A Survey of Procedural Techniques for City Generation

The computer game industry requires a skilled workforce and this combined with the complexity of modern games, means that production costs are extremely high. One of the most time consuming aspects is the creation of game geometry, the virtual world which the players inhabit. Procedural techniques have been used within computer graphics to create natural textures, simulate special effects and generate complex natural models including trees and waterfalls. It is these procedural techniques that we intend to harness to generate geometry and textures suitable for a game situated in an urban environment. Procedural techniques can provide many benefits for computer graphics applications when the correct algorithm is used. An overview of several commonly used procedural techniques including fractals, L-systems, Perlin noise, tiling systems and cellular basis is provided. The function of each technique and the resulting output they create are discussed to better understand their characteristics, benefits and relevance to the city generation problem. City generation is the creation of an urban area which necessitates the creation of buildings, situated along streets and arranged in appropriate patterns. Some research has already taken place into recreating road network patterns and generating buildings that can vary in function and architectural style. We will study the main body of existing research into procedural city generation and provide an overview of their implementations and a critique of their functionality and results. Finally we present areas in which further research into the generation of cities is required and outline our research goals for city generation

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty. However, visual novelty detection research often only evaluates on repurposed datasets such as CIFAR-10 originally intended for object classification, where images focus on one distinct, well-centered object. New benchmarks are needed to represent the challenges of navigating the complex scenes of an open world. Our new NovelCraft dataset contains multimodal episodic data of the images and symbolic world-states seen by an agent completing a pogo stick assembly task within a modified Minecraft environment. In some episodes, we insert novel objects of varying size within the complex 3D scene that may impact gameplay. Our visual novelty detection benchmark finds that methods that rank best on popular area-under-the-curve metrics may be outperformed by simpler alternatives when controlling false positives matters most. Further multimodal novelty detection experiments suggest that methods that fuse both visual and symbolic information can improve time until detection as well as overall discrimination. Finally, our evaluation of recent generalized category discovery methods suggests that adapting to new imbalanced categories in complex scenes remains an exciting open problem.Comment: Published in Transactions on Machine Learning Research (03/2023

Non-photorealistic rendering: a critical examination and proposed system.

In the first part of the program the emergent field of Non-Photorealistic Rendering is explored from a cultural perspective. This is to establish a clear understanding of what Non-Photorealistic Rendering (NPR) ought to be in its mature form in order to provide goals and an overall infrastructure for future development. This thesis claims that unless we understand and clarify NPR's relationship with other media (photography, photorealistic computer graphics and traditional media) we will continue to manufacture "new solutions" to computer based imaging which are confused and naive in their goals. Such solutions will be rejected by the art and design community, generally condemned as novelties of little cultural worth ( i.e. they will not sell). This is achieved by critically reviewing published systems that are naively described as Non-photorealistic or "painterly" systems. Current practices and techniques are criticised in terms of their low ability to articulate meaning in images; solutions to this problem are given. A further argument claims that NPR, while being similar to traditional "natural media" techniques in certain aspects, is fundamentally different in other ways. This similarity has lead NPR to be sometimes proposed as "painting simulation" — something it can never be. Methods for avoiding this position are proposed. The similarities and differences to painting and drawing are presented and NPR's relationship to its other counterpart, Photorealistic Rendering (PR), is then delineated. It is shown that NPR is paradigmatically different to other forms of representation — i.e. it is not an "effect", but rather something basically different. The benefits of NPR in its mature form are discussed in the context of Architectural Representation and Design in general. This is done in conjunction with consultations with designers and architects. From this consultation a "wish-list" of capabilities is compiled by way of a requirements capture for a proposed system. A series of computer-based experiments resulting in the systems "Expressive Marks" and 'Magic Painter" are carried out; these practical experiments add further understanding to the problems of NPR. The exploration concludes with a prototype system "Piranesi" which is submitted as a good overall solution to the problem of NPR. In support of this written thesis are : - • The Expressive Marks system • Magic Painter system • The Piranesi system (which includes the EPixel and Sketcher systems) • A large portfolio of images generated throughout the exploration

Health monitoring of trees and investigation of tree root systems using ground penetrating radar (GPR)

Evidence suggests that trees and forests around the world are constantly being threatened by disease and environmental pressures. Over the last decade, new pathogens spread rapidly in European forests, and quarantine measures have mostly been unable to contain outbreaks. As a result, millions of trees were infected, and many of these have already died. It is therefore vital to identify infected trees in order to track, control and prevent disease spread. In addressing these challenges, the available methods often include cutting of branches and trees or incremental coring of trees. However, not only do the tree itself and its surrounding environment suffer from these methods, but they also are costly, laborious and time-consuming. In recent years the application of non-invasive testing techniques has been accepted and valued in this particular area. Given its flexibility, rapidity of data collection and cost-efficiency, Ground Penetrating Radar (GPR) has been increasingly used in this specific area of research. Consequently, this PhD Thesis aims at addressing a major challenge within the context of early identification of tree decay and tree disease control using GPR. In more detail, two main topics are addressed, namely the characterisation of the internal structure of tree trunks, and the assessment of tree root systems’ architecture. As a result, a comprehensive methodology for the assessment of both tree trunks and roots using GPR is presented, which includes the implementation of novel algorithms and GPR signal processing approaches for the characterisation of tree trunks’ internal structure and the three-dimensional mapping of tree root systems. Results of this research project were promising and will contribute towards the establishment of novel tree evaluation approaches

Natural landscape scenic preference: techniques for evaluation and simulation.

