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This content will become publicly available on March 25, 2025

Title: Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery

Flood mapping on Earth imagery is crucial for disaster management, but its efficacy is hampered by the lack of high-quality training labels. Given high-resolution Earth imagery with coarse and noisy training labels, a base deep neural network model, and a spatial knowledge base with label constraints, our problem is to infer the true high-resolution labels while training neural network parameters. Traditional methods are largely based on specific physical properties and thus fall short of capturing the rich domain constraints expressed by symbolic logic. Neural-symbolic models can capture rich domain knowledge, but existing methods do not address the unique spatial challenges inherent in flood mapping on high-resolution imagery. To fill this gap, we propose a spatial-logic-aware weakly supervised learning framework. Our framework integrates symbolic spatial logic inference into probabilistic learning in a weakly supervised setting. To reduce the time costs of logic inference on vast high-resolution pixels, we propose a multi-resolution spatial reasoning algorithm to infer true labels while training neural network parameters. Evaluations of real-world flood datasets show that our model outperforms several baselines in prediction accuracy. The code is available at https://github.com/spatialdatasciencegroup/SLWSL.

 
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Award ID(s):
2414185 2207072 2008973 2152085
NSF-PAR ID:
10521511
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
AAAI Press
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
38
Issue:
20
ISSN:
2159-5399
Page Range / eLocation ID:
22457 to 22465
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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