Multi-hazards pose significant risks to the communities and critical infrastructures. The interaction of multi-hazards results in compounding consequences that can exhaust the functions of the local governments. Unplanned urbanization, population growth, and climate change further limit the municipalities' resources and capacities. In order to manage and reduce the residual, current, and future multi-hazard risks followed by resilient development, the local governments require robust risk-informed spatial planning, considering the prevalent hazards, land use, elements-at-risk, and its associated vulnerabilities. Multi-hazard risk assessment was conducted in Godawari Municipality, of Kailali district of Nepal. The aim was to develop a methodology for analyzing the major natural hazards prevalent in the municipality, assess the vulnerability of the communities and infrastructure to the major natural hazards, determine their degree of exposure to future hazardous events, and develop risk profiles as a basis for the land use planning processes. Floods, landslides, and earthquakes were the major hazards of the municipality that were modelled. Intensive field surveys were conducted to collect historical records of disasters and their impacts on elements-at-risk, such as buildings, agricultural lands, roads, and populations. An open-source, web-based spatial decision supporting tool called RiskChanges (http://riskchanges.org/) was used to analyze the exposure, loss, and risk. Different hazard layers were overlaid with available elements-at-risk layers to obtain their exposures. Losses were calculated for each hazard type, frequency class, and exposed elements‐at‐risk combination by multiplying their vulnerabilities, and spatial probabilities. Risks were presented in terms of Average Annual Loss (AAL) for all the elements-at-risk for various return periods. The combined multi-hazard risk map indicated 26.65% of the area as a high-risk zone, 49.9% as a moderate-risk zone, and 23.42% as a low-risk zone. Interpretation of the risk assessment results is expected to assist the local government in identifying the areas suitable for future developments and allocate the resources efficiently to build back better.

Due to the rapid construction of social infrastructure in the 20th century, automation of maintenance and management of social infrastructure is an important issue in the 21st century, as it is aging simultaneously. The use of ground-penetrating radar to understand and automatically analyze information in the underground space is attracting attention as an important tool in the maintenance and management of buried pipes, but analytical methods have not yet been developed. This study proposes an automatic detection method for underground pipes using hyperbolic fitting. Since the parameters can be set freely, this method is expected to detect buried pipes with higher accuracy than conventional artificial intelligence models, which are easily affected by the quality of training data. It is also shown that the detection rate can be improved by using three-dimensional filtering and edge detection, and that it is possible to observe temporal changes in the state of buried pipes.

Deterioration of infrastructures and increase of its maintenance cost have been a worldwide issue, and Ground Penetrating Radar (GPR) observation is used for efficient maintanance. For bridge decks, detection of rebars inside bridge decks is important because they often become a trigger for damages. This research aims to reproduce 3D rebar mesh from GPR volume images based on 3D frequency filtering method. As a result, the proposed method reproduced 3D rebar mesh of a constructed test field in correct spacing. Also, it worked effectively for data of an in-service bridge.

1. Purpose: This article shows the diversity of resilience dimensions and constructs influential resilience factors for applying to urban planning measures at district level. Natural disasters have been on the top highest global risks for the last decade; and according to United Nations the frequency and intensity of such natural disasters are increasing.[1] Resilience concept has gain accelerating attention in the scientific world to promise less vulnerable communities and safer habitats. However, the multidisciplinary concept has been still relatively vague in urban studies and practice since there hasn’t been a consensus on its meaning and measures [2]. The disciplinary foundation of this paper starts from the conceptual domain of resilience definitions and ends with structuring resilience factors against natural disasters that are applicable to urban planning measures at districts scale. These urban planning measures include land use zoning, building code, morphology, spatial configuration of urban district. Some researchers have discussed three resilience types: adaptive, single equilibrium and multi-equilibrium [2]. but the applications of these typologies and characteristics have not been explained for planning resilient urban districts.

