clm-817ee311c000c4e8
quantitative
Ramani Huria ('open map' in Swahili) trained roughly 470 university students and hundreds of Red Cross volunteers to map flood-prone Dar es Salaam onto OpenStreetMap
Permanent project revision
Revision 2026-10-14-baseline · effective 2026-10-14
Current revision. This is the snapshot used by the live entity page.
Created the first structured Evidence Passport with 11 claims and 6 source dossiers.
sha256:64eca56938ef8fcbda4451a29286a22cbd5f13ca62fa581366af79d955d5a6da
add /evidencePassport — Added claim-level evidence mapping and source dossiers.add /revisionHistory — Established a permanent, hash-addressed baseline revision.The text below is copied verbatim from the published record. Each claim names the source dossiers that carry it; caveats stay attached to the claim they qualify.
clm-817ee311c000c4e8
quantitative
Ramani Huria ('open map' in Swahili) trained roughly 470 university students and hundreds of Red Cross volunteers to map flood-prone Dar es Salaam onto OpenStreetMap
clm-4a161461dfa07ae4
descriptive
using smartphones, drones and street-view surveys
clm-26a14055113f9fc2
quantitative
By early 2019 it had mapped about 49 of the city's 90 wards and some 450,000 buildings
clm-3a6998a8a5705b5e
descriptive
the city's multi-agency emergency team DarMAERT still uses the flood atlases in disaster response
clm-37b2759dd9ed62cc
quantitative
OMDTZ, the local NGO that grew out of the project, said in a September 2025 retrospective that rapid urban growth has left parts of the map outdated
clm-2f55734a46200b85
descriptive
it keeps updating OpenStreetMap through volunteer mapathons and contract projects such as a World Bank-supported waste-data survey
clm-d598571f8ab78f80
quantitative
A peer-reviewed 2020 study by researchers at TU Delft and Deltares, with one co-author from the Humanitarian OpenStreetMap Team, found that adding the project's community-mapped drainage data made a neighbourhood flood model match validation data better than a run on terrain data alone
clm-4d5bff6796fce009
descriptive
at a level of detail it called inaccessible in many data-scarce places
clm-40857c251a2b49c5
quantitative
In 2025 DarMAERT described using the project's 2018 flood atlas to identify flood-prone areas and guide responders
clm-0c7df4f917b97d80
descriptive
The lasting value was the open dataset itself: community-generated maps outlived the funded programme and still guide disaster response
clm-96a51726918e3a36
quantitative
but a map built between 2015 and 2020 ages with a fast-growing city, and keeping it current falls to volunteers and one-off contracts
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sha256:64eca56938ef8fcbda4451a29286a22cbd5f13ca62fa581366af79d955d5a6da
src-a069b57ed1641a5eProject description: trains local university students and community members to map Dar es Salaam on OpenStreetMap for flood prevention and response; part of the Tanzania Urban Resilience Programme of DFID and the World Bank, supported by GFDRR and guided by the Humanitarian OpenStreetMap Team.
src-d4e8868c2d7286c3HOT's project page: phase from April 2015, Ramani Huria 2.0 from 2017 combining exposure and flood-hazard data; smartphone data collection; project news on drone imagery and street-view mapping (2016).
src-7d35b48669aa90e0The project's flood-resilience atlas: detailed local maps of the most flood-prone areas, combined with InaSAFE for disaster scenarios; supported by the World Bank, GFDRR and the Red Cross.
src-190fdabefa80b787Case study: since 2015 the project mapped 49 of 90 wards (about half the city), about 450,000 buildings and 1,700 schools; 470 university students trained and more than 300 Red Cross members involved.
src-e058a4f398bf2e82OMDTZ retrospective: the World Bank-funded project ran 2015–2020; DarMAERT uses the 2018 atlas to support field response; data collected 2015–2020 is outdated in fast-growing areas; OMDTZ updates OpenStreetMap through mapathons, Mapillary imagery and contract projects such as World Bank-supported solid-waste data collection.
src-132d694a23b43f9ePeer-reviewed study (TU Delft, Deltares, Humanitarian OpenStreetMap Team): Ramani Huria's community-mapped drainage data made a neighbourhood flood model correspond better with validation data than a terrain-only run; flood-extent surveys suit community mapping, drainage dimensions need trained mappers.