| Popis: |
Major humanitarian organizations face the crucial challenge of estimating land damage from conflict in developing countries. A lack of on the ground data collection motivates the use of satellite imagery to meet this challenge. However, existing analysis methods involving satellite imagery are time-consuming, require special expertise, or lack automation. To mitigate these obstacles, SatDash was designed using Sentinel-2 images and ACLED data to provide a classification of areas that have undergone land damage due to conflict in northwestern Nigeria and Mali. SatDash was constructed using free and publicly available images and is accompanied by a user-friendly dashboard that allows domain experts to train their own data and export it for future use. The dashboard was created for a humanitarian organization, referred to as the DAAO, Damage Assessment and Aid Organization, and the design process adhered to four primary recommendations for a successful AI for Social Good (AI4SG) partnership that are further detailed in this paper. Within this paper, I draw attention to the context of CHI4Good research, detailing how the deployment phases of such systems often have their own set of potential barriers, along with describing ethical challenges that arise with this type of research. This paper focuses primarily on the design process and responses to both the constraints mentioned in literature and those presented by the DAAO. I acknowledge that AI applications, especially in development contexts, require close attention and context-specific awareness, and this is reflected through the conscious decision to include domain experts and ensure that the tool is only used for its intended purpose. When designing SatDash, the primary aim was to think critically about the involvement of local context and spur the conversation about inclusive design of similar systems in a large organization such as the DAAO. This research affirms that satellite imagery data can be used to assist humanitarian aid organizations with land change detection and demonstrates how human-in-the-loop systems can aid these organizations with identification of communities negatively impacted by hunger and recurring conflict. |
