We study how to convert OpenStreetMap data to road networks for downstream applications. OpenStreetMap data has different formats. Extensible Markup Language (XML) is one of them. OSM data consist of nodes, ways, and relations. We process OSM XML data to extract the information of nodes and ways to obtain the map of streets of the Memphis area. We can use this map for different downstream applications.

Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty. In this paper we explore the suitability of data derived from OpenStreetMap to proxy for the location of two crucial public services: schools and health clinics. Thanks to the efforts of thousands of digital humanitarians, online mapping repositories such as OpenStreetMap contain millions of records on buildings and other structures, delineating both their location and often their use. Unfortunately much of this data is locked in complex, unstructured text rendering it seemingly unsuitable for classifying schools or clinics. We apply a scalable, unsupervised learning method to unlabeled OpenStreetMap building data to extract the location of schools and health clinics in ten countries in Africa. We find the topic modeling approach greatly improves performance versus reliance on structured keys alone. We validate our results by comparing schools and clinics identified by our OSM method versus those identified by the WHO, and describe OSM coverage gaps more broadly.

道路建设项目维护运输基础设施。这些项目的范围从短期（例如，重新铺面或固定坑洼）到长期（例如，添加肩膀或建造桥梁）。传统上，确定下一个建设项目是什么以及安排什么何时进行安排，这是通过人类使用特殊设备的检查来完成的。这种方法是昂贵且难以扩展的。另一种选择是使用计算方法来整合和分析多种过去和现在的时空数据以预测未来道路构建的位置和时间。本文报告了这种方法，该方法使用基于深神经网络的模型来预测未来的结构。我们的模型在由构造，天气，地图和道路网络数据组成的异质数据集上应用卷积和经常性组件。我们还报告了如何通过构建一个名为“美国建设”的大型数据集来解决我们如何解决足够的公开数据，其中包括620万个道路构造案例，并通过各种时空属性和路线网络功能增强，收集了。在2016年至2021年之间的连续美国（美国）中。使用对美国几个主要城市进行广泛的实验，我们显示了工作在准确预测未来建筑时的适用性 - 平均F1得分为0.85，准确性为82.2％ - 这是52.2％ - 胜过基线。此外，我们展示了我们的培训管道如何解决数据的空间稀疏性。

作为城市景观研究的重要组成部分，分析和研究街道绿色植物可以增加对城市绿化的理解，从而有助于更好的城市生活环境规划和设计。规划城市绿化的最佳道路是一种有效地最大程度地利用城市绿化的手段，这在城市居民的身心健康和游客的路径计划中起着积极作用。在本文中，我们使用Google Street View（GSV）获取大阪市的街景图像。采用语义细分模型来细分街道视图图像并分析大阪市的绿色视图指数（GVI）。基于GVI，我们利用邻接矩阵和Floyd-Warshall算法来计算绿色视图索引最佳路径，从而解决了ArcGIS软件的局限性。我们的分析不仅允许计算GVI最佳路径的特定途径，而且还实现了邻里城市绿化的可视化和整合。通过总结所有数据，我们可以对研究区域的街道绿化进行直观的感觉和客观分析。基于此，例如城市居民和游客可以最大程度地利用可用的自然资源，从而获得更好的生活。该数据集和代码可在https://github.com/jackieam/gvi-best-path上找到。

空间和地理数据的表示学习是一种快速开发的领域，其允许使用深神经网络的区域和高质量推断之间的相似性检测。然而，过去的方法集中在嵌入光栅图像（地图，街道或卫星照片），移动数据或道路网络上。在本文中，我们提出了第一种关于在微区网格中的城市功能和土地利用的开放式车间地区的传染媒介表示的第一种方法。我们确定与土地使用，建筑和城市地区功能，水，绿色或其他自然区域的主要特征相关的OSM标签的子集。通过手动验证标记质量，我们选择了36个城市用于培训区域的陈述。优步的H3索引用于将城市划分为六边形，而OSM标签为每个六角形汇总。我们提出了基于负采样的跳过克模型的Hex2VEC方法。由此产生的矢量表示展示了地图特征的语义结构，类似于基于向量的语言模型中的存在。我们还在六个波兰城市中从区域相似性检测的见解，并提出了通过附聚类获得的区域类型。

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation.

Streets networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modelled as nodes and streets as links between them. Previous work has shown that raster representations of the original data can be created through a learning algorithm on low-dimensional representations of the street networks. In contrast, models that capture high-level urban network metrics can be trained through convolutional neural networks. However, the detailed topological data is lost through the rasterisation of the street network. The models cannot recover this information from the image alone, failing to capture complex street network features. This paper proposes a model capable of inferring good representations directly from the street network. Specifically, we use a variational autoencoder with graph convolutional layers and a decoder that outputs a probabilistic fully-connected graph to learn latent representations that encode both local network structure and the spatial distribution of nodes. We train the model on thousands of street network segments and use the learnt representations to generate synthetic street configurations. Finally, we proposed a possible application to classify the urban morphology of different network segments by investigating their common characteristics in the learnt space.

