143 research outputs found
Exploring Deep Learning for deformative operators in vector-based cartographic road generalization
Cartographic generalisation is the process by which geographical data is simplified and abstracted to increase the legibility of maps at reduced scales. As map scales decrease, irrelevant map features are removed (selective generalisation), and relevant map features are deformed, eliminating unnec- essary details while preserving the general shapes (deformative generalisation). The automation of cartographic generalisation has been a tough nut to crack for years because it is governed not only by explicit rules but also by a large body of implicit cartographic knowledge that conven- tional automation approaches struggle to acquire and formalise. In recent years, the introduction of Deep Learning (DL) and its inductive capabilities has raised hope for further progress. This thesis explores the potential of three Deep Learning architectures — Graph Convolutional Neural Network (GCNN), Auto Encoder, and Recurrent Neural Network (RNN) — in their application on the deformative generalisation of roads using a vector-based approach. The generated small- scale representations of the input roads differ substantially across the architectures, not only in their included frequency spectra but also in their ability to apply certain generalisation operators. However, the most apparent learnt and applied generalisation operator by all architectures is the smoothing of the large-scale roads. The outcome of this thesis has been encouraging but suggests to pursue further research about the effect of the pre-processing of the input geometries and the inclusion of spatial context and the combination of map features (e.g. buildings) to better capture the implicit knowledge engrained in the products of mapping agencies used for training the DL models
Surface Water-Groundwater Model Development for Integrated Assessment
This thesis develops an approach to the modelling of connected surface water and groundwater resources that is designed to support the needs of those who undertake integrated modelling and assessment. The research is motivated by an integrated assessment (IA) project undertaken in the Namoi Catchment in New South Wales, Australia, which examines the social, economic, and ecological consequences of water resources management decisions. To better understand the catchment-scale hydrogeological processes operating in the study area, two data-mining techniques are developed that exploit readily available hydrometric data. The first is a cluster analysis of groundwater hydrographs to improve knowledge of the connectivity between aquifers; the second looks at correlations between cumulative rainfall departure (CRD) and groundwater levels to gain insight into recharge pathways and the impacts of groundwater extractions. The results of the analysis have implications for conjunctive water management, highlighting areas of connectivity where groundwater extraction is likely to be supported by surface water recharge, and areas of disconnection where extraction risks mining the alluvial aquifer. The work also informs the design of a suitable integrated surface water-groundwater (SW-GW) modelling approach.
For SW-GW modelling to support IA, a key challenge is to minimise data requirements and computational complexity while maintaining a sufficiently good representation of system behaviour to support the needs of socio-economic and ecological analyses. A survey of existing surface water and groundwater models examines the strengths and weaknesses of various methodologies. Based on the findings of the survey, two novel SW-GW models are devised to meet the specific needs of IA projects and address gaps in existing tools. The first model, IRG-lumped, is a spatially-lumped, conceptual model that represents hydrogeological processes at the catchment scale. The second model, IRG-spatial, replaces the groundwater component of the IRG-lumped model with a spatially-distributed, physics-based approach.
