Jung-Hong Hong

1. Whether caused by nature or by humans, disasters have a significant impact on human life and well-being. These impacts can manifest physically through injuries or fatalities, as well as material...

Smart cities represent a new perspective on modern urban development. They involve an information infrastructure environment with application intelligence to improve operational efficiency and welfare effectively. However, understanding how to overcome the barriers of data fragmentation and heterogeneity to exploit the strengths of existing resources and create integration effects remains a key challenge in smart city development. This research focuses on the effective management of heterogeneous sensor systems across different domains to improve quick disaster responses. Metadata serve as the core of this proposed framework, which is designed to not only describe the common and unique characteristics of various IoT-based devices and services, but also to provide necessary information to support the searching, requesting, and updating of required sensors and observation, as well as responding to the upcoming disaster. A workflow consisting of four list types was proposed and used to guide the response procedure. This research specifically aims to enable heterogeneous sensor systems available to all public or private stakeholders to be integrated in a collaborative fashion. While a flooding response was chosen for demonstration in this research, the proposed standard-based framework can be further promoted for other types of smart city applications, not limited to disaster response. The study’s results and implications underscore the importance of effective management of heterogeneous sensor systems and the role of metadata in enabling disaster responses in smart cities.

One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data.

This paper proposes to identify the variation of accessibility to healthcare facilities based on vulnerability assessments of floods by using open source data. The open source data comprises Open Street Map (OSM), world population, and statistical data. The accessibility analysis is more focused on vulnerable populations that might be affected by floods. Therefore, a vulnerability assessment is conducted beforehand to identify the location where the vulnerable population is located. A before and after scenario of floods is applied to evaluate the changes of healthcare accessibility. A GIS Network Analyst is chosen as the accessibility analysis tool. The results indicate that the most vulnerable population lives in the Asemrowo district. The service area analysis showed that 94% of the West of Surabaya was well-serviced in the before scenario. Otherwise, the decrement of service area occurs at the city center in the after scenario. Thus, the disaster manager can understand which vulnerable area is to be more prioritized in the evacuation process.

The installation of closed-circuit television monitors (CCTV) has rapidly increased in number ever since the 11 September attacks. With the advantages of direct visual inspection, CCTV systems are widely used on various occasions that require instantaneous and long-term monitoring. Especially for emergency response tasks, the prompt availability of CCTV offers EOC (Emergency Operation Center) commanders much better action reference about the reported incidents. However, the heterogeneity among the CCTV systems impedes the effective and efficient use and sharing of CCTV services hosted by different stakeholders, making individual CCTV systems often operate on their own and restrict the possibility of taking the best advantages of the huge number of existing CCTV systems. This research proposes a metadata-driven approach to facilitate a cross-domain sharing mechanism for heterogeneous CCTV systems. The CCTV metadata includes a set of enriched description information based on the analysis from the aspects of Who, When, Where, What, Why and How (5W1H) for CCTV. Sharing mechanisms based on standardised CCTV metadata can then suffice the need for querying and selecting CCTV across heterogeneous systems according to the task at hand. One distinguished design is the modelling of the field of view (FOV) of CCTV from the 3D perspective. By integrating with the 3D feature-based city model data, the 3D FOV information not only provides better visualisation about the spatial coverage of the CCTV systems but also enables the 3D visibility analysis of CCTV based on individual features, such that the selection decision can be further improved with the indexing of CCTV and features. As the number and variety of CCTV systems continuously grows, the proposed mechanism has a great potential to serve as a solid collaborated foundation for integrating heterogeneous CCTV systems for applications that demand comprehensive and instantaneous understanding about the dynamically changing world, e.g., smart cities, disaster management, criminal investigation, etc.

The installation of closed-circuit television monitors (CCTV) has been rapidly increasing ever since the September 11 attacks and has become one of the most widely used types of sensors for the tasks that require instantaneous and long-term monitoring. With the distinguished characteristics of direct visual inspection of the subject of interests, the availability of CCTVs offers the EOC commanders a quick way to validate and access the reported disaster incidents during emergency response. However, the heterogeneity of CCTV systems and the lack of appropriate descriptions precludes the optimized use of CCTV and causes immense difficulties in effectively coordinating or appraising the use of CCTV systems. By arguing standardized metadata plays the most crucial role in quickly and precisely finding the needed CCTV during emergency response, regardless of its specifications, suppliers, or locations, we propose an integrated operational framework based on the CCTV metadata specifically designed for emergency response in this paper. This framework standardizes the metadata to unify the descriptions for heterogeneous CCTV systems and fulfill the requirements for searching and selecting CCTV. Instead of the 2D point-based location or sector-based CCTV FOV coverage, we also propose to extend the FOV from 2D to 3D to precisely describe the area and even the features that can be monitored by the CCTV. This not only improves the precision and efficiency of selecting CCTV but also increase the commander’s ability to make quick and accurate responses to disasters. The advantages of integrating heterogeneous CCTV systems can tremendously improve the possibility of monitoring and updating the real-time status caused by hazards.

Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator.

In recent years, the demands of 3D cyber-city have been steadily growing. With strong links to the citizens’ lives, building information is considered as the most important component in the 3D urban model. To further facilitate the best usage of 3D data, the development of 3D SDI requires creative thinking to meet different application needs. While many current applications are restricted to visualization only, we argue the 3D building data in 3D SDI must at least consider the issues of feature modelling, identification, semantics, level of details, cross-domain linking and services. This paper intends to assess the use of the semantic-enriched 3D building data in the applications of disaster management. Based on CityGML, we first create 3D building data based on a hierarchy of building-storey-household representation. Identifier systems are respectively developed for each level of features for the purpose of identifying individual features and linking to other sources of data, e.g., the household registration information. By reviewing and comparing the outcomes of the past research of 3D flood simulation, we demonstrate the improved 3D building data additionally enables the direct impact analysis at the chosen level of features, as well as visually present enriched analyzed outcomes for decision making, e.g., the number of trapped people in specific floor. As the merits of the SDI is to share reliable information, encourage multiple-purpose applications and avoid duplicated spending, we thereby conclude the necessity to further examine the level of details and multiple representation of the serviced 3D building data for cost-effective and cross-domain application development.