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

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

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

Automatizované odvození geometrie jízdních pruhů na základě leteckých snímků a existujících prostorových dat

The aim of the thesis is to develop a method to identify driving lanes based on aerial images and existing spatial data. The proposed method uses up to date available data in which it identifies road surface marking (RSM). Polygons classified as RSM are further processed to obtain their vector line representation as the first partial result. While processing RSM vectors further, borders of driving lanes are modelled as the second partial result. Furthermore, attempts were done to be able to automatically distinguish between solid and broken lines for a higher amount of information contained in the resulting dataset. Proposed algorithms were tested in 20 case study areas and results are presented further in this thesis. The overall correctness as well as the positional accuracy proves effectivity of the method. However, several shortcomings were identified and are discussed as well as possible solutions for them are suggested. The text is accompanied by more than 70 figures to offer a clear perspective on the topic. The thesis is organised as follows: First, Introduction and Literature review are presented including the problem background, author's motivation, state of the art and contribution of the thesis. Secondly, technical and legal requirements of RSM are presented as well as theoretical concepts and...Cílem této práce je vytvoření metody odvození geometrie jízdních pruhů na základě leteckých snímků a existujících prostorových dat. Navržená metoda používá současně dostupná data, ve kterých identifikuje vodorovné dopravní značení (VDZ). Polygony, které jsou klasifikovány jako VDZ, jsou následně zpracovány jedním z navržených algoritmů, který vytvoří jejich liniovou reprezentaci (vektor), která je jedním z dílčích výsledků. Tyto linie jsou dále analyzovány a na jejich základě dochází k vytvoření linií symbolizujících hranice mezi jednotlivými jízdními pruhy, které představují druhý dílčí výsledek. Kromě toho je snaha o automatizované rozlišení mezi plnou a přerušovanou čárou, což přináší větší informační hodnotu vytvořeného datového souboru. Navrhnuté algoritmy byly otestovány ve 20 zájmových územích a výsledky testování jsou uvedeny v této práci. Celková správnost a stejně tak i prostorová přesnost testovaných dat dokazuje, že navrhovaná metoda je efektivní. V průběhu testování byly identifikovány určité nedostatky navrhovaného procesu, které jsou v textu blíže popsány, stejně tak je v textu navrženo jejich eventuální řešení. Práce je doprovázena více než 70 obrázky, které ilustrují text a přinášejí jasnější pohled na probíraná témata. Práce je rozdělena na následující kapitoly: nejprve Úvod a Přehled...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Automatizované odvození geometrie jízdních pruhů na základě leteckých snímků a existujících prostorových dat

The aim of the thesis is to develop a method to identify driving lanes based on aerial images and existing spatial data. The proposed method uses up to date available data in which it identifies road surface marking (RSM). Polygons classified as RSM are further processed to obtain their vector line representation as the first partial result. While processing RSM vectors further, borders of driving lanes are modelled as the second partial result. Furthermore, attempts were done to be able to automatically distinguish between solid and broken lines for a higher amount of information contained in the resulting dataset. Proposed algorithms were tested in 20 case study areas and results are presented further in this thesis. The overall correctness as well as the positional accuracy proves effectivity of the method. However, several shortcomings were identified and are discussed as well as possible solutions for them are suggested. The text is accompanied by more than 70 figures to offer a clear perspective on the topic. The thesis is organised as follows: First, Introduction and Literature review are presented including the problem background, author's motivation, state of the art and contribution of the thesis. Secondly, technical and legal requirements of RSM are presented as well as theoretical concepts and...Cílem této práce je vytvoření metody odvození geometrie jízdních pruhů na základě leteckých snímků a existujících prostorových dat. Navržená metoda používá současně dostupná data, ve kterých identifikuje vodorovné dopravní značení (VDZ). Polygony, které jsou klasifikovány jako VDZ, jsou následně zpracovány jedním z navržených algoritmů, který vytvoří jejich liniovou reprezentaci (vektor), která je jedním z dílčích výsledků. Tyto linie jsou dále analyzovány a na jejich základě dochází k vytvoření linií symbolizujících hranice mezi jednotlivými jízdními pruhy, které představují druhý dílčí výsledek. Kromě toho je snaha o automatizované rozlišení mezi plnou a přerušovanou čárou, což přináší větší informační hodnotu vytvořeného datového souboru. Navrhnuté algoritmy byly otestovány ve 20 zájmových územích a výsledky testování jsou uvedeny v této práci. Celková správnost a stejně tak i prostorová přesnost testovaných dat dokazuje, že navrhovaná metoda je efektivní. V průběhu testování byly identifikovány určité nedostatky navrhovaného procesu, které jsou v textu blíže popsány, stejně tak je v textu navrženo jejich eventuální řešení. Práce je doprovázena více než 70 obrázky, které ilustrují text a přinášejí jasnější pohled na probíraná témata. Práce je rozdělena na následující kapitoly: nejprve Úvod a Přehled...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc