MUIR - Multi-Layer Understanding of urban Infrastructure and Response

Natural disasters and battle can cause immense damage to visible structures such as buildings, roadways, power-lines, but also to underground gas, power, and water infrastructure. This damage can cause significant safety hazards and be catastrophic to the way-of-life for locals while also hindering disaster response times, assessments, and repair. Disaster response efforts typically require heavily trained teams to manually operate complex equipment to assess and localize damage to infrastructure. After damage assessment is performed, there is typically another manual effort to plan the reconstruction of the infrastructure. The goal of our proposed “Multi-Layer Understanding of urban Infrastructure and Response” (MUIR) approach is to automate and expedite these and similar assessment and response efforts. This shall be accomplished by enabling lightly trained teams to rapidly deploy one or more pre-configured toolboxes with pre-packaged sensors, platforms, and algorithms to quickly map infrastructure, assess damage, and virtually reconstruct the infrastructure while accounting for uncertainty and possibly inaccurate priors (e.g. subject matter expertise, utility maps).    Our proposed solutions will allow first responders faster situational awareness for timely and impactful positioning and planning of resources, as well as for proper deployment of assistance, while also assessing the potential safety risks/threats in the region. Our overall technical solution includes (1) a configurable toolbox of heterogeneous platforms/sensors, (2) deployment of assets, (3) automated scene understanding algorithms (e.g. object detection, scene segmentation, contextual structural constraints) (4) damage assessment (e.g. deep network detectors and anomaly detection) to locate damage, with (5) processing on the platforms and ground station to (6) virtually reconstruct the infrastructure (7) optionally incorporate human adjudication, all while accounting for prior knowledge (8). This is all in the presence of incomplete, inaccessible, and/or dynamic observations that can change due to natural factors such as wind, rain, or snow. Infrastructure reconstruction will allow a more optimal allocation of both manned and unmanned vehicles operating in a highly dynamic and risky environment. MUIR proposes a solution capable of supporting various sensors and platforms while accounting for scalability.