# Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification

arXiv: https://arxiv.org/abs/2505.24375 · HTML: https://mlsanity.com/paper/2505.24375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 6 of 6)

- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [PyTorchVideo: A Deep Learning Library for Video Understanding](https://mlsanity.com/paper/2111.09887/) (2021)
- [Automatic classification of trees using a UAV onboard camera and deep learning](https://mlsanity.com/paper/1804.10390/) (2018)
- [Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/2210.09290/) (2022)

## Abstract (arXiv)

This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-processing, driving, and processing - the approach employs a 3D ResNet-50 architecture implemented with PyTorchVideo. Trained on a manually annotated dataset of field recordings, the model achieves strong performance, with a validation F1 score of 0.88 and precision of 0.90. These results underscore the effectiveness of spatiotemporal convolutional networks for capturing both motion patterns and appearance in real-world forestry environments. The system integrates standard preprocessing and augmentation techniques to improve generalization, but overfitting is evident, highlighting the need for more training data and better class balance. Despite these challenges, the method demonstrates clear potential for reducing the manual workload associated with traditional time studies, offering a scalable solution for operational monitoring and efficiency analysis in forestry. This work contributes to the growing application of AI in natural resource management and sets the foundation for future systems capable of real-time activity recognition in forest machinery. Planned improvements include dataset expansion, enhanced regularization, and deployment trials on embedded systems for in-field use.