The FLAIR #2 dataset hereby presented includes two very distinct types of
data, which are exploited for a semantic segmentation task aimed at mapping
land cover. The data fusion workflow proposes the exploitation of the fine
spatial and textural information of very high spatial resolution (VHR)
mono-temporal aerial imagery and the temporal and spectral richness of high
spatial resolution (HR) time series of Copernicus Sentinel-2 satellite images.
The French National Institute of Geographical and Forest Information (IGN), in
response to the growing availability of high-quality Earth Observation (EO)
data, is actively exploring innovative strategies to integrate these data with
heterogeneous characteristics. IGN is therefore offering this dataset to
promote innovation and improve our knowledge of our territories.

该研究介绍了 FLAIR #2 数据集，它利用高分辨率航空影像和高分辨率卫星影像的时间和光谱信息进行地物语义分割和地表覆盖制图， 以此促进创新和提高我们对领土的了解。

FLAIR #2: 多源光学图像的纹理和时间信息用于语义分割

FLAIR #2: textural and temporal information for semantic segmentation  from multi-source optical imagery

The Copernicus Sentinel-2 program now provides multispectral images at a
global scale with a high revisit rate. In this paper we explore the usage of
convolutional neural networks for urban change detection using such
multispectral images. We first present the new change detection dataset that
was used for training the proposed networks, which will be openly available to
serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is
composed of pairs of multispectral aerial images, and the changes were manually
annotated at pixel level. We then propose two architectures to detect changes,
Siamese and Early Fusion, and compare the impact of using different numbers of
spectral channels as inputs. These architectures are trained from scratch using
the provided dataset.

本文利用 Copernicus Sentinel-2 提供的全球高频多光谱图像和卷积神经网络探索了城市变化检测的应用。首先，介绍了用于训练所提出的网络的新变化检测数据集；随后，提出了两种架构来检测变化，在使用不同数量的光谱通道作为输入的情况下比较了其影响；最后，从头开始使用所提供的数据集训练这些架构。