The aesthetic beauty of a landscape is a very subjective issue: every person has their own opinions and their own idea of what beauty is. However, all people have a common evolutionary history, and, according to the Biophilia hypothesis, a genetic predisposition to liking certain types of landscapes. It is possible that this common inheritance allows us to attempt to model scenic preference for natural landscapes. The ideal type of model for such predictions is the psychophysical preference model, integrating psychological responses to landscapes with objective measurements of quantitative and qualitative landscape variables. Such models commonly predict two thirds of the variance in the predications of the general public for natural landscapes. In order to create such a model three sets of data were required: landscape photographs (surrogates of the actual landscape), landscape preference data and landscape component variable measurements. The Internet was used to run a questionnaire survey; a novel, yet flexible, environmentally friendly and simple method of data gathering, resulting in one hundred and eighty responses. A geographic information system was used to digitise ninety landscape photographs and measure their landforms (based on elevation) in terms of areas and perimeters, their colours and proxies for their complexity and coherence. Landscape preference models were created by running multiple linear regressions using normalised preference data and the landscape component variables, including mathematical transformations of these variables. The eight models created predicted over sixty percent of variance in the responses and had moderate to high correlations with a second set of landscape preference data. A common base to the models were the variables of complexity, water and mountain landform, in particular the presence or absence of water and mountains was noted as being significant in determining landscape scenic preference. In order to fully establish the utility of these models, they were further tested against: changes in weather and season; the addition of cultural structures; different photographers; alternate film types; different focal lengths; and composition. Results showed that weather and season were not significant in determining landscape preference; cultural structures increased preferences for landscapes; and photographs taken by different people did not produce consistent results from the predictive models. It was also found that film type was not significant and that changes in focal length altered preferences for landscapes

Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

The Perception of Sonic Environments: Representing Soundscapes in Semi-Open Spaces

What we hear plays a crucial role in our experience of the outdoors; however, cities have long been polluted with unwanted sound levels. Semi-open spaces are most critically affected yet also provide spatial capabilities to lessen the perceived impact of noise. In response, soundscape studies view sound as a resource to be explored rather than inhibited, placing the listener's perception and awareness at the forefront of evaluating sonic environments. The research presented in this dissertation aims to understand the relationships between soundscape evaluations and design preferences for the outdoor environment, particularly in semi-open spaces. A user-interactive approach exposes the participant to visual representation methods from a reflection of the literature on the perceptual process of sound stimuli and historical modes of analysing sound. The sonic and spatial characteristics studied will be drawn from a series of soundwalks that evaluate semi-open spaces. The research is thus interested in discrepancies found in soundscape appraisals due to visual differences in the representations, including visual renders, raytracing diagrams, and heatmap animations. The results confirm the influence of visual preferences on soundscape judgments and further reveal the impact of listener sensitivities to sounds. The findings respond to suggestions that affective responses to the outdoor environment can be described dimensionally, which strongly correlate with participant design responses perceived to improve the sonic environment. Promoting user engagement and soundscapes analysis may provide new data on personal expectations and preferences in the design workflow. For this reason, perhaps designers can develop ways towards a holistic approach that can communicate the qualities of the environment to the participant and, in turn, place the end user at the centre of the workflow, delicately balancing the built environment with the overlap of daily activities

Moving Beyond Content‐Specific Computation in Artificial Neural Networks

A new wave of deep neural networks (DNNs) have performed astonishingly well on a range of real‐world tasks. A basic DNN is trained to exhibit, in parallel, a large collection of different input‐output dispositions. While this is a good model of the way humans perform some tasks automatically and without deliberative reasoning, more is needed to approach the goal of human‐like artificial intelligence. Indeed, DNN models are increasingly being supplemented to overcome the limitations inherent in dispositional‐style computation. Examining these developments, and earlier theoretical arguments, reveals a deep distinction between two fundamentally different styles of computation, defined here for the first time: content‐ specific computation and non‐content‐specific computation. Deep episodic RL networks, for example, combine content‐specific computations in a DNN with non‐content‐specific computations involving explicit memories. Human concepts are also involved in processes of both kinds. This suggests that the remarkable success of recent AI systems, and the special power of human conceptual thinking are both due, in part, to the ability to mediate between content‐specific and non‐content‐specific computations. Hybrid systems take advantage of the complementary costs and benefits of each. Combining content‐specific and non‐content‐ specific computations both has practical benefits and provides a better model of human cognitive competence