叙事制图是一项学科，研究了故事和地图的交织性质。然而，叙述的传统地理化技术经常遇到几个突出的挑战，包括数据采集和一体化挑战和语义挑战。为了解决这些挑战，在本文中，我们提出了具有知识图表（KGS）的叙事制图的想法。首先，要解决数据采集和集成挑战，我们开发了一组基于KG的地理学工具箱，以允许用户从GISYstem内搜索和检索来自集成跨域知识图中的相关数据以获得来自GISYSTEM的叙述映射。在此工具的帮助下，来自KG的检索数据以GIS格式直接实现，该格式已准备好用于空间分析和映射。两种用例 - 麦哲伦的远征和第二次世界大战 - 被提出展示了这种方法的有效性。与此同时，从这种方法中确定了几个限制，例如数据不完整，语义不相容，以及地理化的语义挑战。对于后面的两个限制，我们为叙事制图提出了一个模块化本体，它将地图内容（地图内容模块）和地理化过程（制图模块）正式化。我们证明，通过代表KGS（本体）中的地图内容和地理化过程，我们可以实现数据可重用性和叙事制图的地图再现性。

我们介绍了高音，这是一个从莎士比亚戏剧中得出的不同关系数据表示的数据集。我们的表示范围从单个场景中捕获字符共发生的简单图表到编码复杂通信设置和角色贡献的超图像具有边缘特异性节点权重的超匹配。通过使多个直观表示形式容易可用于实验，我们便促进了严格的表示图，图形挖掘和网络分析中的稳健性检查，突出了特定表示的优势和缺点。利用高音释放的数据，我们证明了许多流行图挖掘问题的解决方案高度依赖表示的选择，从而使当前的图形策划实践提出了质疑。作为对我们的数据源的敬意，并断言科学也可以是艺术，我们以戏剧的形式介绍了所有观点。

ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Compared with earlier ClueWeb corpora, the ClueWeb22 corpus is larger, more varied, of higher-quality, and aligned with the document distributions in commercial web search. Besides raw HTML, ClueWeb22 includes rich information about the web pages provided by industry-standard document understanding systems, including the visual representation of pages rendered by a web browser, parsed HTML structure information from a neural network parser, and pre-processed cleaned document text to lower the barrier to entry. Many of these signals have been widely used in industry but are available to the research community for the first time at this scale.

对于流量场景的分类，可以以统一的方式描述场景的描述模型，无关，无关。本文描述了一种以语义方式描述交通场景的模型。描述模型允许独立于道路几何和道路拓扑描述交通场景。这里，流量参与者将投影到道路网络上并表示为图中的节点。根据两个交通参与者之间关于道路拓扑的相对位置，在相应节点之间创建语义分类边。为了具体化，边缘属性通过两次交通参与者之间的相对距离和速度而言，关于车道的过程。描述的一个重要方面是它可以容易地转换为机器可读格式。当前描述侧重于交通场景的动态对象，并考虑交通参与者，例如行人或车辆。

Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.

This paper presents the development of a system able to estimate the 2D relative position of nodes in a wireless network, based on distance measurements between the nodes. The system uses ultra wide band ranging technology and the Bluetooth Low Energy protocol to acquire data. Furthermore, a nonlinear least squares problem is formulated and solved numerically for estimating the relative positions of the nodes. The localization performance of the system is validated by experimental tests, demonstrating the capability of measuring the relative position of a network comprised of 4 nodes with an accuracy of the order of 3 cm and an update rate of 10 Hz. This shows the feasibility of applying the proposed system for multi-robot cooperative localization and formation control scenarios.

知识图（kgs）已被证明是构建数据的可靠方法。他们可以提供有关文化遗产收藏的丰富情境信息。但是，文化遗产库库远非完整。他们通常会缺少重要的属性，例如地理位置，尤其是对于雕塑，移动或室内实体，例如绘画。在本文中，我们首先提出了一个框架，用于从各种数据源及其连接的多跳知识中汲取有关有形文化遗产实体的知识。其次，我们提出了一个多视图学习模型，用于估计给定的文化遗产实体之间的相对距离，该模型基于实体的地理和知识联系。

追踪和处理当代时代的对象的要求逐渐增加，因为许多应用程序迅速需要精确的移动对象位置。地图匹配方法被用作预处理技术，该技术与相应道路上的移动对象点匹配。但是，大多数GPS轨迹数据集都包含静置的不规则性，这使得匹配算法不匹配轨迹与无关紧要的街道。因此，确定GPS轨迹数据集中的停留点区域会导致更好的准确匹配和更快的方法。在这项工作中，我们将停留点集中在带有DBSCAN的轨迹数据集中，并消除冗余数据，以通过降低处理时间来提高MAP匹配算法的效率。与基于模糊逻辑的地图匹配算法相比，我们认为我们提出的方法的性能和精确性。幸运的是，我们的方法可产生27.39％的数据尺寸减少和8.9％的处理时间缩短，其准确结果与以前的基于模糊的MAP匹配方法相同。

规划自行车共享站的布局是一个复杂的过程，特别是在刚刚实施自行车共享系统的城市。城市规划者通常必须根据公开可用的数据并私下提供来自管理的数据，然后使用现场流行的位置分配模型。较小城市的许多城市可能难以招聘专家进行此类规划。本文提出了一种新的解决方案来简化和促进通过使用空间嵌入方法来实现这种规划的过程。仅基于来自OpenStreetMap的公开数据，以及来自欧洲34个城市的站布局，已经开发了一种使用优步H3离散全球电网系统将城市分成微区域的方法，并指示其值得放置站的区域在不同城市使用转移学习的现有系统。工作的结果是在规划驻地布局的决策中支持规划者的机制，以选择参考城市。

Figure 1: We introduce datasets for 3D tracking and motion forecasting with rich maps for autonomous driving. Our 3D tracking dataset contains sequences of LiDAR measurements, 360 • RGB video, front-facing stereo (middle-right), and 6-dof localization. All sequences are aligned with maps containing lane center lines (magenta), driveable region (orange), and ground height. Sequences are annotated with 3D cuboid tracks (green). A wider map view is shown in the bottom-right.

视觉位置识别（VPR）不仅对于自动驾驶车辆的定位和映射至关重要，而且对于视力受损的人群的辅助导航至关重要。为了大规模启用长期VPR系统，需要解决一些挑战。首先，不同的应用程序可能需要不同的图像视图方向，例如自动驾驶汽车的前视图，而低视力人的侧视图。其次，由于行人和车辆身份信息的成像，大都市场景中的VPR通常会引起隐私问题，呼吁在VPR查询和数据库构建之前需要数据匿名化。这两个因素都可能导致VPR性能变化，而尚未得到很好的理解。 To study their influences, we present the NYU-VPR dataset that contains more than 200,000 images over a 2km by 2km area near the New York University campus, taken within the whole year of 2016. We present benchmark results on several popular VPR algorithms showing that对于当前的VPR方法，侧视观点明显更具挑战性，而数据匿名的影响几乎可以忽略不计，以及我们的假设解释和深入的分析。

There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. Simulation is a valuable and meaningful solution with training and testing functions, and it can say that simulation is a critical link in the autonomous driving world. There are also many different applications or systems of simulation from companies or academies such as SVL and Carla. These simulators flaunt that they have the closest real-world simulation, but their environment objects, such as pedestrians and other vehicles around the agent-vehicle, are already fixed programmed. They can only move along the pre-setting trajectory, or random numbers determine their movements. What is the situation when all environmental objects are also installed by Artificial Intelligence, or their behaviors are like real people or natural reactions of other drivers? This problem is a blind spot for most of the simulation applications, or these applications cannot be easy to solve this problem. The Neurorobotics Platform from the TUM team of Prof. Alois Knoll has the idea about "Engines" and "Transceiver Functions" to solve the multi-agents problem. This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal. Then based on the NRP-Core Platform, this initial development aims to construct an initial demo experiment. The consist of this report starts with the basic knowledge of NRP-Core and its installation, then focus on the explanation of the necessary components for a simulation experiment, at last, about the details of constructions for the autonomous driving system, which is integrated object detection and autonomous control.

动态图可视化吸引了研究人员的集中度，因为它代表了多个领域的实体之间的时变关系（例如，社交媒体分析，学术合作分析，团队运动分析）。集成视觉分析方法对于呈现，比较和审查动态图是结果的。即使开发了多年的动态图可视化，但是如何有效地可视化具有微妙变化的大规模和时间密集型动态图数据对研究人员仍然具有挑战性。为了为此类动态图数据提供有效的分析方法，我们提出了一种快照生成算法，该算法涉及人类中的人类，以帮助用户将动态图分为多粒性和分层快照，以进一步分析。此外，我们设计了视觉分析原型系统（DGSVI），以帮助用户有效访问动态图见解。 DGSVI集成了图形操作接口，以帮助用户在视觉上和交互式上生成快照。它配备了可视化动态图数据的层次快照的概述和详细信息。为了说明我们提出的此类动态图数据的建议方法的可用性和效率，我们在竞争中介绍了基于篮球运动员网络的两个案例研究。此外，我们进行了评估，并收到经验丰富的可视化专家的激动人心的反馈。