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SUMMARY:Scaling Up Forest Vision with Synthetic Data: Current Progress and
  Next Steps - Yihang She\, University of Cambridge
DTSTART:20250529T120000Z
DTEND:20250529T130000Z
UID:TALK232015@talks.cam.ac.uk
CONTACT:114742
DESCRIPTION:*Abstract*\n\nMachine learning rests on three pillars: algorit
 hms\, hardware\, and data. In the context of close-range forest monitoring
 \, we've already seen major advances in the first two—shifting from clas
 sical processing methods to neural networks\, and from manual tools like t
 ape measures to LiDAR-based laser scanning. These breakthroughs have enabl
 ed the development of faster and more accurate forest monitoring algorithm
 s.\n\nHowever\, data remains a bottleneck. High-quality\, annotated forest
  datasets are scarce and costly to produce\, and their size still falls sh
 ort of the scale required for robust machine learning. Meanwhile\, the ris
 e of graphics engines—and the success of synthetic data in domains like 
 self-driving and robotics—makes us wonder: can forests benefit from a si
 milar approach? The key challenge lies in whether synthetic forest environ
 ments can capture the representations needed for generalisation to real-wo
 rld data.\n\nIn this talk\, I’ll focus on the task of instance segmentat
 ion of individual trees—a core bottleneck in many field applications. I
 ’ll present my current progress in generating synthetic forest plots and
  point cloud data using Unreal Engine\, and evaluate their performance aga
 inst a state-of-the-art model trained on a leading real-world dataset. I
 ’ll also discuss upcoming directions and experimental plans. Time permit
 ting\, I’ll give a live demo of my synthetic data pipeline\, showing how
  we can go from video games to ML-ready datasets.\n\nThis is a work-in-pro
 gress talk\, and I look forward to feedback and discussion.\n\n*Bio*\n\nYi
 hang She is a second-year PhD student in Computer Science at the Universit
 y of Cambridge. His research focuses on advancing computer vision in the n
 ovel context of forest monitoring\, spanning both close-range and satellit
 e-based observations.
LOCATION:Room GS15 at the William Gates Building and on Zoom: https://cl-c
 am-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=ad
 don 
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