Abstract: A significant Mw 6.5 earthquake occurred in Pidie Jaya, Aceh on December 7th, 2016. The event affected 104 people death and more than 1000 people suffered injuries due to the rubble of the building. Geologically, the region is composed by of Quaternary alluvial deposits. This is one of factor that amplification occurred in some area. On the other hand, an understanding of the source and mechanism of the earthquake needs to be done. A few days after the earthquake, we deployed 9 seismometers that covered the area of Pidie, Pidie Jaya and Bireuen. This experiment aims to record the aftershock and understanding of earth- quake source and mechanism. In addition, we conducted buildingdamage survey to know the pattern of distribution of buildingdamage.
An earthquake potentially destroys a tall building. The buildingdamage can be indexed by FEMA into three categories namely immediate occupancy (IO), life safety (LS), and collapse prevention (CP). To determine the damage index, the building model has been simulated into structure analysis software. Acceleration data has been analyzed using non linear method in structure analysis program. The earthquake load is time history at surface, PGA=0105g. This work proposes an intelligent monitoring system utilizing artificial neural network to predict the buildingdamage index. The system also provides an alert system and notification to inform the status of the damage. Data learning is trained on ANN utilizing feed forward and back propagation algorithm. The alert system is designed to be able to activate the alarm sound, view the alert bar or text, and send notification via email to the security or management. The system is tested using sample data represented in three conditions involving IO, LS, and CP. The results show that the proposed intelligent monitoring system could provide prediction of up to 92% rate of accuracy and activate the alert. Implementation of the system in building monitoring would allow for rapid, intelligent and accurate prediction of the buildingdamage index due to earthquake.
Figure 5 (left) is an example of the inte- rpretation of damage to buildings in the Bakalan sub-village, Argomulyo village, Cangkringan by using Geojot and GPS Photo Link application. Geojot is used to generate geotagged photograph and to fill the attributes of geotagged photograph, while the GPS Photo Link is used to create reports and spatial data based on pho- tographs from Geojot with attributes that have been filled. It can be seen that the large building collapse occurred, large part damage on main structure, most broken/ cracked/removed, partialy lifted on roof, blown out on windows but frame intact, totally damage on supporting component, and harm to be functionalized. Based on the above photo and the attributes, GPS Photo Link can be assembled into water- mark photo as report that shows the buildingdamage attribute information. The geotagged photograph as shown in Figure 5 (right) was taken with GPS Lock-Off mode so that the coordinates listed are the coordinates of camera positions. Figures 290° WNW is the direction of the shooting (the camera towards the object to be photographed). GPS accuracy that can be obtained when shooting with geotagging Android devices are + 5-10 meters. Desired minimum accuracy limit for the GPS when photographing can be determi- ned on Geojot settings.
In this paper, a novel approach is presented that applies multiple overlapping UAV imagesto buildingdamage detection. Traditional buildingdamage detection method focus on 2D changes detection (i.e., those only in image appearance), whereas the 2D information delivered by the images is often not sufficient and accurate when dealing with buildingdamage detection. Therefore the detection of buildingdamage in 3D feature of scenes is desired. The key idea of 3D buildingdamage detection is the 3D Change Detection using 3D point cloud obtained from aerial images through Structure from motion (SFM) techniques. The approach of buildingdamage detection discussed in this paper not only uses the height changes of 3D feature of scene but also utilizes the image's shape and texture feature. Therefore, this method fully combines the 2D and 3D information of the real world to detect the buildingdamage. The results, tested through field study, demonstrate that this method is feasible and effective in buildingdamage detection. It has also shown that the proposed method is easily applicable and suited well for rapid damage assessment after natural disasters.
In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional buildingdamage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in buildingdamage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
Satellite remote sensing data are the only available data source during the first hours/days after the event. Aerial images, if acquired, are available starting from a few days after the event, while field information (the ground truth data used for validation purposes) are generally acquired several weeks later, often during the Post Disaster Needs Assessment phase. Therefore satellite remote sensing play a crucial role in supporting the emergency response activities, especially in depicting the overall spatial pattern of buildingdamage when aggregated at building block level (Corbane et al, 2011b) The methodology largely adopted in an operational context in order to cope with the time constraints and the accuracy requirements of emergency response activities is based on visual interpretation of buildingdamage through comparison of pre and post event very high resolution optical remote sensing data. Several methodologies have been proposed for the analysis of SAR intensity and/or phase data, that should be further investigated in order to fully exploit this data source (crucial especially in case of persistent cloud cover in the first hours after the event) in an operational framework.