2. Outline: Reviews of resilience research epistemology shows major science fields that have shaped disaster resilience knowledge are: Planning & Development, Environmental studies, Urban studies and Engineering [3]. Therefore, the scope of researched definitions will focus on them. this article analyzes definitions to infer disaster resilience applications for urban planning and attributes them to measures at district scale. Scholars and Global institutions such as Intergovernmental Panel on Climate Change (IPCC) have defined resilience depending on their targets. According to IPCC “resilience is the ability of a social or ecological system to absorb disturbances while retaining the same basic structure and ways of functioning, the capacity for self-organization and the capacity to adapt to stress and change”.[4] such definitions need to be further explained to achieve these goals in each scale.

3. Conclusion: Investigating resilience definitions reveals diverse characteristics such as Resourcefulness, Redundancy, Robustness, Rapidity, Inclusion, Flexibility, and suggests processes to plan, absorb, respond, recover, adapt, and learn. These influential resilience attributes can be implemented by urban planning measures proposed in this paper. In urban district planning, resourcefulness and redundancy can be achieved by land use measures to consider diverse material and human resources by mixed-used functions and provision of blue-green spaces. In face of natural disasters building code measures can acquire both robustness and flexibility; morphology and special configuration of urban district absorb disaster impact and can be planned to enable rapid response and recovery process. Providing hazard maps and community-based planning are useful measures for adaptation and learning process.

References:

1- United Nations Office for Disaster Risk Reduction (2022). Global Assessment Report on Disaster Risk Reduction 2022: Our World at Risk: Transforming Governance for a Resilient Future. Geneva.

2- N. Singh, A. Sharifi in: A. Sharifi, P. Salehi(Eds.), Resilient Smart Cities, Springer, Cham, 2022, pp.67-92.

3- L. Wang, X. Xue, Y.Zhang, X.Luo, Exploring the Emerging Evolution Trends of Urban Resilience Research by Scientometric Analysis, Int. J. Environ. Res. Public Health, 15(2018), p.2181

4- IPCC, Change IC. Impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK. 2007

In the cavity survey, we already have the technology for observing the existence and an approximate shape. Detailed information is needed to know the volume and whole shape of the cavities. However, it’s impossible of only 6cm in diameter. Therefore, the small point cloud observation device has been newly developed.

Recently, the study of extending the service life of bridges has gained attention. In Japan, periodic inspections of bridges by the close visual inspection method are conducted once every five years. This bridge inspection method needs much cost. Because of the lack of engineers and budget, some local governments couldn’t complete the bridge’s aggressive preventive maintenance in Japan. To solve those problems, studies of automation have been made to reduce the cost of the inspection task which depends on human power.

Deep learning-based damage detection methods are one of the methods to reduce human power. An image processing method could detect damage from a photo image of a bridge. We focus on the state-of-the-art technology for semantic segmentation method which uses the Transformer model. This kind of method has high accuracy to detect the target from an input image. On the other hand, there is not enough discussion about the effectiveness of the size of an image for this new method. In many cases, the size of an input image is different from the assumption of the input layer of a detection model. It is necessary for preprocessing: rescale, cut out, split, and so on. Such preprocessing changes the information of an input image but there is not enough discussion about its effectiveness when using the Transformer model-based method.

In this study, we set the task of detecting the peeling and the rebar exposure on the surface image of a bridge as an evaluation. We prepared three image datasets generated from one image dataset for training detection models. Each dataset has split images generated by different split sizes. We evaluate three detection models by a suitable preprocessed input dataset for each model and compared the results.

Myanmar has rapidly been urbanized alongside transition to democratization from its military government era which came after the British colonial era in the 19th century. It is important for the government to consider disaster risk reduction strategies for the rapid urban development in big cities in the country. The authors had been working to evaluate urban vulnerabilities of Yangon City in a SATREPS project named “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar” for six years since 2014 and estimated future urban risk due to earthquakes. One of the problems for future safety of the city is rapidly increasing slum districts.

As a conclusion, this research clarified as follows: (1) slum settlements in Yangon City were distributed along the central north-south axis of the city before 1988, and they were relocated to three Townships. In recent years, new slums had been formed with the expansion of the city. (2) The sulum “555” was gradually formed after 2008 Cyclone Nargis, consisting of five communities which has own town functions. (3) In the absence of government support, foods and minimum lifelines had been supplied in residential areas and they had been able to spend independent lives in “555”. On the other hand, environmental improvement seems to be necessary in the perspective of sanitation and vulnerability. Regarding the residents’ future intentions, two third of them want to move out of the slum settlement area.

