> Clean up as necessary (there are some extraneous ways at state > boundaries & elsewhere)
BLM manages about 10% of the total area of the United States, and those areas historically have had the least resources dedicated in terms of mapping. Also, the BLM data is an amalgamation of data from corporate ( mining usually ), various government agencies at local, county, state, and federal levels. This data from other sources may have been incorporated piece meal over many decades, and not updated since ( like 'way back' US Census Tiger line files ). Some of these conflation issues are discussed in this article: https://www.esri.com/news/arcuser/0110/accuracy-precision.html With any of these datasets, one should read ( and understand ) the metadata associated with the file. For example: https://pubs.usgs.gov/ds/821/downloads/metadata.txt - this had a fairly rigorous QA to fairly current imagery. So it might actually be much better than the existing data in OSM that came from the TIGER import ( which was in turn derived by the US Census from much older data ). The opposite situation can also occur - some BLM data was manually from paper maps. Exactly what is the decision tree when these issues are encountered? A read through that metadata can give an idea of how they parsed out the issues, for that theme. For instance, frequently area BLM boundaries follow watercourse, and I do a check against the best available orthophotography and the USGS 3DEP ( https://www.usgs.gov/news/new-elevation-map-service-available-usgs-3d-elevation-program ). Similarly with ridge lines. Boundaries with USDA National Forest and state forest frequently interweave. Also, one theme ( layer ) for an area might be excellent and the other just crap - BLM manages for lots of different uses, and some just get more attention than others. There's a high, a middle, and a low road here - most likely for low end expediency you'd just leave any existing OSM lines in place even if they varied considerably, and just import any BLM information if it didn't already appear in OSM, in the middle you'd pick the best and adjust the OSM data where it was considerably different, and the high would be a thorough effort. At one point there was a plug-in for openJUMP called RoadMatcher that might be useful, last I looked the tools in qGIS were lagging. ESRI sells a 'personal edition' version of ArcGIS for a $100 that has extensive tools for these tasks, it becomes worth it in about two hours. Just curious ... what makes those 'extraneous ways at state boundaries', extraneous? > Clean up as necessary... So, I guess I'd be interested in how you get to the necessary. The BLM data covers an area about half the size of Europe. Michael Patrick Data Ferret
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