# Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/comparing-machine-and-deep-learning-methods-for-large-3d-heritage-semantic/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Francesca Matrone,Eleonora Grilli,Massimo Martini,Marina Paolanti,Roberto Pierdicca,Fabio Remondino |
| Citations | 163 |
| DOI | 10.3390/ijgi9090535 |
| Fields | Earth and Planetary Sciences,Environmental Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2220-9964/9/9/535/pdf?version=1599547084 |
| OpenAlex ID | https://openalex.org/W3083248912 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Roberto Pierdicca](https://scholariq.org/researchers/roberto-pierdicca/)
- [Marina Paolanti](https://scholariq.org/researchers/marina-paolanti/)

## Paper primary topic

- [3D Surveying and Cultural Heritage](https://scholariq.org/topics/3d-surveying-and-cultural-heritage/)

## Paper topics

- [3D Surveying and Cultural Heritage](https://scholariq.org/topics/3d-surveying-and-cultural-heritage/)
- [Remote Sensing and LiDAR Applications](https://scholariq.org/topics/remote-sensing-and-lidar-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.