Map Validation for Autonomous Driving Systems

High-definition mapping is fundamental for self-driving vehicles. In this thesis we describe different approaches for online map validation whose goal is to verify if reality and map data are inconsistent. A probabilistic framework to perform the sensor fusion is defined and a spatial correlation is introduced to interpolate the information. The result is a probabilistic representation of the map whose assumed values represent the probability with which the map is valid in every point

Remote sensing to characterise vegetation fuel moisture content in the UK uplands

Wildfires are an important hazard globally as they lead to significant land degradation, carbon losses and impact on human activities. Recent research has demonstrated how dynamic fire risk estimates can be informed by the use of remote sensing technology. The focus here is on improving methods for fire risk evaluation, so that prediction about where and when fires are likely to start can become more accurate. Fuel moisture content (FMC) is one of the most important factors influencing wildfire risk, as it controls the probability of ignition and the rate of spread of a fire. This work aims to assess the potential of calibrated time-series Sentinel-2A MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data to estimate and map FMC in upland areas of the UK. The work employs laboratory and field-scale measurements, and radiative transfer modelling, to test the relationships between reflectance and FMC. Calluna vulgaris samples were collected from a test site in the UK Peak District, and their FMC determined. Near-coincident multi-temporal satellite imagery was acquired for the test site and maps of FMC generated using relationships tested through the laboratory work and modelling. The results showed a strong relationship between the normalized difference water index (NDWI) and moisture stress index (MSI) with FMC, which was independent of scale. The relationship was not strongly affected by variations in soil background properties or differences in solar zenith angle. Spatial mapping of FMC across the Peak District National Park revealed temporal and spatial variations in FMC in Calluna-dominated areas. The results have implications for wildfire risk management and for upland vegetation management and conservation

Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas

Land Quality and Landscape Processes

This monograph contains a selection of scientific papers presented on the conference on Land Quality and Landscape Processes, hold in Keszthely, Hungary. It covers topics related to various aspects of land quality including : concepts of assessment; evaluation of biomass productivity ; bioindicators of land quality ; quality assessment of degraded land ; land use related data processingJRC.H.5-Land Resources Managemen

Modelling spatial and temporal urban growth

Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery

STM/STS INVESTIGATIONS OF TOPOLOGICAL SURFACE STATES ON SB FILMS

Ph.DDOCTOR OF PHILOSOPH

Geometric Accuracy Evaluation of High-Resolution Satellite Images Based on Xianning Test Field

The evaluation of geometric accuracy of high-resolution satellite images (HRSIs) has been increasingly recognized in recent years. The traditional approach is to verify each satellite individually. It is difficult to directly compare the difference in their accuracy. In order to evaluate geometric accuracy for multiple satellite images based on the same ground control benchmark, a reliable test field in Xianning (China) was utilized for geometric accuracy validation of HRSIs. Our research team has obtained multiple HRSIs in the Xianning test field, such as SPOT-6, Pleaides, ALOS, ZY-3 and TH-1. In addition, ground control points (GCPs) were acquired with GPS by field surveying, which were used to select the significant feature area on the images. We assess the orientation accuracy of the HRSIs with the single image and stereo models. Within this study, the geometrical performance of multiple HRSIs was analyzed in detail, and the results of orientation are shown and discussed. As a result, it is feasible and necessary to establish such a geometric verification field to evaluate the geometric quality of multiple HRSIs