Distributed MobilityDB: A Scalable Moving Object Database Management System

In recent years, the integration of deep learning techniques into satellite image analysis has revolutionized numerous industries, ranging from urban planning and environmental monitoring to disaster response and agricultural management. These advancements have been driven by the ability of deep learning models to automatically detect and classify objects within vast quantities of satellite imagery data. Object detection, in particular, plays a crucial role in identifying specific features such as buildings, vehicles, vegetation, and infrastructure, facilitating precise spatial mapping and actionable insights. This study addresses the challenge of object detection in satellite images, crucial for various applications such as urban planning, environmental monitoring, and disaster management. The proposed system investigates the effectiveness of YOLOv5 architecture in accurately detecting objects of interest within satellite imagery. The YOLO (You Only Look Once) models are selected for their ability to provide real-time detection while maintaining high accuracy, making them suitable for processing large-scale satellite datasets efficiently. The research involves training YOLOv5 model on annotated satellite image datasets, encompassing diverse object classes and environmental conditions. The performance evaluation includes metrics such as accuracy, precision, recall, and inference speed, providing insights into the capabilities and limitations of each architecture.

Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch. Transfer learning also facilitates the effective utilization of limited labeled data for fine-tuning, making it a valuable strategy for optimizing model accuracy in complex urban land classification tasks. Moreover, we strategically employ fine-tuned versions of Inception-v3 and DenseNet121 networks, emphasizing the transformative impact of these architectures. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. Through experiments conducted on the UC-Merced_LandUse dataset, we demonstrate the effectiveness of our approach, achieving remarkable results, including 92% accuracy, 93% recall, 92% precision, and a 92% F1-score. Moreover, employing heatmap analysis further elucidates the decision-making process of the models, providing insights into the classification mechanism. The successful application of CNNs in LAC, coupled with heatmap analysis, opens promising avenues for enhanced urban planning, agricultural zoning, and environmental monitoring through more accurate and automated land-area classification.

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

Spatial data management is crucial for applications like urban planning and environmental monitoring. While traditional relational databases are commonly used, they struggle with large and complex spatial data. NoSQL databases provide support for unstructured data and scalability. This article compares the performance and disk space usage of SQL Server (a relational database) and MongoDB (NoSQL database) using an open-source library. Experiments conducted with the OpenStreetMap dataset from Central America show that the MongoDB database outperformed the relational SQL Server database in most cases, offering practical advantages for spatial data management in Geographic Information System applications.

Spatiotemporal data, which captures how variables evolve across space and time, is ubiquitous in fields such as environmental science, epidemiology, and urban planning. However, identifying causal relationships in these datasets is challenging due to the presence of spatial dependencies, temporal autocorrelation, and confounding factors. This tutorial provides a comprehensive introduction to spatiotemporal causal inference, offering both theoretical foundations and practical guidance for researchers and practitioners. We explore key concepts such as causal inference frameworks, the impact of confounding in spatiotemporal settings, and the challenges posed by spatial and temporal dependencies. The paper covers synthetic spatiotemporal benchmark data generation, widely used spatiotemporal causal inference techniques, including regression-based, propensity score-based, and deep learning-based methods, and demonstrates their application using synthetic datasets. Through step-by-step examples, readers will gain a clear understanding of how to address common challenges and apply causal inference techniques to spatiotemporal data. This tutorial serves as a valuable resource for those looking to improve the rigor and reliability of their causal analyses in spatiotemporal contexts.

