# Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)

arXiv: https://arxiv.org/abs/1701.05818 · HTML: https://mlsanity.com/paper/1701.05818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2017.

## Built on by (most cited first, 1 of 1)

- [Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia](https://mlsanity.com/paper/2311.07537/) (2023)

## Abstract (arXiv)

In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.