Cyclone Idai first made landfall in Mozambique on Thursday, March 14, 2019, affecting tens of thousands of people across the country and neighboring Malawi. The Ngangu Township in Chimanimani and the Rusitu Valley communities were the hardest hit. The UN Office for the Coordination of Humanitarian Affairs stated that the impact is yet to be established, however reports indicate loss of life and significant damage, with extreme flooding causing loss of roads, homes and infrastructure. Officials estimate the severe flooding will impact more than 1.5 million people in Mozambique and Malawi, with possibly more than 1,000 people reportedly killed and hundreds more missing.
When crises like this occur, DigitalGlobe is committed to supporting the humanitarian community and fulfilling Maxar’s purpose of Building a Better World by providing critical and actionable information to assist response efforts. As part of our Open Data Program, DigitalGlobe will publicly release data of the affected areas to support disaster response as it becomes available.
Any imagery or data distributed through the Open Data Program is licensed under the Creative Commons Attribution Non-Commercial 4.0 license (CC BY-NC 4.0). This licensing allows for non-commercial use of the information, meaning it can quickly be integrated into first responder workflows with organizations like Team Rubicon, the Red Cross and other non-profits. If commercial companies are interested in using the data distributed through the Open Data Program, it can be purchased by contacting DigitalGlobe.
For this Open Data Program activation, DigitalGlobe provided pre- and post-event WorldView optical imagery, MDA RADARSAT-2 synthetic aperture radar imagery, and Ecopia Building Footprints powered by DigitalGlobe, which are high-precision, GIS-ready building footprints created using Ecopia.ai’s proprietary artificial intelligence algorithms combined with DigitalGlobe’s current, high-resolution commercial satellite imagery, resulting in a greater than 95% accuracy rating, a uniquely high percentage in the industry. In this Building Footprints dataset, each building is represented by a center point, creating a map of infrastructure that was built before Cyclone Idai made landfall.