Speaker
Description
Mobile sensing and deep learning approaches offer strong potential to modernize forest road inventories and support applications such as technical road classification, operational planning, and maintenance decision support. Over the past four years, we have developed and validated RoadSens, a vehicle mounted proximal sensing platform that combines stereo vision, GNSS based geo referencing, and learning based perception to extract geometric and condition related information from forest roads. Multiple case studies have demonstrated the technical feasibility of the approach and its applicability across a range of operational use cases.
While the sensing and geo referencing concepts have proven robust, this poster focuses on the main remaining bottleneck for further system improvement: the availability, diversity, and quality of training data for object detection models, particularly for road surface deterioration and damage features relevant to maintenance planning. Current models can reliably detect more uniform features, but performance degrades for irregular, heterogeneous, and context dependent damage types such as wheel ruts, erosion features, or mixed degradation patterns. These limitations are not primarily methodological, but data driven.
Our experience with RoadSens shows that model performance is highly sensitive to:
(i) variability in road construction types and materials
(ii) vegetation development and seasonal conditions,
(iii) differing definitions of “damage” across regions and user groups, and
(iv) class imbalance and annotation ambiguity in training datasets.
At the same time, the modular design of the RoadSens pipeline makes it well suited for continuous learning, provided that larger, more diverse, and better curated datasets become available.
The central message of this poster is therefore twofold: the concept has been proven, but scaling and operational robustness depend on shared data efforts. We propose using this contribution as a starting point for discussion with fellow researchers and practitioners on how to collaboratively build joint, preferably open and standardized datasets for forest road geometry and damage features. Such datasets could significantly accelerate the development of transferable models and support a wide range of applications beyond RoadSens, including maintenance prioritization, accessibility analysis, and infrastructure monitoring.
The poster explicitly invites exchange on dataset design, annotation strategies, quality control, and governance models to foster community driven data resources for forest road research and operations.
| Keywords | training-data; open-datasets; road-deterioration; maintenance |
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