Photogrammetry and Neural Networks to Detect Form Changing Slope - PowerPoint PPT Presentation

Feb 05, 2023 •14 likes •164 views

Photogrammetry and Neural Networks to Detect Form Changing Slope Conditions Christoph Mertz Carnegie Mellon University Application: Landslide detection 2018: Record year of landslides in our region Record rainfall : wettest year Soil:

Photogrammetry and Neural Networks to Detect Form Changing Slope Conditions Christoph Mertz Carnegie Mellon University
Application: Landslide detection 2018: Record year of landslides in our region • Record rainfall : wettest year • Soil: red clay • Many hills • Not enough $$$ Route 30 Greenleaf St. / West End
What is Deep Learning? Example: Find the function that marks each pixel with the probability that it is “road” =F( ) ~10 million parameters ~1 million elements ~1 million elements Advantage: Only need to show it enough examples! Disadvantage: Need to show it >10,000, sometimes millions of examples
State of the Art computer vision / machine learning Object classification and localization
State of the Art computer vision / machine learning Panoptic segmentation
State of the Art computer vision / machine learning Keypoint detection
Indicator events in images Debris on road Cracks: longitudinal, then curving Persistently wet =>reduced friction Leaking pipe => Earth movement might cause leak.
3D reconstruction from images (Photogrammetry) From 80 images:
Indicator events in 3D Tree Retaining wall: bulges, tilting, bowing, undermining Rail guard
Current focus: development of cracks Example: Spring Run Road November 11, 2018 May 20, 2019 March 12, 2019
3D model of Spring Run Road landslide
Cross section top view side view (cross section)
Work with Civil Engineering: Modeling of failing slope Ansys model showing the sliding of assumed failure surface using CZM
Get lots of data with Transit bus Applications: Detect relevant changes and events Send only relevant information, given bandwidth, time, and privacy constraints Monitor and assess infrastructure and traffic Bus with cameras, GPS, storage, communication and computing Damage detection – e.g. landslides Traffic management center Traffic counts – parked and moving cars Update HD maps

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