#587 - Ebola Outbreak, Panguma area road network (Sierra Leone) -- Updated Imagery, 2014-07-12

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  • Author: HOT
  • Requesting org.: IFRC / Red Cross
  • Priority: High

The International Federation of Red Cross and Red Crescent Societies (IFRC) is deploying for the Ebola epidemic going on there. More detailed basemap data (roads, villages, streets) is needed to assist in the response.


Justification

There is a resurgence of the Ebola outbreak, and a risk of the epidemy spreading with the multiplication of sites of infection. There are difficulties, including in forests of Sierra Leone with tracing the point of contact and delivering the message to the population about the infection. Both the Red Cross (redcross) and MSF (MSF) are deploying in Sierra Leone where various sites have been identified.


MapGive

Through the MapGive project, the Humanitarian Information Unit (HIU) of the U.S. Department of State is providing the OpenStreetMap community access to updated satellite imagery services to help assist with humanitarian mapping.

Created by AndrewBuck - Updated - Priority: low

  Instructions

Changeset Comment
Sierra Leone, #hotosm-ebola-sl-587, #MapGive ; source = WorldView-2, DigitalGlobe, NextView, 14 February 2014 and 6 March 2014
When saving your work, please leave the default comment but add what you actually mapped, for example "added buildings and a residential road".
Imagery

[imagery url available only after accepting the license]

Access to this imagery is limited by the NextView license agreement.

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2014 West Africa Ebola Response
Wiki Coordination Page

Please map in all roads in the area including smaller paths for as far as you can follow them. Tag roads according to the Highway Tag Africa wiki page. Most roads connecting villages should either be unclassified or tertiary with only occasional small clusters of isolated houses being connected to the road network by a path (only use path if it would be too small to even drive a 4x4 vehicle on).

Also trace landuse=residential around clusters of houses and mark large open areas (not fields, just smooth open ground next to towns) as leisure=common as these are potential helicopter landing sites. Another common and useful feature to tag is school areas, easily identified as 1 or 2 long buildings at the edge of the village often with two small toilet buildings behind them. The schools are almost always accompanied by a large open area for the children to play in, tag the whole area of the school complex with amenity=school and trace the school buildings and bathrooms (don’t worry about the buildings in the rest of the town for now).

Note: Varying issues in alignment can occur in various sections where the new provided imagery overlaps with the older Bing imagery. This affects when imagery should be shifted or not.

Underlying high-resolution Bing imagery exists around the edges of this imagery strip. There are numerous OSM features that have already been created in these areas. If you are updating this area with the newer provided imagery you must shift the imagery to align with the existing OSM features.

How to shift imagery in ID editor: In right-hand menu click on ‘Background settings’, then click on ‘Fix alignment’. You can then click on the arrow buttons to shift the imagery.

For JOSM, right click on the imagery layer in the ‘Layers’ panel on the right and then click ‘New offset’, you can then left-click drag to shift the imagery or right click drag to pan around (zoom works as normal).

The rest of the imagery only had low resolution Bing imagery coverage. For these areas no shifting is needed. Create new features aligned on top of the newly provided imagery!

Note that for some areas, clouds will obscure the view. In these areas, do your best. We will revise the mapping of these areas when new imagery is available.


Save with credit to HotOSM and MapGive. Comment the changeset with: Sierra Leone, #hotosm-ebola-sl-587, #MapGive

And use the following source tag either on the objects you create or on the changeset itself (if using JOSM): source=WorldView-2, DigitalGlobe, NextView, 14 February 2014 and 6 March 2014


Imagery Details

This imagery is from Digital Globe’s WorldView-2 satellite taken on from multiple images acquired in March 2013 and March 2014. The imagery is almost 50GB in size and has been pan-sharpened (combined panchromatic and multispectral images) for visual clarity and orthorectified for terrain corrected geographic precision. Additionally, the image has been contrast stretched using a custom stretch and processed into a Tiled Map Service (TMS) for performance.

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