The field of Earth Observations (EO) offers a wealth of data from diverse
sensors, presenting a great opportunity for advancing self-supervised
multimodal learning. However, current multimodal EO datasets and models focus
on a single data type, either mono-date images or time series, which limits
their expressivity. We introduce OmniSat, a novel architecture that exploits
the spatial alignment between multiple EO modalities to learn expressive
multimodal representations without labels. To demonstrate the advantages of
combining modalities of different natures, we augment two existing datasets
with new modalities. As demonstrated on three downstream tasks: forestry, land
cover classification, and crop mapping. OmniSat can learn rich representations
in an unsupervised manner, leading to improved performance in the semi- and
fully-supervised settings, even when only one modality is available for
inference. The code and dataset are available at github.com/gastruc/OmniSat.

利用地球观测数据的多模态性质，提出了一种无监督多模态学习方法 OmniSat，可用于改进森林学、土地覆盖分类和农作物映射等任务，并在半监督和全监督设置下获得更好的性能。

OmniSat：地球观测自监督模态融合

OmniSat: Self-Supervised Modality Fusion for Earth Observation

There is an increasing number of real-world problems in computer vision and
machine learning requiring to take into consideration multiple interpretation
layers (modalities or views) of the world and learn how they relate to each
other. For example, in the case of Earth Observations from satellite data, it
is important to be able to predict one observation layer (e.g. vegetation
index) from other layers (e.g. water vapor, snow cover, temperature etc), in
order to best understand how the Earth System functions and also be able to
reliably predict information for one layer when the data is missing (e.g. due
to measurement failure or error).

计算机视觉和机器学习中存在越来越多的现实世界问题，需要考虑到世界的多个解释层次（模态或视角），并学习它们的相互关系。

多重随机掩蔽自编码器集成的鲁棒多模态半监督学习

Multiple Random Masking Autoencoder Ensembles for Robust Multimodal  Semi-supervised Learning

The framework of the seventeen sustainable development goals is a challenge
for developers and researchers applying artificial intelligence (AI). AI and
earth observations (EO) can provide reliable and disaggregated data for better
monitoring of the sustainable development goals (SDGs). In this paper, we
present an overview of SDG targets, which can be effectively measured with AI
tools. We identify indicators with the most significant contribution from the
AI and EO and describe an application of state-of-the-art machine learning
models to one of the indicators. We describe an application of U-net with SE
blocks for efficient segmentation of satellite imagery for crop detection.
Finally, we demonstrate how AI can be more effectively applied in solutions
directly contributing towards specific SDGs and propose further research on an
AI-based evaluative infrastructure for SDGs.

本文介绍了应用 AI 和地球观测数据对可持续发展目标进行监测的方法，提出了一种基于 U-net 和 SE 块的卫星图像分割模型用于作物检测，同时提出了建立评估基础设施以实现更好实现 SDGs 的未来研究方向。