We Prefer to Stay Grounded!

Forfattere

DOI:

https://doi.org/10.5617/nmi.10720

Emneord (Nøkkelord):

artificial intelligence, digital twins, machine learning, deep learning, robotics, sustainability, IoT, IoT sensors, climate change, AI4Good, Carbon Footprint, carbon neutrality, explainable AI

Sammendrag

We have proposed a three-pronged approach to identify sustainable practices during road construction: (1) Route segmentation and identification of drive phases, including loading and dumping stages; (2) Use of Digital Elevation Models (DEM) and digital terrain models(DTM) acquired by UAVs to measure progress of construction from volumetric changes; and (3) Use of vegetation indices derived from satellite imagery to calculate changes in carbon stock. The paper introduces a novel technique for identifying driving phases for heavy trucks based only on GPS data. We also delve into how these approaches can serve as the foundational elements in the creation of digital twins tailored to the construction industry.

Nedlastinger

Publisert

2024-05-22