Semantic segmentation and vision-based geolocalization in aerial images are
challenging tasks in computer vision. Due to the advent of deep convolutional
nets and the availability of relatively low cost UAVs, they are currently
generating a growing attention in the field. We propose a novel multi-task
multi-stage neural network that is able to handle the two problems at the same
time, in a single forward pass. The first stage of our network predicts
pixelwise class labels, while the second stage provides a precise location
using two branches. One branch uses a regression network, while the other is
used to predict a location map trained as a segmentation task. From a
structural point of view, our architecture uses encoder-decoder modules at each
stage, having the same encoder structure re-used. Furthermore, its size is
limited to be tractable on an embedded GPU. We achieve commercial GPS-level
localization accuracy from satellite images with spatial resolution of 1 square
meter per pixel in a city-wide area of interest. On the task of semantic
segmentation, we obtain state-of-the-art results on two challenging datasets,
the Inria Aerial Image Labeling dataset and Massachusetts Buildings.

本文提出了一种新的多任务、多阶段神经网络，能够在单次前向传递中同时处理语义分割和基于视觉的航拍图像地理定位两个问题，并在卫星图像中实现商用 GPS 级别的本地化精度以及在 Inria Aerial Image Labeling 数据集和 Massachusetts Buildings 数据集上达到领先水平的分割效果。