Revealing Kunming’s (China) historical urban planning policies through Local Climate Zones

Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the role that spatial planning strategies and urbanisation regulations can play herein. This is addressed by (1) quantifying the cities' expansion and intra-urban restructuring using Local Climate Zones (LCZs) for three periods in time (2005, 2011 and 2017) based on the World Urban Database and Access Portal Tool (WUDAPT) protocol, and (2) cross-referencing observed land-use and land-cover changes with existing planning regulations. The results of the surveys on urban development show that, between 2005 and 2011, the city showed spatial expansion, whereas between 2011 and 2017, densification mainly occurred within the existing urban extent. Between 2005 and 2017, the fraction of open LCZs increased, with the largest increase taking place between 2011 and 2017. The largest decrease was seen for low the plants (LCZ D) and agricultural greenhouse (LCZ H) categories. As the potential of LCZs as, for example, a heat stress assessment tool has been shown elsewhere, understanding the relation between policy strategies and LCZ changes is important to take rational urban planning strategies toward sustainable city development

Base64 Malleability in Practice

Base64 encoding has been a popular method to encode binary data into printable ASCII characters. It is commonly used in several serialization protocols, web, and logging applications, while it is oftentimes the preferred method for human-readable database fields. However, while convenient and with a better compression rate than hex-encoding, the large number of base64 variants in related standards and proposed padding-mode optionality have been proven problematic in terms of security and cross-platform compatibility. This paper addresses a potential attack vector in the base64 decoding phase, where multiple different encodings can successfully decode into the same data, effectively breaking string uniqueness guarantees. The latter might result to log mismatches, denial of service attacks and duplicated database entries, among the others. Apart from documenting why canonicity can be broken by a malleable encoder, we also present an unexpected result, where most of today\u27s base64 decoder libraries are not 100% compatible in their default settings. Some surprising results include the non-compatible behavior of major Rust base64 crates and between popular Javascript and NodeJS base64 implementations. Finally, we propose ways and test vectors for mitigating these issues until a more permanent solution is widely adopted

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). On the other hand, unsupervised learning does not require labelled data and can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. In this work, we implement a conditional Generative Adversarial Network to reconstruct road geometries via deep inpainting procedures on a new dataset containing unlabelled road samples from challenging areas present in official cartographic support from Spain. The goal is to improve the initial road representations obtained with semantic segmentation models via generative learning. The performance of the model was evaluated on unseen data by conducting a metrical comparison where a maximum Intersection over Union (IoU) score improvement of 1.3% was observed when compared to the initial semantic segmentation result. Next, we evaluated the appropriateness of applying unsupervised generative learning using a qualitative perceptual validation to identify the strengths and weaknesses of the proposed method in very complex scenarios and gain a better intuition of the model’s behaviour when performing large-scale post-processing with generative learning and deep inpainting procedures and observed important improvements in the generated data

Application of Multi-GNSS Positioning in Landslide Surface Deformation Monitoring

