VOICE: Visual Oracle for Interaction, Conversation, and Explanation

We present VOICE, a novel approach for connecting large language models' (LLM) conversational capabilities with interactive exploratory visualization. VOICE introduces several innovative technical contributions that drive our conversational visualization framework. Our foundation is a pack-of-bots that can perform specific tasks, such as assigning tasks, extracting instructions, and generating coherent content. We employ fine-tuning and prompt engineering techniques to tailor bots' performance to their specific roles and accurately respond to user queries, and a new prompt-based iterative scene-tree generation establishes a coupling with a structural model. Our text-to-visualization method generates a flythrough sequence matching the content explanation. Finally, 3D natural language interaction provides capabilities to navigate and manipulate the 3D models in real-time. The VOICE framework can receive arbitrary voice commands from the user and responds verbally, tightly coupled with corresponding visual representation with low latency and high accuracy. We demonstrate the effectiveness and high generalizability potential of our approach by applying it to two distinct domains: analyzing three 3D molecular models with multi-scale and multi-instance attributes, and showcasing its effectiveness on a cartographic map visualization. A free copy of this paper and all supplemental materials are available at https://osf.io/g7fbr/

The Effect of Urban Form on Urban Microclimate

Urban heat islands aggravated by accelerating climate change present significant threats to human health and economic productivity, increasing energy consumption to maintain human comfort and thus the ecological footprint of cities and their inhabitants. Most Australian capital cities have developed strategies to absorb significant population growth within existing boundaries by promoting a more compact settlement form to limit further urban sprawl, increase the efficiency of infrastructure and reduce transport related greenhouse gas emissions. In the absence of climate sensitive considerations, however, contemporary planning policy disregards the fact that higher urban densities potentially intensify urban heat islands, as evident not only in compact city centres, but also in recent suburban developments.This PhD research demonstrates how the design of neighbourhoods can influence the urban microclimate at local scale, and thus the carbon footprint and liveability of our cities. The study compared the morphology of precincts and streets in relation to air and surface temperatures, with the focus on the modifying effect of individual urban form parameters such as vegetation, street geometry, volumetric building density, and urban surface characteristics. Airborne remote high-resolution thermal and hyper-spectral imaging and Lidar were employed to examine the spatial structure of neighbourhoods and their diurnal thermal patterns across the Sydney metropolitan area. Two flights were carried out at midnight and noon on calm and clear days in August 2012 in conjunction with simultaneous in-situ measurements obtained in automobile transects.Key outcomes include the development of a methodology for automated morphological classification into Local Climate Zones and city-wide thermal mapping based on a combination of remotely sensed data sets. The comparative analysis of local surface heat island magnitudes allows the assessment of a neighbourhood’s vulnerability to urban warming within the context of different urban densities.A statistical analysis quantified individual contributions of derived urban form parameters to air and surface temperature modification at precinct and street scale, including urban canyon geometry, vegetation abundance, surface cover and albedo. The resulting predictive statistical models enable the assessment of the heat island potential of proposed urban developments and scenario modelling of urban warming mitigation strategies in existing neighbourhoods

Understanding land surface temperature differences of local climate zones based on airborne remote sensing data

The local climate zones (LCZ) scheme has attracted the interest of climate researchers as it enables the standardized study of urban heat islands by combining thermal and physical parameters of built and natural structures. Most recent work on LCZ has concentrated on understanding air temperature differences, adapting the scheme to different contexts and improving satellite-based classification methods. However, studies using very high-resolution imagery, including 3-D descriptors and analyzing their land surface temperature (LST) variability are scarcer. Since a correct delineation of LCZ implies significant temperature differences among classes, the aims of this study are 1) to test a GIS-based method for the classification of LCZ based on cut-off values valid to the Australian context; and 2) to examine the quality of classifications by analyzing the LST variability among LCZ using high-resolution airborne remote sensing data. Results show that diurnal and nocturnal mean LSTs significantly differ among most LCZs. Welch's ANOVA and subsequent post hoc tests for pairwise comparisons also demonstrate that these differences prevail for 71.8% of zones during the daytime and 73.6% of zones at night. Overall, LCZs A, 8, and 3 are the most distinguishable zones during the daytime and LCZs D, G, 1, 4, and 5 are well differentiated at night. In contrast, LCZs 1 and 4 are the least distinguishable at daytime and LCZs 10, A, and E are not well differentiated at night. The present study has successfully validated the present airborne-based classification method which is contingent on further accuracy assessment and improvements

Application of a green infrastructure typology and airborne remote sensing to classify and map urban vegetation for climate adaptation

Green infrastructure (GI) is widely accepted as a cost-effective adaptation strategy to global warming and climate change by providing multiple regulating ecosystem services. However, a more comprehensive GI typology (GIT) is necessary to identify, classify and monitor baseline vegetation by covering larger urban areas within a short time. This paper responds to this gap and proposes a local-scaled classification method using airborne remote sensing data. Two previous studies were conducted on how to categorise GI according to functional, structural and configurational attributes (Bartesaghi et al. 2016, 2015). As a result, a new standardised classification scheme and a GIT matrix were introduced. It is the aim of the present study to demonstrate their applicability through a representative pilot study in the City of Sydney. A new GIS-based workflow for the automated extraction and categorisation of GI from high resolution airborne LiDAR and hyperspectral imagery is presented. Due to the study scope and nature of data, we will only concentrate on mapping and classifying green open spaces and tree canopies. The resulting typology and workflow will enable the performance evaluation of urban greening across a variety of ecosystem services. The GIT can also be implemented by industry and local governments as a planning tool to benchmark and compare existing green cover conditions as well as to implement well-targeted planning, design and management interventions in the context of climate change adaptation and mitigation. We recommend further analysis to refine and test the derivative uses of this tool for other contexts and purposes

Mapping local climate zones for urban morphology classification based on airborne remote sensing data

There is ample evidence of the cooling effects of green infrastructure (GI) that has been extensively documented in the literature. However, the study of the thermal profiles of different GI typologies requires the classification of urban sites for a meaningful comparison of results, since specific spatial and physical characteristics produce distinct microclimates. In this paper, the Local Climate Zones (LCZ), a scheme of thermally relatively homogeneous urban structures proposed by Stewart and Oke, was used for mapping and classifying the urban morphology of a study area in Sydney, Australia. A GIS-based workflow for an automated classification based on airborne remote sensing data is presented. The datasets employed include high resolution hyperspectral imagery, LiDAR (light detection and ranging), and cadastral information. This paper also proposes a standardised and replicable workflow that can be applied by researchers and practitioners from novices to experts. The results presented here provide evidence that LCZ can be effectively derived from multiple airborne remote sensing datasets, which can then be used to identify morphological profiles to support varied climatological studies. Future stages of this research include coupling this method with a newly developed GI typology for a more comprehensive analysis of the cooling effects of GI by taking into account the morphological disparities of LCZ