In crisis contexts, using satellite imagery can provide evidence of possible impacts of violent conflict in hard-to-reach areas, detected through land cover and land use changes. For instance, monitoring agricultural changes can help assess vulnerabilities in areas where high levels of insecurity may have hindered the access of farmers to their fields. Detecting new structures or the abandonment of settlement in conflict-prone regions can help track population displacement patterns.
Because they complete the information available in areas that are usually hard-to-access with little to no recent field data, those outputs can be absorbed into a variety of national or regional assessments (on security, displacement, food security, others). Besides, because of the spatial precision enabled by the high-resolution EO imagery employed, such products can help organisations better target areas impacted and in need of assistance.
Our team combines years of experience working in LCLU change detection from satellite imagery in conflict-prone areas across Africa and other regions where insecurity has intensified in recent years.
Use of cutting edge technology:
- We use open source technologies and data. This reduces the cost of producing maps and also ensures that clients are free to use the data with no restrictions.
- By using high resolution images, our results are very granular, with maps providing information at locality level. This technical opportunity can really change the way humanitarians find reliable evidence in conflict-prone areas.
- Combining our satellite-derived results with insecurity data (such as ACLED dataset, or any field-based database collecting violent events or other insecurity data) to highlight any possible spatial correlations between the two.
Our Results:
- Our team has experience in mapping areas with sharp agricultural decline, usually showing a strong spatial correlation with incidence of conflict (Sahel, Haiti, others).
- Tracking population displacement by detecting village abandonment and expansion of formal and informal camps (between South Sudan and Uganda; Myanmar and Bangladesh).
- Developed a system that leverages machine learning to automatically detect the number of structures in IDP and refugee camps (formal and informal) from high resolution satellite imagery.