This repository contains the dataset for the paper: 2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs) (https://doi.org/10.3390/rs11192219).
In this study, a deep learning (DL)-based approach is proposed for the detection and reconstruction of buildings from a single aerial image. The pre-required knowledge to reconstruct the 3D shapes of buildings, including the height data as well as the linear elements of individual roofs, is derived from the RGB image using an optimized multi-scale convolutional–deconvolutional network (MSCDN). The proposed network is composed of two feature extraction levels to first predict the coarse features, and then automatically refine them. The predicted features include the normalized digital surface models (nDSMs) and linear elements of roofs in three classes of eave, ridge, and hip lines. Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs well on 3D reconstruction of buildings with different shapes and complexities. The average root mean square error (RMSE) and normalized median absolute deviation (NMAD) metrics are about 3.43 m and 1.13 m, respectively for the predicted nDSM. Moreover, the quality of the extracted linear elements is about 91.31% and 83.69% for the Potsdam and Zeebrugge test data, respectively. Unlike the state-of-the-art methods, the proposed approach does not need any additional or auxiliary data and employs a single image to reconstruct the 3D models of buildings with the competitive precision of about 1.2 m and 0.8 m for the horizontal and vertical RMSEs over the Potsdam data and about 3.9 m and 2.4 m over the Zeebrugge test data.
building reconstruction; deep learning; convolutional neural networks; building detection; depth prediction
-- It consists of cropped images, nDSMs, and roof elements including three classes of eaves (red), ridges (green) and hips (blue) (1670 training instances with the size of 224x224 and before data augmentation).
-- The format of dataset is a MATLAB structure (imdb.mat file) including three fields of images, depths, and elements.
-- Image samples of dataset (before cropping):
https://drive.google.com/file/d/1eg4d6quqKbrtj1hKkuhzV4F4EChZnqKt/view?usp=sharing
If you use this dataset in your research, please make sure to cite the paper: Fatemeh Alidoost, Hossein Arefi, Federico Tombari; “2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)”, Remote Sens. 2019, 11, 2219, https://doi.org/10.3390/rs11192219