Using remote sensing (RS) and explainable AI (XAI) to enhance disaster risk management (DRM) and disaster risk reduction (DRR) presents several challenges. RS provides vast amounts of geospatial data, but integrating it with AI models requires addressing data quality, resolution, and timeliness issues. XAI, while improving transparency in AI decision-making, struggles with balancing complexity and interpretability, especially in high-stakes disaster scenarios.

A key challenge is the lack of labeled data for training AI models, as disaster events are rare and diverse. Additionally, RS data often contains noise and requires preprocessing, which can introduce biases. XAI models must also be tailored to non-expert stakeholders, such as emergency responders, to ensure actionable insights.

Furthermore, integrating RS and XAI into existing DRM frameworks requires overcoming technical, infrastructural, and institutional barriers. Ethical concerns, such as data privacy and algorithmic bias, also need addressing. Despite these challenges, combining RS and XAI holds promise for improving disaster preparedness, response, and recovery, provided these issues are systematically addressed.