Abstract: A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

Abstract: A computer-based method includes receiving, at a computer-based system, an aerial image of a property that includes a first visual indicator on the aerial image that follows and identifies a boundary line for the property; using a building rooftop Deep Fully Convolutional Network (DFCN), configured and trained to predict the presence of building rooftops in aerial imagery, to predict whether any building rooftops are present within the boundary line of the property based on the aerial image; and applying a second visual indicator to the aerial image to identify and outline a building rooftop in the aerial image identified by the building rooftop deep fully convolutional network. In some implementations, other Convolutional Networks (ConvNets) are used to predict other property attributes and characteristics.

Abstract: A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

Abstract: A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

Abstract: A computer-based method of formulating and delivering dynamic, severity-based weather peril scoring includes ingesting human-observed weather data and radar weather data, applying a grid that has a plurality of cells to a map of a geographical region to divide the geographical region into a plurality of areas, with each area being defined by a corresponding one of the grid cells, for each area/grid cell: calculating, with a computer-based processor, a first severity-weighted risk index for at least one weather peril based on the human-observed weather data, calculating, with the computer-based processor, a second severity-weighted risk index for the at least one weather peril based on the radar weather data, and blending, with the computer-based processor, the first severity-weighted risk index and the second severity-weighted risk index to produce a blended risk index.

Abstract: A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

Abstract: A computer-based method of formulating and delivering dynamic, severity-based weather peril scoring includes ingesting human-observed weather data and radar weather data, applying a grid that has a plurality of cells to a map of a geographical region to divide the geographical region into a plurality of areas, with each area being defined by a corresponding one of the grid cells, for each area/grid cell: calculating, with a computer-based processor, a first severity-weighted risk index for at least one weather peril based on the human-observed weather data, calculating, with the computer-based processor, a second severity-weighted risk index for the at least one weather peril based on the radar weather data, and blending, with the computer-based processor, the first severity-weighted risk index and the second severity-weighted risk index to produce a blended risk index.

Abstract: Systems and methods for creating linear models from historic crop yield reports and end of year crop estimates based on geo-spatial crop yield reports and a large plurality of index vectors wherein a large percentage of the index vectors are based on weather data variables and heuristic domain specific formulas that use weather data variables from a geo-spatially encoded weather data variable production system via incremental feature selection of index vectors and a crop yield predictor that determines a crop yield prediction and range of potential predictions via the use of principal component analysis of the weather data variable time series used in said plurality of index vectors and heuristic domain specific formulas.