The IRG-lumped and IRG-spatial models are calibrated and validated for Maules Creek Catchment, a sub-catchment of the Namoi. Uncertainties in the model parameters and outputs are examined using an ensemble of independent calibrations. Even though both models are designed to be relatively parsimonious, they still suffer from a high degree of parameter uncertainty. This is partly due to the complex hydrogeology of the Maules Creek Study Area and limitations in the available observational data. For use in IA projects, the two models have varying strengths and weaknesses. For projects where the primary aim is to simulate groundwater-dominated baseflows, the IRG-lumped model may be preferred as it runs quickly and does not need detailed information on aquifer hydrogeology. The Maules Creek application demonstrated that the model is able to simulate average changes in groundwater heads in a sloping aquifer. However, choosing an appropriate, spatially-lumped representation of a heterogeneous aquifer can be challenging, requiring extensive data analysis and the application of expert judgement. The IRG-spatial adds value if the calculation of spatially varying groundwater heads is needed, but it is more computationally intensive. Unexpectedly, it can be easier to apply than the IRG-lumped model if suitable data is available, since it deals naturally with aquifer heterogeneity. The results of this research demonstrate that, for IA projects, neither approach is unequivocally superior for modelling connected surface water and groundwater systems. The appropriate model must be chosen considering the needs of the specific project and the physical characteristics of the study area
Beyond Protected Heritage Sites: A Geospatial Study of Malaprabha River Valley
The three heritage epi-centres of the Malaprabha River Valley, in Karnataka: Aihole, Badami and Pattadakal are developed as heritage precincts of Early Chalukyan heartland. Although the Valley is a lived landscape housing heritage structures from times before and after Early Chalukyan period. Though the heritage charters (national and international) and pedagogies advocate for integrated study of heritage by including their landscape and recognise the limitation of a monument centric approach, the existing processes remain monument centric.The primary objective of the doctoral research was to identify, document and geospatially analyse the Valley’s heritage built environments for addressing questions around built heritage and their landscape by not limiting to the protected monuments. This doctoral research used water harvesting features and hydrology to explore, study, and analyse the Valley’s heritage built environments. Towards this, a list of heritage built environments (both protected and unprotected) were compiled, geotagged, and geospatially analysed. This list - from multiple sources (such as colonial records, cartographic sources, and existing online government databases) - had to be freshly prepared due to non-availability of ready reliable geospatial data. The present study visualized and analysed the Valley’s built environment as an imbricated palimpsest, by foregrounding their physical landscape which enabled the appreciation of ecological and socio-cultural significance of the many unprotected structures. The study highlighted that the structures that qualify as heritage need not carry the typical physical fabric similar to the protected monuments. Rather, they can be architecturally insignificant, mundane-looking structures having continuity into the present times through collective memories and carry socio-cultural significance. The statistical analysis indicated the importance of recognising the potentials and strengths of the settlement’s physiographic parameters (as hydrology, hydro-geomorphology, geology, and soil). Recognising the relevance of the small structures and thoughtfully made subtle terrain modifications at Badami's environs highlighted the dependency of the protected monuments on such inconspicuous landscape features. The present work emphasizes that water features (both natural and man-made) proved to be the visible or sometimes invisible link that weaves together the Valley's protected and unprotected heritage built environments
Natural Capital:Quantifying Existing Stocks and Future Potential using a Geospatial Approach
Geospatial techniques for quantifying, modelling, and mapping natural capital and ecosystem services have the potential to improve our understanding of the benefits provided by natural assets and identify changes in land use that could increase these benefits. However, questions remain around how such an approach could be implemented in practice. In this thesis, analyses are undertaken across multiple scales to explore how geospatial techniques can be applied to help solve current challenges in land management and planning. At the local scale, a land cover and benefit transfer methodology is developed and applied for the first time to value current natural capital assets within individual farms in the UK. This work highlights how the land cover product used in the methodology can have a substantial impact on valuations, with differences of up to 58% found at the five farms studied. The magnitude of these differences varies according to the landscape structure of the farm, with higher resolution land cover products incorporating larger amounts of woodland, primarily through inclusion of smaller patches, leading to overall higher valuations. At the national scale, the creation of new natural capital assets is explored by investigating proposed large-scale afforestation targets in the UK. In the initial part of the study, the feasibility of meeting these targets is investigated in the first national assessment of land available for afforestation, considering a range of physical, environmental, and policy constraints in three hypothetical planting scenarios. This found that while there is sufficient space to meet the afforestation targets in all three scenarios, this would require planting on a large proportion of unconstrained land, which could limit opportunities for spatially targeting woodland creation. The implications of this transformational change in British land cover, and policies that would be required to support this transition, are highlighted. In the second part of the study, the potential to deliver ecosystem services from afforestation is investigated. Models and spatial analysis are used to quantify the provision of carbon sequestration, recreation, and flood mitigation from potential new woodland across England, identifying targeted locations where new planting could maximise the provision of these three services. The impact of planning afforestation at different spatial scales is explored by identifying priority locations nationally and within smaller planning units such as local authorities. This shows that while spatial targeting within larger spatial units results in the greatest provision of ecosystem services, targeting even within smaller units provides substantially greater benefits than random, untargeted afforestation. Overall, the thesis develops and applies new geospatial tools for quantifying, modelling and mapping natural capital and ecosystem services. In doing so, it highlights the sensitivity of the techniques to the quality of the input data and the scale of the analysis. The outputs generate detailed insights into the distribution and potential changes in natural capital that can result from land use decisions which provides valuable evidence for directing future policy and practice
EG-ICE 2021 Workshop on Intelligent Computing in Engineering
The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
Drones and Geographical Information Technologies in Agroecology and Organic Farming
Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies.
