International Business Machines Corporation

Globalfoundries Inc.

Marcus Oliver Freitag

Hendrik F Hamann

Conrad M Albrecht

Siyuan Lu

Levente Klein

Alfred Grill

Christos Dimitrios Dimitrakopoulos

Robert L Wisnieff

Fernando Jimenez Marianno

Sharathchandra UmapathiRao Pankanti

Jack Oon Chu

Timothy James McArdle

Theodore Gerard Van Kessel

Wei Zhang

Rui Zhang

Chun-Yung Sung

Ulrich Alfons Finkler

Sergio Adolfo Bermudez Rodriguez

David Shing-Ki Kung

Xiaodong Cui

Johannes Walter Schmude

Techniques for image processing and transformation are provided. A plurality of images and a plurality of maps are received, and a system of neural networks is trained based on the plurality of images and the plurality of maps. A first image is received, and a first map is generated by processing the first image using the system of neural networks.

Deep learning to correct map and image features

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.

Method and system for crop recognition and boundary delineation

Methods and systems for generating a composite image in remote sensing applications are described. In an example, a device can receive an image having a plurality of points specified in an image space. The device can extract a portion of the image and transform points among the extracted portion from the image space to a parameter space defined by a distance parameter and an orientation parameter. The device can identify a set of intersection points in the parameter space that indicate at least one occurrence of a geometry feature in the extracted portion of the image. The device can augment the portion of the image with a plurality of new pixels based on the identified set of intersection points. The device can generate a composite image using the augmented image, where the composite image can include a plurality of augmented images corresponding to other portions of the image.

Scalable visual analytics for remote sensing applications

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.

Efficient querying using overview layers of geospatial-temporal data in a data analytics platform

A database system includes a processing sub-system having an electronic hardware controller that receives first formatted data, and an electronic file formatting sub-system having an electronic hardware controller that assigns coordinate values corresponding to a second data format to the first formatted data. The file formatting sub-system generates a dual-format data file that fuses together the first formatted data with the coordinate values corresponding to a second data format. The database system further includes a storage sub-system having a data storage unit configured to store the dual-format data file.

Scalable space-time density data fusion

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.

Method and system for crop type identification using satellite observation and weather data

Scalable feature classification for 3D point cloud data is provided. In one aspect, a method for rasterizing 3D point cloud data includes: obtaining the 3D point cloud data; generating a digital elevation model (DEM) from the 3D point cloud data; decomposing the DEM into local and global fluctuations to obtain a local DEM; generating geo-referenced shapes by automatically thresholding the local DEM; cropping and normalizing the local DEM using minimum bounding boxes derived from the geo-referenced shapes and manual annotations from subject matter experts to create a cropped DEM; and linking geo-spatially tagged labels from the subject matter experts to the cropped DEM. These data can be then directly fed into a system having an ensemble of artificial neural networks. By way of example, a scalable ecosystem is presented on the basis of the geo-spatial platform IBM PAIRS.

Scalable feature classification for laser scanning data and digital elevation models

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 &#x3c4;; and outputting the set of cluster intervals and the set of isolated points.

Event clustering and event series characterization based on expected frequency

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.

A computer-implemented method includes receiving one or more training images depicting one or more training geographical regions. One or more environmental characteristic (EC) values are determined for the training images. The EC values include at least one EC value for each of the training images. One or more models are generated for mapping an EC value of an image to a determination of whether an artifact is present in a geographical region depicted by the image, based on the EC values and based on knowledge of which of the training images depict training geographical regions having artifacts present. A new image is received depicting a new geographical region. The models are applied to the new image. A probability that a new artifact is present in the new geographical region depicted in the new image is determined, based on the applying the models to the new image.

Detecting artifacts based on digital signatures

An embodiment includes dividing a map into a first grid comprising a first plurality of cells with a first resolution and into a second grid comprising a second plurality of cells with a second resolution; determining that, because an initial portion of a key of a first cell of the second grid comprises a key of a first cell of the first grid, the first cell of the first grid comprises the first cell of the second grid; storing the first and the second grid in memories of distributed servers, wherein the first cell of the first grid and the first cell of the second grid are stored in a same one of the distributed servers or are stored in neighboring ones of the distributed servers; and directing respective processors of the distributed servers to perform a parallel search of the first grid and the second grid using the keys.

Parallel querying of adjustable resolution geospatial database

A single crystalline silicon carbide layer can be grown on a single crystalline sapphire substrate. Subsequently, a graphene layer can be formed by conversion of a surface layer of the single crystalline silicon layer during an anneal at an elevated temperature in an ultrahigh vacuum environment. Alternately, a graphene layer can be deposited on an exposed surface of the single crystalline silicon carbide layer. A graphene layer can also be formed directly on a surface of a sapphire substrate or directly on a surface of a silicon carbide substrate. Still alternately, a graphene layer can be formed on a silicon carbide layer on a semiconductor substrate. The commercial availability of sapphire substrates and semiconductor substrates with a diameter of six inches or more allows formation of a graphene layer on a commercially scalable substrate for low cost manufacturing of devices employing a graphene layer.

Formation of a graphene layer on a large substrate

A graphene layer is formed on a crystallographic surface having a non-hexagonal symmetry. The crystallographic surface can be a surface of a single crystalline semiconductor carbide layer. The non-hexagonal symmetry surface of the single crystalline semiconductor carbide layer is annealed at an elevated temperature in ultra-high vacuum environment to form the graphene layer. During the anneal, the semiconductor atoms on the non-hexagonal surface of the single crystalline semiconductor carbide layer are evaporated selective to the carbon atoms. As the semiconductor atoms are selectively removed, the carbon concentration on the surface of the semiconductor-carbon alloy layer increases. Despite the non-hexagonal symmetry of the surface of the semiconductor-carbon alloy layer, the remaining carbon atoms can coalesce to form a graphene layer having hexagonal symmetry.

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Pleasantville, NY, United States of America

Marcus Oliver Freitag