Incorporating Earth Observation (EO) into natural resource development is increasingly essential as countries strive to increase domestic capacities and resilience. This includes supporting the development of new mines and energy projects and the associated monitoring and reclamation requirements for these developments. In Canada, thousands of new infrastructure and resource projects are proposed or under review each year, placing significant strain on regulatory systems and underscoring the need for efficient, scalable monitoring solutions. At the same time, Earth Observation (EO) technologies are transforming how we understand, plan for, manage, and monitor natural resource development. These tools—encompassing technologies in radar and optical remote sensing in both the public and commercial sphere, as well as big data systems and AI-enabled analytics offer powerful, scalable solutions to support regulatory decision-making, impact assessment, and land reclamation. Yet uptake of these technologies across policy, regulatory, and operational contexts remains limited. Persistent barriers include gaps in EO awareness, lack of standardized methods, challenges with integrating Earth Observation data into traditional field based assessment and workflows, and uncertainty about cost-effectiveness or legal defensibility. Bridging this gap requires both top-down and bottom-up approaches: coordinating national efforts to produce high-quality EO datasets and workflows, alongside regional, practitioner-driven initiatives that compare new tools to legacy methods and build the business case for adoption. This open session will showcase efforts to develop EO methods and technologies to support resource development and to operationalize EO for impact assessment processes and for environmental monitoring and reclamation purposes.

The increasing frequency and intensity of floods, in conjunction with evolving land use and land cover that fundamentally alter hydrological flows, underscores the need for more adaptive and scalable approaches to flood mapping. Historically, flood mapping relied on numerical hydrodynamic simulations or interpreted from earth observation data. Both require extensive time, expertise, and computational resources. Recent developments in artificial intelligence (AI), machine learning, and remote sensing technologies are reshaping the way we approach flood mapping. These technologies offer powerful new ways to complement (or, in some cases replace) traditional methods, enabling faster and more scalable flood predictions with comparable accuracy. Part of what makes these AI-driven approaches compelling is the ability to integrate vast datasets - including EO imagery, topography, weather data, and social media inputs - to deliver real-time and/or predictive flood insights. In this session, we will take a closer look at how the field of flood mapping is evolving in response to innovative technologies, growing data availability, and changing environmental conditions. Presenters will discuss the latest AI-driven techniques, hybrid modeling frameworks, and the associated challenges.

To understand and cope with the negative impacts of rapid urbanization and climate changes, comprehensive information about characteristics and dynamics of urban land surfaces are crucial for developing strategies to build resilient urban environments. As more high-resolution remote sensing sensors and imagery data become available, remote sensing plays essential roles in both urban information collection and EO-based insight generation for supporting urban climate and environment resilience, disaster mitigation and monitoring, and urban planning. In recent years, the scope of Canadian remote sensing society has been extended to more urban applications, along with the remote sensing application interest trends globally, Canada has made substantial progress in utilizing remote sensing technologies to address urban challenges and manage sustainable development. The ongoing research and development in remote sensing, coupled with the integration of machine learning and other advanced techniques, promise further advancements in the future. In this special session, we will present several Canadian showcases of recent developments in remote sensing technologies for various urban applications. The presentations will be made by key players from academia, municipalities and federal laboratories. For promoting future collaborations, an invited presentation from Europe will present in this session about the findings learned in EO-based technology development for European urban applications.