全球地球观察（EO）的运营能力不断增长为数据驱动的方法创造了新的机会，以理解和保护我们的星球。但是，由于巨大的档案尺寸和EO平台提供的有限的勘探功能，目前使用EO档案的使用受到了极大的限制。为了解决这一限制，我们最近提出了米兰，这是一种基于内容的图像检索方法，用于在卫星图像档案中快速相似性搜索。米兰是基于公制学习的深层哈希网络，将高维图像特征编码为紧凑的二进制哈希码。我们将这些代码用作哈希表中的钥匙，以实现实时邻居搜索和高度准确的检索。在此演示中，我们通过将米兰与Agoraeo内的浏览器和搜索引擎集成在一起来展示米兰的效率。地震支持卫星图像存储库上的交互式视觉探索和典型查询。演示访问者将与地震互动，扮演不同用户的角色，这些用户的角色通过其语义内容搜索图像，并通过其语义内容搜索并应用其他过滤器。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

尽管个人数据保护方面有法律进展，但未经授权实体滥用的私人数据问题仍然至关重要。为了防止这种情况，通常建议通过设计隐私作为数据保护解决方案。在本文中，使用通常用于提取敏感数据的深度学习技术研究了摄像机失真的效果。为此，我们模拟了对应于具有固定焦距，光圈和焦点的现实摄像机以及来自单色摄像机的灰度图像的现实摄像头的焦点外图像。然后，我们通过一项实验研究证明，我们可以构建一个无法提取个人信息（例如车牌编号）的隐私相机。同时，我们确保仍然可以从变形的图像中提取有用的非敏感数据。代码可在https://github.com/upciti/privacy-by-design-semseg上找到。