Mozambique GIS data

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

**ADAM ID: **1001154\_34 Tropical storm during the period Mar 08 2025-Mar 16 2025 in Mozambique, Madagascar. The center of the storm was located near latitude -26.04 longitude 51.93. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Mozambique **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.3603 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0231 million km (6.4173%) - Unpaved roads: 0.0801 million km (22.2253%) About 0.2571 million km (71.3574%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0005 million km (0.2115%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Maganja da Costa, Moaquival and Namacurra Districts, Nampula Province, Mozambique as observed from a RCM-1 image acquired on 15 March 2022 at 05:10 local time. Within the analyzed area of about 1500 km2, about 237 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 22,600 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

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We have assessed primary, secondary, and tertiary roads for road blockages (flooding or debris) for Maputo and South Sofala in Mozambique following flooding. The data is held in a shapefile format using the WGS 84 EPSG: 4326 projection. You can find the Planetary Response Network Mozambique Flooding 2026 project we ran here.

This dataset contains OpenStreetMap data exported from the HOTOSM disaster mapping in response to flooding events in the Gaza, Maputo and Maputo City Provinces of Mozambique (GLIDE: FL-2026-000004-MOZ) in early 2026. Severe flooding in Mozambique has affected more than half a million people following heavy rains in the first weeks of the new year. Gaza province has been hit hardest, alongside Maputo and Sofala provinces. The first OSM extract was based on Copernicus' flood extend from 29 January 2026. The first disaster response mapping project focused on settlements around Chicumbane in the Province of Gaza which, from 9 February, expanded to other settlements in the province. It now includes additional settlements further north in the Province. HOT worked with UNICEF, INGD (Mozambique Disaster Risk Management Institute) and OSM Mozambique to define priority areas and features. The data has been contributed by volunteers through the Humanitarian OpenStreetMap Team's Tasking Manager and represents buildings and roads in the affected area. It was updated once or twice a week as mapping progresses but is now updated every other week. Subscribe to this dataset to be notified of any new updates. This dataset includes all OpenStreetMap features in the area of interest for this event, covering: Buildings, Roads. See the list of projects in the Tasking Manager here. For more information see the HOTOSM activation page. As of 22 April 2026: over 3,300 contributors (non unique).

**ADAM ID: 1001053\_10** Cyclone (tropical storm) during the period Mar 03 2024-Mar 13 2024 in Mozambique, South Africa, Eswatini, Zimbabwe, Malawi. It impacted 0 people.

**UNOSAT code: FL20251229MOZ** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 31 December 2025, using an automated machine-learning-based analysis. Within the analysed area of approximately 32,000 km², an estimated 930 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 4,200 people are exposed to or living in close to the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ, GDACS ID: 1103694** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 5 January 2026, using an automated machine-learning-based analysis. Within the analysed area of approximately 25,000 km², an estimated 870 km² of land appears to be affected by floodwaters, representing an increase of about 100 km² compared to 31 December 2025 within the same analysed area. Based on WorldPop spatial demographic data, approximately 7,300 people are exposed to or living in close proximity to the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 23 January 2026. Within the analysed area of approximately 43,500 km², an estimated 3,000 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 230,000 people are exposed to or living near the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Maganja Da Costa, Namacurra & Nicoadala Districts, Zambezia Province, Mozambique as observed from a TerraSAR-X image acquired on 22 March 2022 at 05:01 local time. Within the analyzed area of about 6,174 km2, about 496 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 49,100 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220124MOZ** This map illustrates satellite-detected surface waters in Zambezia Province, Mozambique as observed from a Sentinel-1 image acquired on 28 January 2021 at 05:00 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 44,000 km2, about 860 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population are mainly located in the districts of Maganja Da Costa with ~22,500 people, Namacurra with ~17,000 people and Pebane with ~7,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

Mozambique administrative level 0-4 boundaries (COD-AB) dataset version 02. This dataset is structured into 4 levels: - Admin 1: 11 província - Admin 2: 161 distrito - Admin 3: 457 posto - Admin 4: 1712 localidade Dates associated with this dataset: - 10 March 2026: dataset reviewed for accuracy and completeness - 01 January 2025: valid for use by the humanitarian community - 10 March 2026: last edit to the dataset before publishing - 01 January 2017: boundaries created by the source Contributed by OCHA Mozambique. Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

**UNOSAT code: FL20251229MOZ, GDACS ID: 1103694** This map illustrates cumulative satellite-detected water extent derived from Sentinel-1 imagery acquired on 23 and 24 January 2026. Within the analysed area of approximately 64,000 km², an estimated 4,800 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 252,000 people are exposed to or living near the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Mossuril, Mogincual and Liúpo Districts, Nampula Province, Mozambique as observed from a Sentinel-1 image acquired on 13 February 2022 at 17:44 local time. Within the analyzed area of about 14,000 km2, about 110 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 12,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2016