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Green Stormwater Infrastructure (GSI) play … Green Stormwater Infrastructure (GSI) plays a critical role in mitigating urban runoff, enhancing water quality, and promoting sustainable stormwater management. To ensure the long-term efficiency of these benefits, effective monitoring and maintenance of GSI is essential; however, current monitoring approaches are limited by costly and time-intensive in-person inspections. This study seeks to directly address these limitations through the integration of remote sensing and machine learning techniques to develop scalable, cost-effective monitoring solutions for GSI. To do so, we present a case study that utilizes high-resolution satellite (<30 cm) and drone imagery (2-4 cm) collected at GSI locations in Milwaukee, WI to extract key maintenance indicators such as vegetation health, sediment, and trash accumulation. Advanced machine learning (both supervised and unsupervised) algorithms, are employed to detect anomalies, assess performance, and automate condition assessment across GSI sites. The developed tools provide near-real-time insights for water resource managers, enabling proactive maintenance and data-driven decision making. This research demonstrates the potential of remote sensing and geospatial technologies to transform GSI monitoring practices and support resilient urban stormwater systems.</br></br>Keywords: Remote sensing, machine learning, near-real-time monitoring, green stormwater infrastructuree monitoring, green stormwater infrastructure +
Data-driven Monitoring of Green Stormwater Infrastructure using Remote Sensing and AI Techniques +
Milwaukee +
United States +
Marquette University +
Milwaukee +
abhiramsivaprasad.pamula@marquette.edu +
Abhiram Siva Prasad +
Marquette University +
Pamula +
Poster_CSDMS_36X24_Abhiramp1.pdf +
Image:Poster_CSDMS_36X24_Abhiramp1.png +
4) Get lazy with LLMs +
2) From Exploration to Publication: Geospatial Research in the Jupyter Ecosystem +
1) Landlab’s NetworkSedimentTransporter: A Lagrangian Model for River Bed Material Transport Dynamics +
Wisconsin +
United States +
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19:34:18, 1 April 2025 +
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13:18:38, 27 May 2025 +
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