# DashCam Video: A complementary low-cost data stream for on-demand forest-infrastructure system monitoring

arXiv: https://arxiv.org/abs/2508.11591 · HTML: https://mlsanity.com/paper/2508.11591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Geometric Median in Nearly Linear Time](https://mlsanity.com/paper/1606.05225/) (2016)
- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [From Google Maps to a Fine-Grained Catalog of Street trees](https://mlsanity.com/paper/1910.02675/) (2019)
- [Hyperparameter Optimisation with Early Termination of Poor Performers](https://mlsanity.com/paper/1907.08651/) (2019)
- [Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks](https://mlsanity.com/paper/2401.07582/) (2024)
- [Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution](https://mlsanity.com/paper/2408.01747/) (2024)

## Abstract (arXiv)

Our study introduces a novel, low-cost, and reproducible framework for real-time, object-level structural assessment and geolocation of roadside vegetation and infrastructure with commonly available but underutilized dashboard camera (dashcam) video data. We developed an end-to-end pipeline that combines monocular depth estimation, depth error correction, and geometric triangulation to generate accurate spatial and structural data from street-level video streams from vehicle-mounted dashcams. Depth maps were first estimated using a state-of-the-art monocular depth model, then refined via a gradient-boosted regression framework to correct underestimations, particularly for distant objects. The depth correction model achieved strong predictive performance (R2 = 0.92, MAE = 0.31 on transformed scale), significantly reducing bias beyond 15 m. Further, object locations were estimated using GPS-based triangulation, while object heights were calculated using pin hole camera geometry. Our method was evaluated under varying conditions of camera placement and vehicle speed. Low-speed vehicle with inside camera gave the highest accuracy, with mean geolocation error of 2.83 m, and mean absolute error (MAE) in height estimation of 2.09 m for trees and 0.88 m for poles. To the best of our knowledge, it is the first framework to combine monocular depth modeling, triangulated GPS-based geolocation, and real-time structural assessment for urban vegetation and infrastructure using consumer-grade video data. Our approach complements conventional RS methods, such as LiDAR and image by offering a fast, real-time, and cost-effective solution for object-level monitoring of vegetation risks and infrastructure exposure, making it especially valuable for utility companies, and urban planners aiming for scalable and frequent assessments in dynamic urban environments.