The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc

Geospatial Analysis and Modeling of Textual Descriptions of Pre-modern Geography

Textual descriptions of pre-modern geography offer a different view of classical geography. The descriptions have been produced when none of the modern geographical concepts and tools were available. In this dissertation, we study pre-modern geography by primarily finding the existing structures of the descriptions and different cases of geographical data. We first explain four major geographical cases in pre-modern Arabic sources: gazetteer, administrative hierarchies, routes, and toponyms associated with people. Focusing on hierarchical divisions and routes, we offer approaches for manual annotation of administrative hierarchies and route sections as well as a semi-automated toponyms annotation. The latter starts with a fuzzy search of toponyms from an authority list and applies two different extrapolation models to infer true or false values, based on the context, for disambiguating the automatically annotated toponyms. Having the annotated data, we introduce mathematical models to shape and visualize regions based on the description of administrative hierarchies. Moreover, we offer models for comparing hierarchical divisions and route networks from different sources. We also suggest approaches to approximate geographical coordinates for places that do not have geographical coordinates - we call them unknown places - which is a major issue in visualization of pre-modern places on map. The final chapter of the dissertation introduces the new version of al-Ṯurayyā, a gazetteer and a spatial model of the classical Islamic world using georeferenced data of a pre-modern atlas with more than 2, 000 toponyms and routes. It offers search, path finding, and flood network functionalities as well as visualizations of regions using one of the models that we describe for regions. However the gazetteer is designed using the classical Islamic world data, the spatial model and features can be used for similarly prepared datasets.:1 Introduction 1 2 Related Work 8 2.1 GIS 8 2.2 NLP, Georeferencing, Geoparsing, Annotation 10 2.3 Gazetteer 15 2.4 Modeling 17 3 Classical Geographical Cases 20 3.1 Gazetteer 21 3.2 Routes and Travelogues 22 3.3 Administrative Hierarchy 24 3.4 Geographical Aspects of Biographical Data 25 4 Annotation and Extraction 27 4.1 Annotation 29 4.1.1 Manual Annotation of Geographical Texts 29 4.1.1.1 Administrative Hierarchy 30 4.1.1.2 Routes and Travelogues 32 4.1.2 Semi-Automatic Toponym Annotation 34 4.1.2.1 The Annotation Process 35 4.1.2.2 Extrapolation Models 37 4.1.2.2.1 Frequency of Toponymic N-grams 37 4.1.2.2.2 Co-occurrence Frequencies 38 4.1.2.2.3 A Supervised ML Approach 40 4.1.2.3 Summary 45 4.2 Data Extraction and Structures 45 4.2.1 Administrative Hierarchy 45 4.2.2 Routes and Distances 49 5 Modeling Geographical Data 51 5.1 Mathematical Models for Administrative Hierarchies 52 5.1.1 Sample Data 53 5.1.2 Quadtree 56 5.1.3 Voronoi Diagram 58 5.1.4 Voronoi Clippings 62 5.1.4.1 Convex Hull 62 5.1.4.2 Concave Hull 63 5.1.5 Convex Hulls 65 5.1.6 Concave Hulls 67 5.1.7 Route Network 69 5.1.8 Summary of Models for Administrative Hierarchy 69 5.2 Comparison Models 71 5.2.1 Hierarchical Data 71 5.2.1.1 Test Data 73 5.2.2 Route Networks 76 5.2.2.1 Post-processing 81 5.2.2.2 Applications 82 5.3 Unknown Places 84 6 Al-Ṯurayyā 89 6.1 Introducing al-Ṯurayyā 90 6.2 Gazetteer 90 6.3 Spatial Model 91 6.3.1 Provinces and Administrative Divisions 93 6.3.2 Pathfinding and Itineraries 93 6.3.3 Flood Network 96 6.3.4 Path Alignment Tool 97 6.3.5 Data Structure 99 6.3.5.1 Places 100 6.3.5.2 Routes and Distances 100 7 Conclusions and Further Work 10

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Classification and use of landform information to increase the accuracy of land condition monitoring in Western Australian pastoral rangelands

The aim of this research was to develop land unit scale data to assist land condition monitoring projects in pastoral rangelands in Western Australia. Landforms are a major components of land units and methods were explored to include landforms as a variable in land unit predictive modelling. Three land unit prediction models were tested, a Binary Weighted Overlay (BWO), a Fuzzy Weighted Overlay (FWO) and a Positive Weights of Evidence (PWofE) model

Urban scene description for a multi scale classication of high resolution imagery case of Cape Town urban Scene

Includes abstract.Includes bibliographical references.In this paper, a multi level contextual classification approach of the City of Cape Town, South Africa is presented. The methodology developed to identify the different objects using the multi level contextual technique comprised three important phases

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces