The United Nations Institute for Training and Research (UNITAR) provides innovative learning solutions to individuals, organizations and institutions to enhance global decision-making and support country-level action for shaping a better future.
Description of Activities on AI
Project 1: ML4 Floods Deployment Test
The United Nations Satellite Centre (UNOSAT) partner with Trillium Technologies and FDL Europe to test and use ML4Floods: an ecosystem of data, models and code pipelines to tackle flooding with machine learning ML.
Project 2: Mapping Refugee Settlement and Damage Assessment with Machine Learning and Remote-Sensing Data
The purpose of this project is the creation of an end-to-end pipeline that takes high-resolution satellite imagery as input and returns a damage assessment in the form of a building footprint together with a damage class label.
Project 3: UNOSAT S-1 FloodAI
The United Nations Satellite Centre (UNOSAT) has recently launched UNOSAT S-1 FloodAI: an end-to-end pipeline where Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery of flood-prone areas are automatically downloaded and processed by a deep learning model to output flood vector data and update operational dashboards. Access to timely and accurate data could not only inform the decision-making process to help optimize the disaster response, but it also has the potential to significantly reduce the loss of life and mitigate structural damage, particularly in the context of humanitarian operations, thus supporting both national authorities and international emergency management organizations for the benefit of local populations.