Project Grant 2411519

Award Date 10/1/23
Completion Date 8/31/24
Dollars Obligated $24K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
High Point, NC 27268, USA
Similar Awards
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant award of $513,284, made on September 15, 2024, aims to enhance research capabilities in artificial intelligence (AI), hyperspectral imaging, and optics at the University of Puerto Rico, Mayaguez (UPRM). The funding will support the acquisition and commissioning of a state-of-the-art computer cluster equipped with 16 networked NVIDIA H100 94GB graphics processing units (GPUs) and 1.5 TB of main memory....
The National Science Foundation (NSF) awarded a $712,258 Project Grant under the Integrative Activities (CFDA 47.083) program to the University of Central Florida (UCF) to acquire an integrated LiDAR and hyperspectral sensing system. This new sensing system, consisting of LiDAR, hyperspectral imaging, and other instruments mounted on drones, robots, and ground vehicles, will enable UCF researchers to capture and integrate visible and invisible data to monitor and analyze structural and...
This $803,970 project grant from the National Science Foundation Office of Advanced Cyberinfrastructure will support the development of a scalable real-time streaming analytics and machine learning framework for geoscience and hazards research. A collaboration between the University of Colorado, University of Oregon, Rutgers University, and UNAVCO will create a data framework to enable generalized real-time streaming analytics and machine learning using over 1,500 sensors from the EarthScope and...
This Project Grant award of $344,819 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at Arizona State University (ASU) to advance machine learning methods for analyzing satellite remote sensing data. The key goals are to: (1) develop a hypermodal geospatial foundation model to accommodate diverse sensor data, (2) create a zero-shot mapping algorithm using natural language prompts, (3) build a testbed...
This $999,664 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) supports the development of a novel framework for extracting spatio-temporal features from integrated remote sensing data to create a comprehensive knowledge base for monitoring and mapping flood events. The research aims to enable rapid response and recovery efforts by providing real-time communication between human and robot agents...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This Project Grant award from the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) provides $720,000 in funding to the University of the District of Columbia (UDC) to conduct research on using machine learning and deep learning algorithms for standoff detection of threat chemicals. The project aims to advance the computational techniques for distinguishing explosives and other illicit substances from benign background materials using infrared backscatter...
This National Science Foundation (NSF) Division of Information and Intelligent Systems (CFDA #47.070) $1,034,326 Project Grant awarded to Brown University supports collaborative research to bridge the semantic-metric gap in multinocular 3D vision systems. The project aims to address key technical challenges in 3D computer vision, such as handling blurry/textureless images, leveraging redundant image data, and connecting geometric point clouds to semantic scene representations. The research...
The National Science Foundation awarded a $260,919 project grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to the University of Puerto Rico Research and Development Center Division (UPR R&D) for a collaborative research project from May 2023 to April 2026. The grant aims to develop an enhanced open networked airborne computing platform to facilitate design, implementation, and testing of airborne computing systems integrating control,...
This $600,000 Project Grant awarded by the National Science Foundation (NSF) Division of Computing and Communication Foundations will fund the development of an innovative aerial imaging system that incorporates state-of-the-art artificial intelligence (AI) for real-time data processing and analysis. The project, titled "CISE-MSI:DP:REAL-TIME AERIAL IMAGING WITH EDGE AI," is a collaboration between students and faculty at Norfolk State University (NSU), a minority-serving institution...

RUI: COLLABORATIVE RESEARCH: CDS & E: A MODULAR MULTILAYER FRAMEWORK FOR REAL-TIME HYPERSPECTRAL IMAGE SEGMENTATION -THE ANALYSIS OF IMAGES HAS BEEN USED BY THE SCIENTIFIC COMMUNITY TO SOLVE CHALLENGING PROBLEMS AND TO GET INSIGHT INTO DIVERSE NATURAL, SOCIAL, AND TECHNICAL PHENOMENA. DIFFERENT TYPES OF IMAGES HAVE BEEN EMPLOYED IN VARIOUS AREAS OF STUDY. ONE EXAMPLE IS THE HYPERSPECTRAL IMAGES, WHICH HAVE HIGHER RESOLUTION WHEN COMPARED TO CONVENTIONAL CAMERA IMAGES. ANALYZING SUCH IMAGES HAS ITS CHALLENGES. FOR INSTANCE, IT IS COMPUTATIONALLY DEMANDING, AND TRADITIONAL METHODS HAVE SOME LIMITATIONS. THIS PROJECT PROVIDES AN EFFICIENT SOLUTION TO ANALYZE SUCH IMAGES, BY EXPLOITING HIGH-PERFORMANCE COMPUTING TOOLS AND MACHINE LEARNING TECHNIQUES. THE RESULTING METHODS ARE APPLIED TO IMAGE-BASED ATMOSPHERIC CLOUD DETECTION. THE PROJECT DEVELOPS A REAL TIME, MULTI-LAYER, AND MODULAR SEGMENTATION FRAMEWORK FOR HYPERSPECTRAL IMAGES. THE DEVELOPED FRAMEWORK AUTOMATICALLY IDENTIFIES VARIOUS REGIONS WITHIN A HYPERSPECTRAL IMAGE BY CLASSIFYING EACH PIXEL OF THE IMAGE AND ASSOCIATING THEM TO CLASS SEGMENTS. THE DEVELOPED SYSTEM IS MULTI-LAYER, WHERE EACH LAYER?S RESPONSIBILITY IS TO PERFORM AN OPERATION ON ITS INPUT, GENERATE REGION CLASSIFICATION DATA, AND PASS THE RESULTANT OUTPUT TO THE NEXT LAYER. IMPORTANTLY, EACH LAYER ANALYZES ITS INPUT FROM DISTINCT VIEWPOINTS, UTILIZING SPECTRAL AND SPATIAL DATA, RESULTING IN A MULTI-LAYER FRAMEWORK WHERE THE LAYERS COMPLEMENT EACH OTHER. ALSO, THIS PROJECT AIMS TO PROVIDE AN OPTIMIZED HIGH-PERFORMANCE (SPEED-UP AND ACCURACY) COMPUTATIONAL TOOL FOR REAL-TIME HYPERSPECTRAL IMAGE ANALYSIS. THIS IS ACHIEVED BY ADAPTING THE ALGORITHMS USED IN THE DIFFERENT PARTS OF THE MODEL FOR PARALLEL PROCESSING. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

Posted 12/20/23, 12:00 AM