With the growing trend of disaster vulnerability, the need to increase resilience of communities has become a global challenge. Humanitarian organisations (HOs) are under pressure to deliver appropriate and efficient response, especially when sudden-onset hazards strike. In response to this need, humanitarian logistics (HL) has emerged to deliver relief efforts pre- and post-disaster leveraging common tools, techniques and practices from the commercial logistics and supply chain management. Examples of logistical decisions that need to be made before and after a disaster strikes include strategic as well as operational decisions such as the design of relief network, developing evacuation plans, partnerships with logistics service providers, prepositioning of relief items, route selection, and last-mile delivery of relief items to the affected communities. Among the biggest challenges of HL are lack of reliable data and time restrictions for implementing the relief operations. This research project aims to address this challenge by taking a data-driven approach using a combination of environmental and disaster-related data to develop reliable yet fast decision support systems, updated in real-time, that can be used by HOs and local authorities in their relief operations. Examples of such data include Copernicus data, satellite imagery or drones data, disaster scenarios, GIS data, GDACS alert system, and databases of disaster risk to infrastructure and social networks. The data-driven methodologies will be developed and validated using case studies in disaster prone areas such as the megacities of Tehran and Istanbul contrasted with the challenges of small, isolated communities such as islands and the Arctic.