Building footprint extraction from digital surface models using neural networks

Ksenia Davydova; Shiyong Cui; Peter Reinartz

doi:10.1117/12.2240727

18 October 2016 Building footprint extraction from digital surface models using neural networks

Ksenia Davydova, Shiyong Cui, Peter Reinartz

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100040J (2016) https://doi.org/10.1117/12.2240727
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Two-dimensional building footprints are a basis for many applications: from cartography to three-dimensional building models generation. Although, many methodologies have been proposed for building footprint extraction, this topic remains an open research area. Neural networks are able to model the complex relationships between the multivariate input vector and the target vector. Based on these abilities we propose a methodology using neural networks and Markov Random Fields (MRF) for automatic building footprint extraction from normalized Digital Surface Model (nDSM) and satellite images within urban areas. The proposed approach has mainly two steps. In the first step, the unary terms are learned for the MRF energy function by a four-layer neural network. The neural network is learned on a large set of patches consisting of both nDSM and Normalized Difference Vegetation Index (NDVI). Then prediction is performed to calculate the unary terms that are used in the MRF. In the second step, the energy function is minimized using a maxflow algorithm, which leads to a binary building mask. The building extraction results are compared with available ground truth. The comparison illustrates the efficiency of the proposed algorithm which can extract approximately 80% of buildings from nDSM with high accuracy.

Conference Presentation

Citation Download Citation

Ksenia Davydova, Shiyong Cui, and Peter Reinartz "Building footprint extraction from digital surface models using neural networks", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040J (18 October 2016); https://doi.org/10.1117/12.2240727

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