The BIGMIG service objective is to alleviate forced migration through intelligent use of space based big data. The service will provide tools capable of aiding in both migration prevention and management, and expand in scope to meet the needs of new users as they arise.
Users and their needs
Services are targeted at Non-Governmental Organisations (NGOs), and other international aid and development organisations.
Two use cases - agriculture in Northern Mozambique and detection of damage to road networks, buildings and agriculture in the aftermath of a natural disaster in Southern Mozambique– are addressed in the project.
A sample of the needs identified by the target users include:
Use Case 1:
- Analyse natural resources along migration routes, namely: crop extend and evolution.
- Monitor (abnormally low) crop production as an indicator of forced migrations.
- Improve agricultural production.
- Retrieve natural resources scarcity indicators for the early warning of humanitarian crisis.
- Develop new technological packages suitable for the Mozambican socio-demographic and geo-ecological conditions that will allow a sustainable agricultural production allowing an adequate food and income production.
Use Case 2:
- The size (extension) and level (of depth) of washed away land and the disruption of soil.
- Receding waters after cyclones/ calculation of volumes of soils to be replaced.
- The roads/ bridges damaged/ destroyed in the aftermath of each case;
- Analysis of houses, schools and other facilities for common use damaged during the various crisis.
- Damage to agriculture after catastrophic events.
- Reference map of infrastructures: linear, buildings, etc.
- Easy and quicker access to the geo-information products.
Northern and Southern Mozambique.
Service/ system concept
Two use cases have been selected for the BIGMIG demo phase:
Use Case 1 – Improving farming efficiency in Northern Mozambique and support to the overall community’s resilience to forced mitigation (anchor customer: Ayunda en Acciòn). The products include a Land Use Land Cover map and a crop type map.
Use Case 2 – Support Helpcode in planning an improving efficiency of aid delivery in Southern Mozambique in the aftermath of cyloneIdai by using satellite imagery (anchor customer: Helpcode). The products include building damage layer, infrastructure damage layer and crop damage map.
BIGMIG combines remote sensing expertise with state of the art deep learning methods to generate valuable data products from raw EO data. The data products are available to visualise on a customizable web portal by the users.
Space Added Value
Space assets considered in the project are:
- Optical satellite imagery (Sentinel-2 time-series).
- Very High Resolution (VHR) optical imagery.
- GNSS (e.g. GPS).
Satellite imagery is used to extract information related to landcover (including cropland extent and expansion) and most importantly crop type. The EO data is combined with ground data spanning several years to enable classification of several crop types. VHR satellite imagery is well suited to identify damaged buildings and infrastructure in Southern Mozambique.
The satellite data is complemented in one case by extensive ground data to train machine learning algorithms to map crop type. Whilst, geo-intelligence data related to the location of damaged infrastructure is provided to provide to verify the damage on the ground.
The BIGMIG DEMO represents a successful continuation from a feasibility study concluded in 2018. The 18 month project started in January 2019. GMV have worked closely with two end users to gather their needs, then translate these needs into requirements followed by geo-information products, then mapped onto system requirements and finally performed a design of the system and service architecture. The result is a system focused around two distinct demo cases, namely: generation of a Land Use Land Cover Map (LULC) based on a Sentinel-2 mosaic for 2016 for Northern Mozambique, and monitoring the aftermath of cyclone Idai in Gorongosa District (Mozambique) with focus on infrastructure, building and crop damage. The team mapped crop damage in Gorongosa and produced building and road network damage maps for Nhamantanda.