The interpretation of pole - like road furniture in mobile laser scanning data has received much attention in recent years. Most current studies interpret road furniture as a single object, which is infeasible for road furniture with multiple classes. In order to tackle this problem, we propose a framework using machine learning classifiers to interpret road furniture into detailed classes based on their functionalities such as street lights and traffic signs connected with poles (Figure 1). The overall accuracy of the interpretation in one test site is higher than 90%. A screenshot of our result is as shown in Figure 2. To conclude, our framework well interprets road furniture at a detailed level, which is of great importance for 3D precise mapping.
|Number of pages||2|
|Publication status||Published - 2018|
|Event||NCG symposium 2018 - Wageningen university, Wageningen, Netherlands|
Duration: 29 Nov 2018 → 29 Nov 2018
|Conference||NCG symposium 2018|
|Period||29/11/18 → 29/11/18|