Introduction

Essential Biodiversity Variables (EBVs) have been developed during the last decades. A critical aspect of EBVs is aiming to capture biodiversity changes. So far, six classes of EBVs have been defined but are still in development, while some variables are still rather conceptual. Due to the complexity of biodiversity, traditional biodiversity monitoring programs and ecological field studies have been seen as insufficient and spatially uneven. At the same time, biodiversity change is often detected when damage is already irreversible, such as when species become locally or regionally extinct. As such, it is imperative to improve our understanding on how natural and anthropogenic drivers determine the spatial and temporal trends of biodiversity.

Researchers and decision-makers are currently constrained by the lack of data and indicators to make EBVs operational. In this work, we focus on the potential of using remote sensing and machine learning as tools to bridge these gaps and advance our capabilities to understand biodiversity processes.

Methodology

The case study area is in Sweden, with research scope of Swedish forest ecosystems. We use data of forest-cover (f.ex. Forest type, and Forest age), soil and surface (f.ex. Surface-Albedo, Leaf-Area-Index, etc), land cover, and local climate data (such as Temperature and Precipitation) as independent variables; Red-listed species observation/occurrence data as the response variable.

To understand what are the key drivers and their synergistic interactions on species distribution, we use geostatistical techniques (machine-learning or deep-learning approaches such as Geographical Random Forest) to investigate the relationships between the various indicators and occurrence rate (Red-listed species data). These data mentioned above are used as training and testing data for machine-learning. When the links are established and key drivers are clarified, we work on the analysis of anthropogenic drivers vs nature drivers and recall the gaps of current EBVs development. We build a prediction tool based on how species-distribution responds to environmental variables to predict the distribution of red-listed species related to natural and man-made driving factors.

Figure 1. Methodology framework

Conclusion

The result of the research intend to show: the ranking on importance of environmental variables regarding to species occurrence; the primary impact factors that affect species richness; the grouping of man-made drivers vs. natural drivers; the response curves of variables to species occurrence/richness; the synergic effects of individual variables as alliances; and it can be a tool box to predict red-listed species distribution.

Through earth observation on the independent variable data (environmental and land use change), we can identify and predict species distribution. This research work can be useful for further research related to remote sensing for biodiversity change detection, and can be helpful for decision-making on biodiversity protection at an early stage.

Japan is a country where vehicles drive on the left, but pedestrian traffic on roads is unclear.

Pedestrian traffic in train stations, commercial facilities, event facilities, etc. is not defined and there is no unified design philosophy.

As a result, pedestrian confusion occurs on roads and facilities, resulting in congestion and accidents due to reduced capacity.

Large-scale events attract a large number of spectators from all over the world, but the traffic methods differ from place to place and the measures to prevent pedestrian accidents are not well known, which can lead to confusion during normal times and cause accidents in the event of a sudden incident.

During large-scale events with large crowds of people, the risk of accidents of crowds is always inherent, so it is necessary to understand the principles of crowd behavior and to take soft and hard measures for crowd control based on this knowledge.

It is also important to take measures at normal times, to develop the habit of crowd behavior and to provide accurate guidance in order to accurately guide crowds during large-scale events and to evacuate people in the emergency such as earthquakes and terrorist attacks.

The brick kilns are a significant factor in Bangladesh's poor air quality and community health. As brick production increases, hazardous emissions that are harmful to the environment, society, and economy of the country also rise. The research aims to delineate the polluted areas and map spatial air pollutant distribution and brick kilns, as well as their impacts on human health. Earth Observation techniques are used to map the areas and identify the polluted areas along with secondary data from the Department of Environment (DoE) to map Brick kiln density followed by further field surveys to locate the brick kilns outside their database. The polluted areas with higher brick kiln density will be identified by overlapping the air pollution map and the brick kiln density map. Finally, a structured questionnaire survey and KII will be conducted to assess the impact of brick kiln-induced air pollution on human health. This study will help formulate and develop regulatory rules for the design, maintenance, and workplace risks associated with brick kiln industries. It will also create the prospect of government and international investors in funding for brick kiln modification.