Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

An index based road feature extraction from LANDSAT-8 OLI images

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

One  of  the  most  important  methods  to  solve  traffic  congestion  is  to detect the incident state of a roadway. This paper describes the development of a method  for  road  traffic  monitoring  aimed  at  the  acquisition  and  analysis  of remote  sensing  imagery.  We  propose  a  strategy  for  road  extraction,  vehicle detection  and incident detection  from remote sensing imagery using techniques based on neural networks, Radon transform  for angle detection and traffic-flow measurements.  Traffic-bottleneck  detection  is  another  method  that  is  proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection  method had a detection rate of 87.5%

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

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users