The aim of research focused on automated vehicle detection in satellite images using the R-CNN algorithm is to develop accurate and efficient methods for identifying and localizing vehicles in high-resolution satellite imagery. This research seeks to address the growing demand for reliable vehicle detection solutions with applications spanning urban planning, environmental monitoring, disaster response, and defense. We used the R-CNN algorithm and the CNN algorithm in this investigation, each of which has ten iterations (N=10). Two different groups evaluated these two methods, and 100 samples in all were taken into account for the analysis. A power setting of 85% was used for the Gpower statistical test (g power parameters setup with α=0.05 and power=0.85). This power configuration was chosen in accordance with accepted statistical criteria to guarantee that the investigation had a strong capacity to identify statistically significant differences or effects. In the context of automated vehicle detection in satellite photos, our research's results show that the custom CNN model outperformed expectations with an exceptional accuracy of 97.81%. As an illustration of our CNN model's significant performance advantage, the R-CNN model received an accuracy score of 94.84%. In conclusion, the research and application of automated vehicle detection in satellite images using the R-CNN (Region-based Convolutional Neural Network) algorithm represent a significant leap in the field of computer vision and remote sensing. This technology has far-reaching implications across numerous domains, from urban planning and environmental monitoring to disaster response, defense, and beyond. R-CNN and its variants have demonstrated remarkable accuracy in detecting and localizing vehicles within high-resolution satellite imagery.

Based on the network planning of urban regional environmental monitoring system, the network of urban regional environmental monitoring is established. Through data collection and data analysis of regional environmental system, the network framework of urban regional environmental system is formed and analyzed from multiple perspectives. Through comprehensive and perfect environmental monitoring analysis methods, the effectiveness of urban regional environmental system network is improved, the urban regional environmental management and environmental assessment are discussed, and the network planning and layout of environmental monitoring and environmental management system are realized. This paper analyzes the relevant environmental monitoring data from many aspects, and gives the urban regional environmental monitoring system and reasonable structure.

This chapter provides a comprehensive overview of wireless communication technologies related to IoT applications in smart cities and urban planning. In this context, it explains various wireless technologies used in IoT for smart cities and urban planning including Wi-Fi, Zigbee, LoRa, cellular networks (4G/5G), Bluetooth and other emerging standards, including the strengths and weaknesses of each protocol. It covers the fundamental theory of IoT and its architecture. The scope of the chapter covers various IoT applications in urban planning. This work emphasizes on the role of IoT in various aspects of urban planning, including transportation, energy management, environmental monitoring, public safety and infrastructure optimization. It highlights the transformative impact of wireless IoT technologies on urban development. The chapter also discusses various challenges with their advanced solutions in implementing IoT in smart cities

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forecast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the interest of many users has shifted from static views of geospatial phenomena, which capture its “spatiality” only, to more advanced means of discovering dynamic relationships among the patterns and events contained in the data as well as understanding the changes occurring in spatial data over time. Spatio-temporal datasets present several characteristics that distinguish them from other datasets. Usually, they carry distance and/or topological information, organised as multidimensional spatial and temporal indexing structures. The access to these structures is done through special methods, which generally require spatial and temporal knowledge representation, geometric and temporal computation, as well as spatial and temporal reasoning. Until recently, the research in spatial and temporal data handling has been mostly done separately. The research in the spatial domain has focussed on supporting the modelling and querying along spatial dimensions of objects/patterns in the datasets. On the other hand, the research in the temporal domain has focussed on extending the knowledge about the current state of the system governed by the temporal data. However, spatial and temporal aspects of the same data should be studied in conjunction as they are often closely related and models that integrate the two can be beneficial to many important applications. Indeed the amount of available spatio-temporal datasets is growing at exponential speed and it is becoming impossible for humans to effectively analyse and process. Suitable techniques that incorporate human expertise are required. Data mining techniques have been identified as effective in several application domains. In this chapter we discuss the application of data mining techniques to effectively analyse very large spatio-temporal datasets. Spatio-temporal data mining is an emerging field that encompasses techniques for discovering useful spatial and temporal relationships or patterns that are not explicitly stored in spatio-temporal datasets. Usually these techniques have to deal with complex objects with spatial, temporal and other attributes. Both spatial and temporal dimensions add substantial complexity to the data mining process. Following the above mentioned O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

CUPID: An efficient spatio-temporal data engine

Abstract: Geospatial intelligence (GEOINT) is transforming the way organizations analyze, visualize, and interpret spatial data to drive decision-making in critical domains such as urban planning, disaster management, defense, and environmental monitoring. This chapter explores the integration of ArcGIS and Python for advanced spatial analytics, providing a comprehensive framework for automating geospatial workflows, enhancing big data processing, and applying AI-driven modeling techniques. Key topics include spatial data management, network and topological analysis, machine learning for geospatial intelligence, and cloud-based GIS solutions. With advancements in real-time geospatial analytics, edge computing, and blockchain for geospatial security, this chapter provides cutting-edge methodologies for harnessing Python's geospatial libraries alongside ArcGIS to build scalable, automated, and AI-powered GIS solutions. The application of deep learning in satellite imagery classification, predictive spatial modeling, and geospatial intelligence automation positions this integration as a game-changer in modern geospatial decision-making. Keywords: Geospatial intelligence, ArcGIS, Python, spatial analytics, AI-driven GIS, machine learning, remote sensing, geospatial big data, network analysis, predictive modeling, geostatistical analysis, cloud GIS, edge computing, digital twins, GIS automation, real-time geospatial analytics, blockchain GIS, urban planning, disaster management, spatial optimization, geospatial cybersecurity, deep learning for GIS.

There have been a growing number of environmental problems associated with the rapid development of cities. Common environmental monitoring methods are unable to meet the dynamic needs of urban environmental management. The emergence of Internet of Things (IoT) technology provides a new way to improve urban environment monitoring and management. The Environmental Internet of Things (EIoT) makes it possible to sense, acquire, process, and transfer environmental information over a large area in real time. In this paper, we present an integrated system for urban environment monitoring and management by referring to the EIoT concept. We developed and tested the system by monitoring water, soil, air, noise, and some other environmental factors on the campus of our research institute. The system can obtain real-time environmental information in situ and express and publish its outcomes in different formats. Moreover, the system is extendible for additional sensors and environmental factors, and to cover larger areas to achieve better urban environmental monitoring and management services for the construction of sustainable cities.

The current environmental pollution and destruction have developed into a world-wide major social problem that threatens human survival and development. Environmental monitoring is the prerequisite and basis of environmental governance, but overall, the current environmental monitoring system is facing a series of problems. Based on the electrochemical sensor, this paper designs a small, low-cost, easy to layout urban environmental quality monitoring terminal, and multi-terminal constitutes a distributed network. The system has been small-scale demonstration applications and has confirmed that the system is suitable for large-scale promotion

The challenge of building consistent, available, and scalable data management systems capable of serving petabytes of data for millions of users has confronted the data management research community as well as large internet enterprises. Current proposed solutions to scalable data management, driven primarily by prevalent application requirements, limit consistent access to only the granularity of single objects, rows, or keys, thereby trading off consistency for high scalability and availability. But the growing popularity of “cloud computing”, the resulting shift of a large number of internet applications to the cloud, and the quest towards providing data management services in the cloud, has opened up the challenge for designing data management systems that provide consistency guarantees at a granularity larger than single rows and keys. In this paper, we analyze the design choices that allowed modern scalable data management systems to achieve orders of magnitude higher levels of scalability compared to traditional databases. With this understanding, we highlight some design principles for systems providing scalable and consistent data management as a service in the cloud.KeywordsCloud ComputingData Management SystemTraditional DatabaseCommodity HardwareCloud Computing InfrastructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Spatio-temporal data refers to observations indexed by both spatial locations and time points, emphasizing the importance of accounting for dependencies across these dimensions. Such data finds applications in various fields, including environmental monitoring, transportation, agriculture, public health, and urban planning. However, spatio-temporal data often suffers from incomplete observations due to various factors such as sensor failures and environmental disruptions, making accurate imputation particularly challenging. Traditional imputation methods frequently approach the problem from either a spatial or a temporal perspective. However, recent advancements have introduced numerous methods that leverage both spatial and temporal dependencies to reconstruct missing data, leading to significantly improved results. This paper provides a comprehensive review of spatio-temporal missing data imputation techniques, encompassing an extensive array of methods, from traditional statistical approaches to modern machine learning-based techniques. While other domain-specific approaches (e.g., physics-based or simulation-driven methods) exist, the majority of research in this area falls into these two categories, which are therefore the primary focus of this review. Our contributions also include a discussion of key challenges and research gaps, as well as the identification of promising directions for future research in spatio-temporal data imputation.