# Roof material classification from aerial imagery

arXiv: https://arxiv.org/abs/2004.11482 · HTML: https://mlsanity.com/paper/2004.11482/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Fully convolutional Siamese neural networks for buildings damage assessment from satellite images](https://mlsanity.com/paper/2111.00508/) (2021)
- [Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean](https://mlsanity.com/paper/2307.16177/) (2023)
- [Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean](https://mlsanity.com/paper/2312.10306/) (2023)

## Abstract (arXiv)

This paper describes an algorithm for classification of roof materials using aerial photographs. Main advantages of the algorithm are proposed methods to improve prediction accuracy. Proposed methods includes: method of converting ImageNet weights of neural networks for using multi-channel images; special set of features of second level models that are used in addition to specific predictions of neural networks; special set of image augmentations that improve training accuracy. In addition, complete flow for solving this problem is proposed. The following content is available in open access: solution code, weight sets and architecture of the used neural networks. The proposed solution achieved second place in the competition "Open AI Caribbean Challenge".