Imagecat, Inc.

Under the 2011 Nasa Roses Program Grant Developing Global Building Exposure for Disaster Forecasting Mitigation and Response Imagecat and the Center for International Earth Science Information Network (CIESIN) Established Methods of Developing Building Exposure Databases for Modeling Damage From Natural Disasters. the Project Team Processed Earth Observation (EO) Data to Estimate Building Exposure Based on Stratified Sampling and Data Fusion. Along With Eo Data the Final Product Integrates Expert Opinion Field Surveys and Demographic Data to Assure That What Is Known About the Built Environment Is Integrated Into Spatial Datasets Without Skewing Risk Assessments Towards Inventoried Assets. This Project Resulted in Approximately 50 Country-level or Regional Building Exposure Databases for Use in Various Risk Management and Loss Estimation Programs Including Global Facility for Disaster Reduction and Recovery (GFDRR) Zurich Insurance Unicef Transre Usaid and Others. But Buildings Do Not Constitute the Entire Risk. Cities Are Complex Systems With Interconnected Lifeline Networks Propelled by Critical Infrastructure Which Can Sever. Following Hurricanes Maria and Katrina and the T.hoku Earthquake and Tsunami Damage to These Systems Resulted in Cascading Effects That Severely Impeded Recovery and Crippled Regional Economies. Gis Data Gives the Location of Critical Infrastructure (CI) and Can Be Used to Identify and Mitigate Damage But in Many Cases the Location of Key Components Are Unmapped or Unshared Particularly in Developing Countries. Without Finding These Assets It Is Not Possible to Identify Where the Regional Risk From Infrastructure Disruption Stands to Result in Cascading Effects Which in Some Cases Could Significantly Reverse Progress in Developing Countries.. Open Critical Infrastructure Exposure for Disaster Forecasting Mitigation and Response Will Build on These Accomplishments to Expand the Ability to Model the Catastrophic Impacts of Infrastructure Disruption by Providing a Foundation for Ci Exposure Development With Eo. the Team Will Start in India and Expand to Developing Countries Globally Prioritizing Based on End-user Requirements. as With Buildings Development Is a Data Fusion Process Requiring Collection of Existing Datasets and Use of Segmentation and Edge Detection Algorithms to Build Skeletal Networks Which Are Augmented Statistically Through Stratified Sampling. Viirs and Other Sensors Are Used to Identify Major Power Plants Along Transmission Corridors. Missing Smaller Facilities Are Distributed Through a Simulation Process. the Resulting Data Are Suitable for the Types of Risk Studies Prioritized by the Sendai Framework for Disaster Risk Reduction That Are Currently Being Implemented by Ngos in Developing Countries. Data Will Be Delivered Openly and Globally to Developing Countries and All Those Interested in Risk as Well as Integrated Into Commercial Products for Global Risk Identification and Management. the Decision-making Activities That These Enhanced Ci Exposure Databases Will Support Include a) Better Understanding and Pricing of the Risk Transfer Mechanism for Insurers in New and Existing Markets B) Better Risk Mitigation Communication and Reduction Strategies for Government Agencies and Ngos and C) Enhanced Capabilities of Government and Ngos to Perform Post-disaster Needs Assessments. This Proposal Addresses the Applied Sciences Program Goal of Integrating Earth Science Data and Information as Well as Addressing Several Nasa Priorities Including 1) Risk Assessment and Risk-based Prediction Monitoring and Preparedness 2) Damage Assessment and Recovery and 3) Resiliency (mitigation and Adaptation). .