-
Notifications
You must be signed in to change notification settings - Fork 102
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Added Filling Osm Buildings Data Gaps In Lebanon
- Loading branch information
Showing
1 changed file
with
56 additions
and
0 deletions.
There are no files selected for viewing
56 changes: 56 additions & 0 deletions
56
_drafts/filling-osm-buildings-data-gaps-in-lebanon.markdown
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| --- | ||
| title: Filling OSM buildings data gaps in Lebanon | ||
| date: 2025-01-07 15:53:00 Z | ||
| Summary Text: "Following conflict escalation from October 2023 to November 2024, a | ||
| ceasefire deal was reached between relevant actors on 27 November, 2024. As the | ||
| country begins to recover from the months of war, information is needed on the extent | ||
| of the damage to inform humanitarian programming. Economic impact, service disruption, | ||
| and general shelter needs all need accurate damage estimates. A key factor of estimating | ||
| damage from the conflict is the location of buildings, before any were damaged, | ||
| pre-conflict. \n\n### Data gaps\n\nBased on requests from humanitarian analysts | ||
| needing accurate building footprints for precise damage analyses, HOT did an initial | ||
| assessment of OpenSteetMap and Microsoft Machine Learning buildings. We found that | ||
| these go-to data sources for open data lacked the accuracy needed for reliable damage | ||
| assessments. \n\n**Microsoft ML**: A visual analysis of [Microsoft Machine Learning](https://github.com/microsoft/GlobalMLBuildingFootprints) | ||
| (ML) buildings identified poor precision, as shown in blue below. Some buildings | ||
| are missed, and in some cases, multiple buildings are grouped together. \n\n\n**OSM**: | ||
| OpenStreetMap buildings had major coverage gaps in Lebanon, as identified by the | ||
| absence of any OSM buildings in the area shown above and data gaps in the map below. \n\n\n\n**Filling | ||
| buildings data gap in OSM** \n \nAs a precise pre-conflict building footprints | ||
| dataset did not exist and is needed to refine locations of damage detected remotely | ||
| through satellite, HOT kicked off a project in October 2024 with global volunteers | ||
| and a growing OSM community in Lebanon to crowdsource mapping pre-conflict building | ||
| footprints. \n\nBy the end of December 2024, over 100 volunteers have contributed | ||
| over 150,000 edits! We anticipate completion of the 3 southern states of Sour, Bent | ||
| Jbeil, and Marjayoun by early February, 2025. We will then review priority areas, | ||
| but anticipate mapping Jezzine, Saida, El Nabatieh, Hasbaya, and southern Beirut. | ||
| We’ve been prioritizing areas based on humanitarian requests, and initial damage | ||
| estimates. You can track the progress of areas mapped and validated in this [map](https://umap.openstreetmap.fr/en/map/lebanon-conflict-2024-tasking-manager-project-prog_1132719#10/33.3758/35.2359) | ||
| (screenshot below as of 6 Jan ‘25), and learn more [here](https://wiki.openstreetmap.org/wiki/Lebanon:_Conflict_-_October_2024). \n\n\n**Example | ||
| early data improvements in OSM** \n\nThe first district completed in the update | ||
| was Marjayoun southeast Lebanon. The data improvement is evident by the increase | ||
| in OSM of total count of buildings and compared to Microsoft ML building counts | ||
| in all of Marjayoun District: \n\n\n\n\n**Next | ||
| steps & identifying damaged buildings** \n\n**Data access**: All map edits are live | ||
| in OSM. You can access the most updated OSM data through [HOT’s export tool](http://export.hotosm.org/)or | ||
| [HDX](https://data.humdata.org/dataset/hotosm_lbn_buildings).\n\n**Analysing damage**: | ||
| Building footprints are commonly used as a data input in remote damage analyses. | ||
| With support from the [H2H Network](https://h2hnetwork.org/h2hsupport-package-conflict-lebanon/)’s | ||
| Lebanon Activation, HOT is working to better understand how OSM buildings are used | ||
| by partner-led damage analyses. Remote damage analyses differ depending on the leading | ||
| organization and method (eg UNOSAT and the Decentralized Damage Mapping Group). | ||
| HOT plans to create an overview of different remote damage detection methods to | ||
| serve as a data-usage guide. The overview will contain information on the data inputs | ||
| (including OSM), and how the information contained in each output can be used by | ||
| humanitarians. \n\n**How to get involved**\n\n**Mapping**: If you are interested | ||
| in mapping (digitizing) buildings in Lebanon, check out HOT’s [Tasking Manager](https://tasks.hotosm.org/) | ||
| and look for projects in Lebanon! \n\n**Damage analyses**: If you are a humanitarian | ||
| data analyst conducting or using damage assessments or OSM data in Lebanon, we’d | ||
| love to hear from you about your experience at[[email protected]](mailto:[email protected]). | ||
| We’d also appreciate any input on priority areas to map next. \n\n****All of HOT’s | ||
| work in conflict is in line with [HOT’s Data Principles](https://www.hotosm.org/tools-and-data/data-principles/). | ||
| See our [Program on Conflict & Displacement](https://www.hotosm.org/programs/conflict-displacement) | ||
| for more information.*** " | ||
| --- | ||
|
|
||
| Recent conflict has impacted the humanitarian situation in Lebanon. With damaged buildings estimated at up to 25% near the southern border, accurate data is needed to plan a humanitarian response. HOT is working with volunteers to crowdsource the mapping of pre-conflict buildings footprints to serve as a baseline dataset and improve the accuracy of damage estimates. |