Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

A method for detecting an ice on a road surface includes: providing a spectral imaging camera; recording a first reflectance (R1) of the surface at 0.545 to 0.565 μm using the spectral imaging camera; recording a second reflectance (R2) of the surface at 0.620 to 0.670 μm using the spectral imaging camera; recording a third reflectance (R3) of the surface at 0.841 to 0.876 μm using the spectral imaging camera; calculating an ice index based on the first reflectance, the second reflectance, and the third reflectance; providing a thermometer; recording a surface temperature of the surface using the thermometer; and detecting a presence of the ice on the surface based on the ice index and the surface temperature. A system for detecting an ice on a surface is also disclosed.

Apparatuses, systems and methods are provided for generating enterprise data relating to roof damage associated with weather and hail data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the base-line probable roof loss confidence scores. The apparatuses, systems and methods may generate probable roof loss confidence score data based upon the base-line probable roof loss confidence scores, weather event data and hail event data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the probable roof loss confidence score.

Apparatuses, systems and methods are provided for generating enterprise data relating to roof damage associated with weather and hail data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the base-line probable roof loss confidence scores. The apparatuses, systems and methods may generate probable roof loss confidence score data based upon the base-line probable roof loss confidence scores, weather event data and hail event data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the probable roof loss confidence score.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

A terrestrial acoustic sensor array for detecting and preventing airspace collision with an unmanned aerial vehicle (UAV) includes a plurality of ground-based acoustic sensor installations, each of the acoustic sensor installations including a sub-array of microphones. The terrestrial acoustic sensor array may further include a processor for detecting an aircraft based on sensor data collected from the microphones of at least one of the plurality of acoustic sensor installations and a network link for transmitting a signal based on the detection of the aircraft to a control system of the UAV.

A terrestrial acoustic sensor array for detecting and preventing airspace collision with an unmanned aerial vehicle (UAV) includes a plurality of ground-based acoustic sensor installations, each of the acoustic sensor installations including a sub-array of microphones. The terrestrial acoustic sensor array may further include a processor for detecting an aircraft based on sensor data collected from the microphones of at least one of the plurality of acoustic sensor installations and a network link for transmitting a signal based on the detection of the aircraft to a control system of the UAV.

Techniques for forecasting solar power generation include determining, by a computing device, respective proximity scores for a plurality of measurement devices, wherein the respective proximity scores are based on proximity of the measurement devices to a photovoltaic installation; determining, by the computing device, respective bearing scores for the plurality of measurement devices, wherein the respective bearing scores are based on respective angular offsets between the plurality of measurement devices and an azimuth of the photovoltaic installation; selecting, by the computing device, a first measurement device in the plurality of measurement devices using the respective proximity scores and the respective bearing scores; and predicting, by the computing device, a solar power generation level for the photovoltaic installation based on data obtained from the first measurement device.