International Business Machines Corporation

Siyuan Lu

Hendrik F Hamann

Levente Klein

Theodore Gerard Van Kessel

Fernando Jimenez Marianno

Conrad M Albrecht

Josephine B Chang

Marcus Oliver Freitag

Ramachandran Muralidhar

Sharathchandra UmapathiRao Pankanti

Youngdeok Hwang

Johannes Walter Schmude

Jonathan Lenchner

S Jay Chey

Gerald James Tesauro

Wang Zhou

Ildar Khabibrakhmanov

Roland Nagy

Carlo Siebenschuh

Supratik Guha

Xiaoyan Shao

Nigel Hinds

Akihisa Sakurai

Taku Izumiyama

Masao Hasegawa

Wei Zhang

Oki Gunawan

Younghun Kim

Rui Zhang

Jayant R Kalagnanam

Ulrich Alfons Finkler

Bruce Gordon Elmegreen

Kota V R M Murali

David Shing-Ki Kung

Xiaodong Cui

Aveek N Chatterjee

Shankar Km

In an approach to improve detecting and identifying objects through orbital synthetic aperture radar satellites, embodiments arrange an array of elements in a predetermined configuration, and process, by a threshold and signature analysis, detected peaks in processed image data. Further, embodiments generate a list of objects detections based on the processed peaks, and identify an object based on amplitude, polarization ratio, and polarization phase difference. Additionally, embodiments, classify the identified object based on the generated list of objects, and output, by a user interface, a list of probable object detections with position coordinates and identifications based on the classified identified objects, wherein the list of probable objects are above or within a predetermine threshold of confidence.

Computer analysis of remotely detected images for image identification

From each of a plurality of cameras, a visual input of a location is received over a network. For each visual input from the plurality of cameras, a coupling correction is performed between a shaking of the camera with respect to the visual input by subtracting velocity vectors of the plurality of cameras from velocity vectors of pixels defining the visual input to provide a processed input. It is determined whether a shaking identified in the processed input is above a predetermined threshold based on the processed input, thereby detecting one or more anomalies. From the one or more anomalies, at least one of a location, magnitude, or depth of an earthquake are inferred based on the shaking identified in the processed input of each of the plurality of cameras.

Earthquake detection and response via distributed visual input

A computer implemented method rasterizes point cloud data. A number of processor units rasterizes the point cloud data into rasterized layers based on classes in which each rasterized layer in the rasterized layers corresponds to a class in the classes. The number of processor units creates key value pairs from the rasterized layers. The number of processor units store the key value pairs in a key value store. According to other illustrative embodiments, a computer system and a computer program product for rasterizing point cloud data are provided.

Point cloud data management using key value pairs for class based rasterized layers

In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.

Distributed vector-raster fusion

A computer produces predictions throughout a raster field in response to point data, by obtaining a partially empty matrix of point data, filling a matrix of extrapolated raster data by dilating the point data in a first convolutional neural network, and generating a matrix of aggregate raster data by combining the extrapolated raster data with organic raster data in a second convolutional neural network.

Combining point observations with raster data for machine learning

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

Methods and systems for managing vegetation include training a machine learning model based on an image of a training data region before a weather event, an image of the training data region after the weather event, and information regarding the weather event. A risk score is generated for a second region using the trained machine learning model based on an image of the second region and predicted weather information for the second region. The risk score is determined to indicate high-risk vegetation in the second region. A corrective action is performed to reduce the risk of vegetation in the second region.

Predicting and correcting vegetation state

A method of generating an aggregate forecast includes obtaining historical forecasts for a number of time steps and at least one location, obtaining historical conditions for the time steps and the at least one location, training a machine learning algorithm to produce an aggregate historical forecast in response to the historical conditions and the historical forecasts, and producing an aggregate current forecast by running the trained machine learning algorithm on current forecasts. The historical forecasts and the current forecasts vary in at least one of spatial resolution or temporal resolution, and include a first forecast that is valid for a first time step and a second forecast that is valid for a second time step.

Combining forecasts of varying spatial and temporal resolution

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 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.

Crop classification and growth tracking with synthetic aperture radar

Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Solar forecasting using machine learned cloudiness classification

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.

Leveraging air/water current variability for sensor network verification and source localization

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.

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Yorktown Heights, NY, United States of America

Siyuan Lu