Aligning Geospatial AI for Disaster Relief with The Sphere Handbook

Sagar Verma (1, 2, 3) , Siddharth Gupta (3) , Kavya Gupta (2, 1)

1 OPIS - OPtimisation Imagerie et Santé
2 CVN - Centre de vision numérique
3 Granular.ai

The Sphere handbook and its core premise of right to life with dignity have been broadly adopted, establishing a standard operating procedure for global humanitarian intervention. Plenty of machine learning methods aim to aid in disaster relief. While performing exceptionally on a machine learning task, these methods fail to deliver targeted effort to the victims of natural disasters. We argue that this is due to the misalignment of such methods with real-world relief practices. This paper presents the alignment of the Sphere guidelines with Geospatial AI solutions. We show several limitations in different machine learning methods proposed for disaster relief in recent years. We take the case of WASH requirements during flood disasters, extend these models to align with Sphere guidelines, and build a solution that has a much better potential to serve individuals stuck in disasters.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Environnement et Société; Etudes de l'environnement; Intelligence artificielle [cs.AI]

Sagar Verma, Siddharth Gupta, Kavya Gupta. Aligning Geospatial AI for Disaster Relief with The Sphere Handbook. 2022. ⟨hal-03861146⟩