Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.

Abstract: A computer-implemented method for determining field boundaries and crop forecasts in each field is provided. The method includes deriving vegetation indices for each geo-spatial pixel of each image of multi-spectral imagery at a plurality of points in time, constructing minimum bounding boxes for each image according to the vegetation indices, and generating, based on a neural network analysis of each image and the minimum bounding boxes, a geo-spatial plot of crops including a predicted plot of future crop usage for an area including each field in the multi-spectral imagery.

Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.

Abstract: A method for clustering time stamps in time series data includes receiving a one-dimensional array of ordered timestamps and an expected frequency; determining a set of time intervals; determining a first binary array that indicates whether each time interval in the set of time intervals is greater than or less than the expected frequency; determining a second binary array of differences between a corresponding pair of adjacent elements of the first binary array; appending an ith timestamp to one of a set of opening interval bounds, a set of closing interval bounds, or a set of isolated points; merging the set of set of opening interval bounds and the set of closing interval bounds into a set of cluster intervals ?; and outputting the set of cluster intervals and the set of isolated points.

Abstract: A gas sensing device includes a dielectric substrate, a heater integrated into a first side of the substrate and an insulating dielectric formed over the heater. A gas sensing layer is formed on a second side of the substrate opposite the first side. Contacts are formed on the gas sensing substrate. A noble material is formed on a portion of the gas sensing layer between the contacts to act as an ionizing catalyst such that, upon heating to a temperature, adsorption of a specific gas changes electronic properties of the gas sensing layer to permit detection of the gas.

Abstract: A gas sensing device includes a dielectric substrate, a heater integrated into a first side of the substrate and an insulating dielectric formed over the heater. A gas sensing layer is formed on a second side of the substrate opposite the first side. Contacts are formed on the gas sensing substrate. A noble material is formed on a portion of the gas sensing layer between the contacts to act as an ionizing catalyst such that, upon heating to a temperature, adsorption of a specific gas changes electronic properties of the gas sensing layer to permit detection of the gas.

Abstract: A gas sensing device includes a dielectric substrate, a heater integrated into a first side of the substrate and an insulating dielectric formed over the heater. A gas sensing layer is formed on a second side of the substrate opposite the first side. Contacts are formed on the gas sensing substrate. A noble material is formed on a portion of the gas sensing layer between the contacts to act as an ionizing catalyst such that, upon heating to a temperature, adsorption of a specific gas changes electronic properties of the gas sensing layer to permit detection of the gas.

Abstract: A computer-implemented method for determining farm boundary delineations within a target geographic area, comprising extracting data from pixels of a satellite image of the target geographic area, evaluating the data using a classification algorithm to generate one or more line segments between adjacent pixels, the one or more line segments being representative of a portion of a boundary delineation, connecting the one or more line segments to an adjacent line segment to form a boundary delineation defining at least one parcel of land within the target geographic area, and generating a boundary delineation map including the boundary delineation.

Abstract: A computer-implemented method includes accessing, by a processing unit, an existing layer representing geospatial-temporal data at a selected timestamp. A first overview layer of the existing layer is generated by iteratively aggregating each cluster of cells of the existing layer into a corresponding lower-resolution cell of the first overview layer. The first overview layer therefore has a lower resolution than the existing layer. A query is received related to the geospatial-temporal data in the existing layer, and the query is processed with reference to the first overview layer.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: High-efficiency, ultra-low power gas sensors are provided. In one aspect, a gas detector device is provided which includes: at least one gas sensor having a plurality of fins; a conformal resistive heating element on the fins; a conformal barrier layer on the resistive heating element; and a conformal sensing layer on the barrier layer. A method of forming a gas sensor as well as a method for use thereof in gas detection are also provided.

Abstract: A system and method to enhance observation resolution using continuous learning include obtaining a first image of a surface area from a first satellite, and obtaining a second image of the surface area from a second satellite. The first image has a lower spatial resolution than the second image, and temporal resolution of the first images obtained by the first satellite is higher than temporal resolution of the second images obtained by the second satellite. The method also includes determining a convolution matrix A or training a neural network, obtaining additional one or more of the first images prior to obtaining an additional one of the second images, and generating a new image from each of the one or more of the first images using the convolution matrix A or the neural network. The new image has a higher spatial resolution than the one or more of the first images.

Abstract: A method for measuring pollution that includes providing a plurality of analyte sensors arranged in a grid over a sensing area, wherein the analyte sensors measure a pollutant, and positioning at least one current sensor in the sensing area. A pollution source is localized using a pollution source locator including a dispersion model and at least one hardware processor to interpolate a location of a pollution source from variations in current measured from the current sensors and measurements of pollutants from the analyte sensors.

Abstract: A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

Abstract: A computer-implemented method is provided for improving farm management by determining farm boundary delineations within a target geographic area based on crop recognition. The method includes evaluating the data using a classification algorithm to generate one or more line segments at an interface separating each pixel in at least one pair of adjacent pixels. It also includes connecting the one or more line segments to an adjacent line segment to form a boundary delineation. The method further includes generating a boundary delineation map including the boundary delineation as well as generating a farm management plan including the boundary delineation map and a recommended crop type. The farm management plan can be generated based on at least a type of vegetation present.