Lightwave Algorithm for a Radiometric Entrainment System

LARES will enable assessors to utilize vast amounts of data by applying algorithms for pattern recognition and machine learning into an entrainment algorithm. Such a process will in turn give us the possibility to exploit freely available earth observations to determine deviations in electromagnetic emissions which can indicate a roof leakage, a solar panel failure, or urban hotspots. The processing of these data today is time consuming, demands expensive use of resources, and generates too much data to be feasibly handled by human beings. The project aims to reduce the human workload in sorting earth observation data for insurers, property investors and the bank sector. Today, financial risk of a property asset is based on empirical data of material cost and lifespan, experience and area statistics. The project aims to enable insurers, property management, investors and other stakeholders to proactively protect and manage their assets by using the rapidly available metrics for maintenance cost budgeting, insurance risk assessment and factual depreciation of property value. The product will be a business intelligence tool presented in an online portal, with multiple subscription and single purchase options. The processed data will be made available together with a simple cost index factor for the asset owner or the insurance provider. In the first iteration of the portal, data will be available for compact materials in roof structures and solar panels. The portal will be available with tier membership ranging from single property and private cooperatives, professional property managers to large enterprises. All memberships will give different options and analysis detail level. LARES will be a Pan-European Business intelligence tool with valuable metrics to decisionmakers and stakeholders giving valuable insights of their property assets.