This PDF file contains the front matter associated with SPIE Proceedings Volume 13198, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Post-wildfire vegetation cover damage and loss can escalate the risks of the secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change is a key factor in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect.

Urban expansion has led to significant changes in urban green spaces impacting the urban environment and residents’ well-being. Therefore, monitoring changes in urban vegetation using remote sensing techniques is crucial. This study aims to address the limitations of traditional remote sensing techniques by integrating terrestrial laser scanning and UAV photogrammetry for change detection. The study concentrates on change detection within Helsinki's Malminkartano region during the leaf-off and leaf-on seasons for the year 2022. 3D point cloud data are compared using the M3C2-algorithm. The results illustrate their efficacy in detecting changes up to 2.8 meters. Moreover, the accuracy assessment of datasets revealed that 95% confidence threshold corresponded to approximately 4 cm differences in both TLS and UAV photogrammetry datasets. The study emphasizes on data processing uncertainties related to point density, registration, vertical height, and scale differences. Future research should address these uncertainties to ensure an accurate assessment of tree parameters.

A visual odometry (VO) method is proposed that uses road surface motion images for self-localization by mobile robots. Our proposed method utilizes the feature points obtained from the road surface motion images with the RGB-D camera. Experiments with video images of sub-millimeter resolution show that the VO method is capable of self-position estimation within 1% error. Results also show that the error is corrected by using our proposed algorithm and that the position and posture of the mobile robot in the two-dimensional plane can be self-estimated.

The development of science quality miniature payloads for nano satellites has facilitated the implementation of private observatories and even constellations of satellites for all sort of applications. GRASP Earth is currently developing a payload system composed of a multi-angle imaging polarimeter for the measurement of aerosol pollution and a high-resolution spectrometer for the measurement of greenhouse gases like CO2 and CH4, with commercial applications. Based on these measurements the GRASP (Generalized Retrieval of Atmosphere and Surface Properties, https://www.grasp-sas.com/) algorithm can simultaneously retrieve detailed details on the aerosol microphysics including particle size, refractive indices, particle sphericity, and the particle absorption properties, as well as the concentration of the greenhouse gases. These measurements performed from a single platform, and the joint aerosol and gases retrieval by GRASP produces higher accuracy and better sensitivity than each measurement perform independently.

The work presented here aims to understand how exposure to air pollutants for a prolonged period of time might correlate with the number of COVID-19 deaths. In our past work, we had investigated this, and found strong correlation between the two. In our current work, we divided cities based on various factors like population density, number of vehicles and so on, for points-based analysis. We used information from the Sentinel-5P satellite to determine the pollution concentration for cities. Specifically, we looked at the concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), aerosols, carbon monoxide (CO), and ozone (O3). The data regarding the number of deaths due to COVID-19 was gathered from various news reports. Our analysis further strengthened the hypothesis of a strong link between long-term exposure to air pollutants and the number of COVID-19 deaths. This correlation was even stronger for cities likely to have higher air pollution load.

Urban Heat Islands (UHI) represent a critical environmental challenge, characterised by elevated temperatures within urban areas compared to their surroundings, driven by anthropogenic heat release, material properties, and reduced vegetation. This study utilises Google Earth Engine (GEE) to unravel the intricate relationship between vegetation cover, assessed through the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI) and the dynamics of UHI in Bhubaneswar, India, over a decade. The city's unique climate and rapid urban development offer a distinctive perspective on how UHI effects evolve and interact with urban greening efforts. Utilising Landsat 8 and 9 data, the research documents the temporal and spatial shifts in UHI zones, highlighting the mitigative influence of systematically planned vegetation areas. Initial results demonstrate that Bhubaneswar's approach to urban planning, which integrates significant green spaces, contributes to a dynamic UHI landscape, where high-intensity UHI areas have notably shifted or diminished over time. Furthermore, findings suggest that modifications in bare soil areas, coupled with increased vegetation, have been crucial in moderating urban temperatures. The findings advocate for the strategic incorporation of vegetation in urban design to combat UHI effects, enhancing the livability and sustainability of cities facing similar urbanisation and climate challenges.

Urban environments are increasingly experiencing heat-related challenges due to climate change and rapid urbanization. To address these challenges, it is essential to improve our understanding of urban thermal dynamics. Digital twin technologies provide an innovative way to integrate multiple data sources and generate high-fidelity, real-time models of urban landscapes, allowing for deeper insights into urban heat distribution. In this study, we showcase three distinct workflows for generating digital twin environments for urban settings with varying degrees of complexity and visualization fidelity, focusing on thermal radiance mapping and geospatial analysis. The first workflow presents a low-level integration utilizing OpenStreetMap (OSM) building footprints to create a fundamental digital twin. Here, OSM data is leveraged to map urban geometries, providing the basic framework for thermal radiance analysis by associating building shapes and layouts with thermal data. This workflow is ideal for lightweight, accessible applications that focus on simple 2D urban heat mapping with readily available open-source tools. In the second workflow, we integrate Cesium Ion into the QGIS environment to create an enhanced 3D urban digital twin. Cesium Ion’s 3D tiling capabilities are used to visualize urban geometries in three dimensions, enabling more detailed geospatial analysis. Combined with QGIS’s robust spatial data processing, this workflow facilitates advanced urban thermal analysis, including the impact of building heights and materials on heat distribution. Finally, the third workflow demonstrates a cutting-edge approach utilizing NVIDIA Omniverse’s implementation of Open Universal Scene Description (OpenUSD) to create highly detailed and realistic 3D urban environments. This state-of-the-art framework allows for the development of photorealistic urban digital twins, capable of supporting complex simulations of thermal dynamics and interactions. With high-definition rendering and enhanced scene management, this workflow provides the most comprehensive and visually sophisticated model, supporting advanced simulations and thermal analysis in densely populated urban environments. Through these three workflows, we highlight the progression from basic 2D digital twins to photorealistic 3D environments, each offering unique advantages in terms of fidelity, scalability, and analytical power. By integrating thermal radiance mapping with these geospatial techniques, this study contributes to the ongoing evolution of urban digital twin technologies, providing a multi-faceted approach to urban heat management and planning.

This study focuses on integrating distance measurement technologies to enhance infrastructure management and development. We propose a sensor fusion system that combines a image sensor and a distance sensor aligned on the same optical axis. Using a special spectral prism, the system separates incident light into visible and near-infrared components, perfectly aligning the image (RGB) and distance (ToF) data to capture real-time phenomena. This reduces the need for complex calibration. In this study, we performed a static characterization of the depth data using targets with known characteristics. Future research will explore the combination of imaging and ranging systems for practical applications in infrastructure.

As personal mobility (PM) becomes increasingly prevalent in urban environments, the precise detection and monitoring of PM is crucial for urban aesthetics and safety. Therefore, in this study, the YOLO algorithm, renowned for its efficiency and effectiveness in object detection tasks, is employed to detect PM from UAV orthophotos. Additionally, the positional accuracy of the detected PM is evaluated using ground-truth data. Given that PM is relatively small compared to other urban objects, the feasibility of detecting and precisely locating PM was analyzed. The assumption that the centroid of the bounding boxes detected by the YOLO algorithm adequately represents the position of the detected objects was also verified. Aerial photos were collected at a 50 m altitude with an RTK UAV over three study sites. Each site contains approximately 15 PM, and their centroid coordinates were investigated through VRS GNSS surveying. For accurate geo-rectification of raw UAV images, five ground control points were installed on each site, and their coordinates were surveyed. The PM detection results showed that the positional accuracy of the PM centers had an error of 13.95 cm. Finally, it was confirmed that UAV photogrammetry and machine learning applications are effective methods for the precise detection and monitoring of PM in urban environments.

Severe air pollution has far-reaching effects on human health, which is why in 2016 the United States implemented a standard test method for determining plume opacity in the atmosphere of an outdoor environment (ASTM 7520-16). This method uses a digital camera to photograph the smoke stream and a digital image to calculate the opacity of the smoke stream. This method has a strong reliance on the subjective judgment of the inspector. In view of the shortcomings of traditional inaccurate quantification and only quantification of specific air pollutants, this paper proposes to combine the image recognition technology and deep learning analysis of remote hyperspectral cameras to adaptively determine the background of smoke, sunlight and atmospheric environmental conditions, so as to improve the accuracy of intelligent smoke concentration. An attempt was also made to establish a regression model of the opacity of smoke flow of various colors to increase the feasibility of intelligent smoke identification in the future.

Air pollution is considered the largest single environmental health risk by the World Health Organization. Despite several studies on NO2 changes in megacities, a systematic analysis in relation to settlement growth is still pending. In addition, previous studies do not refer to consistent spatial city concepts, which distorts statistics in the comparison. In this study, we examine the trends of NO2 air pollution in megacities in relation to urban settlement growth: Time series of tropospheric NO2 from GOME, SCIAMACHY, GOME-2A, and GOME-2B are evaluated regarding yearly settlement growth as derived from the World Settlement Footprint for the period from 1996 to 2015. Compared to previous studies, this work strictly uses remote sensing data and the spatial concept of Functional Urban Areas. Uncertainties due to incomparable administrative units, heterogeneously reported local data, and population counts are thus widely excluded to enable a reliable comparison of megacities across the globe. We find a wide spectrum of NO2 pollution trends and settlement growth rates. Despite this variety, the results exhibit a pronounced relation to the income group following the world’s economies classification by the World Bank.