The diversity of building architecture styles of global cities situated on
various landforms, the degraded optical imagery affected by clouds and shadows,
and the significant inter-class imbalance of roof types pose challenges for
designing a robust and accurate building roof instance segmentor. To address
these issues, we propose an effective framework to fulfill semantic
interpretation of individual buildings with high-resolution optical satellite
imagery. Specifically, the leveraged domain adapted pretraining strategy and
composite dual-backbone greatly facilitates the discriminative feature
learning. Moreover, new data augmentation pipeline, stochastic weight averaging
(SWA) training and instance segmentation based model ensemble in testing are
utilized to acquire additional performance boost. Experiment results show that
our approach ranks in the first place of the 2023 IEEE GRSS Data Fusion Contest
(DFC) Track 1 test phase ($mAP_{50}$:50.6\%). Note-worthily, we have also
explored the potential of multimodal data fusion with both optical satellite
imagery and SAR data.

我们提出了一个有效的框架来实现对高分辨率光学卫星图像中个别建筑物的语义解释，通过采用领域自适应预训练策略和复合双支干，以及新的数据增强流程、随机权重平均训练和基于实例分割的模型集成，实现了额外的性能提升。值得注意的是，我们还探讨了光学卫星图像和 SAR 数据的多模态数据融合的潜力。

基于领域适应预训练和复合双主干的细粒度建筑屋顶实例分割

Fine-grained building roof instance segmentation based on domain adapted  pretraining and composite dual-backbone

Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF, the symmetric encoder-decoder module, which provides
both local and global context by densifying feature maps with trainable
convolutional filter banks, is utilized to extract multi-scale and high-level
spatial features. The feature maps of multiple scales are then up-sampled and
concatenated, and a novel multi-scale feature fusion module is designed to fuse
the features of different scales for the output. The two output feature maps of
the network are cloud and cloud shadow maps, which are in turn fed to binary
classifiers outside the model to obtain the final cloud and cloud shadow mask.
The MSCFF method was validated on hundreds of globally distributed optical
satellite images, with spatial resolutions ranging from 0.5 to 50 m, including
Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and
collected high-resolution images exported from Google Earth. The experimental
results show that MSCFF achieves a higher accuracy than the traditional
rule-based cloud detection methods and the state-of-the-art deep learning
models, especially in bright surface covered areas. The effectiveness of MSCFF
means that it has great promise for the practical application of cloud
detection for multiple types of medium and high-resolution remote sensing
images. Our established global high-resolution cloud detection validation
dataset has been made available online.

本文提出了一种名为多尺度卷积特征融合 (MSCFF) 的基于深度学习的云检测方法，使用对称编码器 - 解码器模块提取多尺度和高级别空间特征，然后设计了一种多尺度特征融合模块，将不同尺度的特征合并，并获得了比传统基于规则的云检测方法和最先进的深度学习模型更高的精度，尤其在覆盖明亮表面区域上。