This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition.
The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license
AutomatizovanĂ© odvozenĂ geometrie jĂzdnĂch pruhĹŻ na základÄ› leteckĂ˝ch snĂmkĹŻ a existujĂcĂch prostorovĂ˝ch 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...CĂlem tĂ©to práce je vytvoĹ™enĂ metody odvozenĂ geometrie jĂzdnĂch pruhĹŻ na základÄ› leteckĂ˝ch snĂmkĹŻ a existujĂcĂch prostorovĂ˝ch dat. NavrĹľená metoda pouĹľĂvá souÄŤasnÄ› dostupná data, ve kterĂ˝ch identifikuje vodorovnĂ© dopravnĂ znaÄŤenĂ (VDZ). Polygony, kterĂ© jsou klasifikovány jako VDZ, jsou následnÄ› zpracovány jednĂm z navrĹľenĂ˝ch algoritmĹŻ, kterĂ˝ vytvořà jejich liniovou reprezentaci (vektor), která je jednĂm z dĂlÄŤĂch vĂ˝sledkĹŻ. Tyto linie jsou dále analyzovány a na jejich základÄ› docházĂ k vytvoĹ™enĂ liniĂ symbolizujĂcĂch hranice mezi jednotlivĂ˝mi jĂzdnĂmi pruhy, kterĂ© pĹ™edstavujĂ druhĂ˝ dĂlÄŤĂ vĂ˝sledek. KromÄ› toho je snaha o automatizovanĂ© rozlišenĂ mezi plnou a pĹ™erušovanou čárou, coĹľ pĹ™inášà vÄ›tšà informaÄŤnĂ hodnotu vytvoĹ™enĂ©ho datovĂ©ho souboru. NavrhnutĂ© algoritmy byly otestovány ve 20 zájmovĂ˝ch ĂşzemĂch a vĂ˝sledky testovánĂ jsou uvedeny v tĂ©to práci. Celková správnost a stejnÄ› tak i prostorová pĹ™esnost testovanĂ˝ch dat dokazuje, Ĺľe navrhovaná metoda je efektivnĂ. V prĹŻbÄ›hu testovánĂ byly identifikovány urÄŤitĂ© nedostatky navrhovanĂ©ho procesu, kterĂ© jsou v textu blĂĹľe popsány, stejnÄ› tak je v textu navrĹľeno jejich eventuálnĂ Ĺ™ešenĂ. Práce je doprovázena vĂce neĹľ 70 obrázky, kterĂ© ilustrujĂ text a pĹ™inášejĂ jasnÄ›jšà pohled na probĂraná tĂ©mata. Práce je rozdÄ›lena na následujĂcĂ kapitoly: nejprve Ăšvod a PĹ™ehled...Department of Applied Geoinformatics and CartographyKatedra aplikovanĂ© geoinformatiky a kartografiePĹ™ĂrodovÄ›decká fakultaFaculty of Scienc
Representation Challenges
Augmented Reality (AR) and Artificial Intelligence (AI) are technological domains that closely interact with space at architectural and urban scale in the broader ambits of cultural heritage and innovative design. The growing interest is perceivable in many fields of knowledge, supported by the rapid development and advancement of theory and application, software and devices, fueling a pervasive phenomenon within our daily lives. These technologies demonstrate to be best exploited when their application and other information and communication technology (ICT) advancements achieve a continuum. In particular, AR defines an alternative path to observe, analyze and communicate space and artifacts. Besides, AI opens future scenarios in data processing, redefining the relationship between man and computer. In the last few years, the AR/AI expansion and relationship have raised deep transdisciplinary speculation. The research experiences have shown many cross-relations in Architecture and Design domains. Representation studies could arise an international debate as a convergence place of multidisciplinary theoretical and applicative contributions related to architecture, city, environment, tangible and intangible Cultural Heritage. This book collects 66 papers and identify eight lines of research that may guide future developments
- …