With a modernization of legacy GPS and GLONASS systems, as well as with a finalization of the new European Galileo and Chinese BeiDou systems, about 120 navigation satellites for Global Navigation Satellite System (GNSS) users around the world are available presently. Usage of multi-GNSS constellations has therefore become an important research topic in recent years, including the area of landslide monitoring. The main goal of this dissertation thesis was to analyze and study positioning accuracy and performance of different satellite systems combinations with focus on finding the optimal strategy for multi-GNSS data collection and processing in landslide monitoring applications. Five stabilized monitoring points allowing repetitive GNSS observation campaigns were established at the selected Recica landslide in the Czech Republic. Quality of current multi-GNSS precise products provided by different analysis centers (ACs) was evaluated to allow a selection of the optimal one. Although no substantial differences were found, products provided by GeoForschungsZentrum (GFZ) and Center for Orbit Determination in Europe (CODE) can be recommended in overall. Consequently, positioning accuracy provided by various constellation combinations was analyzed by using data from well-established GNSS reference stations while simulating observation conditions of the Recica landslide. The best results were obtained when processing signals from a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems, with a static relative differential technique and observation periods for data collection exceeding eight hours. Finally, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements. A horizontal displacement with an annual velocity of about 3 cm in the horizontal direction was found for three monitoring points while the other two points were more stable.With a modernization of legacy GPS and GLONASS systems, as well as with a finalization of the new European Galileo and Chinese BeiDou systems, about 120 navigation satellites for Global Navigation Satellite System (GNSS) users around the world are available presently. Usage of multi-GNSS constellations has therefore become an important research topic in recent years, including the area of landslide monitoring. The main goal of this dissertation thesis was to analyze and study positioning accuracy and performance of different satellite systems combinations with focus on finding the optimal strategy for multi-GNSS data collection and processing in landslide monitoring applications. Five stabilized monitoring points allowing repetitive GNSS observation campaigns were established at the selected Recica landslide in the Czech Republic. Quality of current multi-GNSS precise products provided by different analysis centers (ACs) was evaluated to allow a selection of the optimal one. Although no substantial differences were found, products provided by GeoForschungsZentrum (GFZ) and Center for Orbit Determination in Europe (CODE) can be recommended in overall. Consequently, positioning accuracy provided by various constellation combinations was analyzed by using data from well-established GNSS reference stations while simulating observation conditions of the Recica landslide. The best results were obtained when processing signals from a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems, with a static relative differential technique and observation periods for data collection exceeding eight hours. Finally, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements. A horizontal displacement with an annual velocity of about 3 cm in the horizontal direction was found for three monitoring points while the other two points were more stable.548 - Katedra geoinformatikyvyhově

La aplicación de la teledetecciónenel desarrollo sostenible urbano

El problema de la congestión del tráfico tiene un impacto negativo en el desarrollo sostenible y la habitabilidad de las ciudades, por lo tanto, abordar el problema de la congestión del tráfico es crucial para el futuro desarrollo de la ciudad. Este estudio tiene como objetivo explorar la aplicación innovadora de la teledetección y los sistemas de información geográfica en el campo del desarrollo sostenible de las ciudades, con el fin de proporcionar una comprensión más profunda del sistema de transporte urbano. Mediante el uso de la capacidad de análisis integral de la teledetección y los sistemas de información geográfica, este estudio se centra en la recopilación de datos abiertos del gobierno de Barcelona y realiza un análisis exhaustivo utilizando la tecnología QGIS. A través de este proceso de análisis, podemos evaluar a fondo las causas fundamentales de la congestión del tráfico y los accidentes de tráfico, y determinar con precisión la interrelación entre los diferentes factores de influencia. Basándonos en estos resultados de evaluación, proporcionamos medidas viables para abordar los problemas de accidentes y congestión del tráfico en Barcelona, sentando así las bases para el desarrollo sostenible del sistema de transporte urbano. Los logros importantes de este estudio también incluyen la identificación de direcciones de investigación prometedoras para el futuro

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.</jats:p

Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

Sustainable development can be attained at a microlevel and having smart campuses around the world presents an opportunity to achieve city-wide smartness. In the process of attaining smartness on campuses, the elements requiring attention must be investigated. There are many publications on smart campuses, and this investigation used the bibliometric analysis method to identify such publications produced over the last decade. A matrix of 578 nodes and 3217 edges was developed from 285 publications on smart campus construction and procurement. Fifteen cluster themes were produced from the bibliometric analysis. The findings revealed that China contributed 48.4% of all published articles on the smart campus. The findings presented a framework from the cluster themes under the four broad infrastructure areas of building construction or repurposing, technology and IT network, continuous improvement, and smart learning and teaching management. The implications of the findings identified that IT project management, traditional procurement strategy, and standard forms of contracts such as the New Engineering Contract (NEC) and the Joint Contract Tribunal (JCT) are applicable in the procurement of smart cities