International Business Machines Corporation

Globalfoundries Inc.

Levente Klein

Hendrik F Hamann

Sergio Adolfo Bermudez Rodriguez

Fernando Jimenez Marianno

Siyuan Lu

Theodore Gerard Van Kessel

Michael Alan Schappert

Alejandro Gabriel Schrott

Prabjit Singh

S Jay Chey

Conrad M Albrecht

Marcus Oliver Freitag

Dennis G Manzer

Upendra D Chitnis

Josephine B Chang

Jonathan Lenchner

Jing Zhang

Gerald James Tesauro

Youngdeok Hwang

Sergio A Bermudez

Roland Nagy

Sharathchandra UmapathiRao Pankanti

Supratik Guha

Bruce Gordon Elmegreen

Wang Zhou

Nigel Hinds

Nigel C P Hinds

Andriy Stepanchuk

Matthias Dittberner

James R Kozloski

Eli M Dow

Ramachandran Muralidhar

Sufi Zafar

Tymon Barwicz

Solomon Assefa

Ehud Aharoni

John Jeremy Rice

Yehuda Naveh

Campbell D Watson

Pablo Meyer Rojas

Ildar Khabibrakhmanov

Mukul Tewari

Wendy Chong

John Wamburu

Ronald F Ridgeway, Jr

Robin Lougee

Bryan Sacks

Carlo Siebenschuh

Mahesh Sudhakaran

Tim Schmidt

Roobina Ohanian

Jason D Renwick

Road condition prediction for potentially hazardous road segments is described. For roadways that may contain accumulated frozen precipitation, a road segment for road condition prediction is selected based on weather conditions. Various models including a solar radiation budget model, a permanent structures model, a dynamic structures model, and a road condition model are generated for the selected road segment and account for shading effects on the road segment caused by objects near the road segment. A road condition prediction for hazardous conditions on the road segment is determined based on the road condition model and provided to a driver to alert the driver of any potentially hazardous conditions.

Road icing condition prediction for shaded road segments

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-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

Aspects of the invention include includes detecting, using a first machine learning model, a first well pad at a first location based at least in part on a first set of data comprising spectral data describing a gas emission from the first location. Detecting an environmental event within a threshold distance of the well pad. Determining a probability of damage to the first well pad from the environmental event.

Terrain-based automated detection of well pads and their surroundings

Aspects of the invention include using a controller to control a transceiver to transmit a sensor query signal to a first sensor at a first location of one or more locations having one or more sensors, wherein the sensor query signal energizes a first power supply for the first sensor, wherein energizing the power supply causes the first sensor to perform a sensor reading at the first location and transmit to the transceiver an encoded response signal representing the sensor reading, and analyzing, using the controller, the encoded response signal to determine the sensor reading at the first location.

Sensor-based communications network for remote and underground locations

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 system and method provide remote tracking of progress at construction sites. When possible, the progress can be monitored remotely via satellite imagery. But once satellite imagery cannot determine the progress that needs to be monitored at the construction site, multiple unmanned aerial vehicles (UAVs) that use RF emitters and receivers are deployed to the construction site to determine progress based on RF scans. A number and type of UAVs, along with their respective flight plans, are determined from site specifications and expected progress at the construction site. The UAVs are programmed with their respective flight plans, synchronized, then deployed to inspect the construction site using RF scans. The UAVs execute their respective flight plans, with some emitting RF signals and others receiving those emitted RF signals. The UAV RF data is then analyzed and compared to the site specifications to determine the progress at the construction site.

Remote tracking of progress at construction sites

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

Embodiments of the present invention are directed to a computer-implemented method for detecting vegetation near a power line. A non-limiting example of the computer-implemented method includes detecting the spectral characteristics of a target location. The spectral characteristics can be used to detect a tree at the target location. The location of the power lines that are within a buffer distance of the tree can be detected. Given that the tree is within a buffer distance, a risk value can be calculated for the tree. The risk value can convey the likelihood that the tree will contact the power line. If the risk is above a threshold amount, a work order can be issued to direct a crew to trim or cut the tree to prevent it from contacting the power line.

Automated management of potentially hazardous objects near power lines

A rover includes a base having wheels and a propulsion system coupled to the wheels to propel the rover around a field. A tower is coupled to the base and extends over the base. Sensors are set on the tower and the base and are configured to sense environmental conditions around the rover at different elevations. A computing system includes a processor and memory. The memory is configured to receive measured data from the sensors and determine an amount and manner of water to be dispensed on plant life in the field.

Autonomous mobile platform and variable rate irrigation method for preventing frost damage

A water irrigation system is provided for irrigating a plurality of zones. The water irrigation system includes a set of acoustic pressure modulators for generating a set of modulated acoustic pressure signals that include an actuating pressure signal and a de-actuating pressure signal. The set of acoustic pressure modulators include different subsets. The different subsets control different ones of the plurality of zones by selectively providing the same or different ones of the actuating and de-actuating pressure signals to the different ones of the plurality of zones at any given time. The water irrigation system further includes a set of acoustically-reactive irrigating elements disposed in each of the plurality of zones, each including an acoustically-reactive oscillating disk based water emitter. The acoustically-reactive oscillating disk based water emitter is selectively actuated or de-actuated responsive to the actuating pressure signal and the de-actuating pressure signal, respectively.

Pressure driven irrigation system

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.

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.

Collection vessel for collecting, measuring, and aggregrating precipitation accumulation data

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

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Tuckahoe, NY, United States of America

Levente Klein