database which can expand following the type of information to appear on new surficial geology maps.
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
7da06f54cc3747e9958d1aa267cfafdd
https://services3.arcgis.com/GSr8HAQhtEt4sNnv/arcgis/rest/services/Surficial_Linear_Features/FeatureServer
Feature Service
EPSG