Award Abstract # 2152085
Collaborative Research: OAC CORE: Large-Scale Spatial Machine Learning for 3D Surface Topology in Hydrological Applications

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF FLORIDA
Initial Amendment Date: September 16, 2021
Latest Amendment Date: June 2, 2022
Award Number: 2152085
Award Instrument: Standard Grant
Program Manager: Juan Li
jjli@nsf.gov
 (703)292-2625
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2021
End Date: September 30, 2026 (Estimated)
Total Intended Award Amount: $261,161.00
Total Awarded Amount to Date: $277,161.00
Funds Obligated to Date: FY 2021 = $261,161.00
FY 2022 = $16,000.00
History of Investigator:
  • Zhe Jiang (Principal Investigator)
    zhe.jiang@ufl.edu
Recipient Sponsored Research Office: University of Florida
1523 UNION RD RM 207
GAINESVILLE
FL  US  32611-1941
(352)392-3516
Sponsor Congressional District: 03
Primary Place of Performance: University of Florida
FL  US  32611-2002
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): NNFQH1JAPEP3
Parent UEI:
NSF Program(s): OAC-Advanced Cyberinfrast Core
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9251, 075Z, 7923, 9150, 079Z
Program Element Code(s): 090Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Rapid advances in sensing technology and computer simulation have generated vast amounts of 3D surface data in various scientific domains, from high-resolution geographic terrains to electrostatic surfaces of proteins. Analyzing such emerging 3D surface big data provides scientists an opportunity to study problems that were not possible before, such as mapping detailed surface water flow and distribution for the entire continental US. Despite its vast transformative potential, machine learning tools to analyze large volumes of 3D surface data are not readily available. The project aims to fill this gap by designing a novel parallel spatial machine learning framework for 3D surface topology and implementing the system in a distributed computing environment. The system can produce high-quality observation-based flood inundation maps derived from satellite images. In collaboration with federal agencies (e.g., U.S. Geological Survey, NOAA), the project will enhance situational awareness for flood disaster response and improve flood forecasting capabilities of the NOAA National Water Model by filling in the gap of lacking observations in model calibration and validation. The proposed software tools will be open-source to enhance the research infrastructure for the broad geoscience communities. Educational activities include curriculum development, mentoring a group of high school students in data science seminars at K-12 Summer Camps, and year-long projects for selected high school students in regional Science Fair competitions.

The project will transform spatial machine learning research by enhancing terrain awareness through modeling large-scale 3D surface topology. Specifically, the project will bring about the following cyberinfrastructure innovations. First, the project will design a topography-aware spatial probabilistic model called hidden Markov contour forest, which advances existing machine learning tools by incorporating physical constraints of heterogeneous 3D terrains into zonal tree structures in the model representation. Second, the project will investigate a parallel inference framework by decomposing both intra-zone dependency and inter-zone dependency. Finally, the project will implement the proposed parallel learning framework in a distributed computing environment by addressing challenges related to task partitioning, load balancing, and dynamic task scheduling. The proposed system will be deployed for real-world rapid flood disaster response and the validation and calibration of the National Water Model through collaboration with the U.S. Geological Survey and NOAA.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Xu, Zelin and Xiao, Tingsong and He, Wenchong and Wang, Yu and Jiang, Zhe "Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery" The 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS) , 2023 Citation Details
Adhikari, Saugat and Yan, Da and Sami, Mirza Tanzim and Khalil, Jalal and Yuan, Lyuheng and Joy, Bhadhan Roy and Jiang, Zhe and Sainju, Arpan Man "An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)" SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems , 2022 https://doi.org/10.1145/3557915.3560962 Citation Details
Jiang, Zhe and Zhang, Yupu and Adhikari, Saugat and Yan, Da and Xie, Yiqun "A Hidden Markov Forest Model for Terrain-Aware Flood Inundation Mapping from Earth Imagery" SIAM International Conference on Data Mining (SDM23) , 2023 Citation Details
He, Wenchong and Sainju, Arpan Man and Jiang, Zhe and Yan, Da and Zhou, Yang "Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training" ACM Transactions on Intelligent Systems and Technology , v.13 , 2022 https://doi.org/10.1145/3481043 Citation Details
He, Wenchong and Jiang, Zhe and Kriby, Marcus and Xie, Yiqun and Jia, Xiaowei and Yan, Da and Zhou, Yang "Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery" Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022 https://doi.org/10.1145/3534678.3539410 Citation Details
Xu, Zelin and Xiao, Tingsong and He, Wenchong and Wang, Yu and Jiang, Zhe and Chen, Shigang and Xie, Yiqun and Jia, Xiaowei and Yan, Da and Zhou, Yang "Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery" Proceedings of the AAAI Conference on Artificial Intelligence , v.38 , 2024 https://doi.org/10.1609/aaai.v38i20.30253 Citation Details

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