The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of “debris”, “intact building” and “unclassified”. Since textural information make better difference between “debris” and “intact building” classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as “debris” class to the whole building area. Calculating the damage degree for each candidate building, finally, buildingdamage map is generated.
Subsequently, the work programme should compile tools and approaches to understand, reduce and address the specific types of loss & damage. This area could help articulate lessons learned, good practice, challenges and analysis of relevance of various instruments and frameworks in the context of adaptation and disaster risk reduction.
acidification, biodiversity loss, loss in arable land or glacier melt. This underlines the historic responsibility of industrialized countries, from where the major share of emissions originate. In wake of existing mitigation actions that feature a significant gap in emission reduction to be consistent with a 2° C let alone 1.5° C pathway and that rather commit humanity to a 2.5 to 5° C degrees world 1 , it is high time for Parties to address the consequences of loss & damage, in parallel with stepping up their mitigation ambition.
To perform quantitative analysis, standard false color composite image of band XS3 as red, XS2 as green and XS1 as blue was created. This provides an immediate feature of post-fire condition. Through visual interpretation either on hardpaper or on computer screen, burnt and unburnt areas could be obviously discriminated. Light red colors correspond to unburnt vegetation either natural or plantation forests, while black with slightly red color shows burnt areas, mainly severely and extremely burnt forest. Since the emphasize was to discriminate the degree of forest damage digitally based on percentage of healthy life-tree, visual interpretation was performed to assist quantitative analysis especially during the selection of training area. In this study, since SPOT multispectral data have only three bands, feature evaluation was done using single band, two-band and three-band combinations. Of all possible combination evaluated, the study found that the best accuracy and inter-class separability were provided but using three-band combinations, i.e., band “XS1” (0.5 to 0.59 m green), band “XS2” (0.61 to 0.68 m, red) and band “XS3” (0.7 to 0.89, near infra red). As proved by Jaya and Kobayashi (1995), band combination using visible and infrared bands was recognized to be useful for detecting biomes or vegetation changes. Green band was expected to provide valuable information on green reflectance of healthy vegetation. Red band that corresponds to chlorophyll absorption of healthy green vegetation biomass present in a scene. This is particularly useful for land and water discrimination (Jensen, 1986).
To show how sharing liability will lead generators to a selection mechanism that does not always accept the low bidder, and to show that this is not only profit enhancing for the generator but is efficient for society as well, we will develop a discrete example. In this example, there will be one generator that requests bids from two TSDFs for handling the generator’s waste. We begin with the base case, which is where TSDFs bear all liability, and show that there will sometimes be a misallocation of waste—that in some states of the world, the winning TSDF will be taking too little care, and that social costs would be lower if the other TSDF had won the auction. Then we consider what would happen if damages were shared between the TSDF and the generator. We show that in some states of the world, the generator will find it profitable to reject the low bidder and instead offer the job to the high bidder at a mutually agreeable price. Such a transfer of responsibility will also be shown to be socially efficient. Although we rely on a discrete example to make our points, we believe that the example is indicative of general effects. Assumptions and Base-Case Analysis (Solo Liability with TSDF). The timing of actions in the model are as follows. The two TSDFs first receive an informational “message” on damage costs. Given their message, each TSDF must decide on a level of care and commit to that care level. Next, the TSDFs each submit a bid to the generator. The generator then selects the winner, and the waste is processed at the level of care chosen previously by the winning TSDF.
Gereja Kristen Jawi Wetan (GKJW) is a church that has the remark of local community in East Java. At the beginning, GKJW was evolved in rural areas, and later development has embraced urban areas, where in total 158 congregations exist until now. Churches acommodating these congregations, commonly adopt four different layouts with windows at the perimeter. For lighting, the churches use daylight and electrical lights. Combination of the two lighting systems is believed contributing to religious atmosphere in the churches. Lighting plays an important role not only in determining quantity of light for facilitating activities but also in creating aesthetic quality and certain expression in a building. For religious buildings, a designer considers the lighting system very carefully as it gives significant influence on the worship service. In the case of GKJW churches, there are variations in lighting quality, and the present study aims at exploring this quality, especially that relates to daylighting.
As the story unfolds, we see that the brutality continues in post-conflict times where economic and political interests collide. The pollution of the Asahan water is an evidence of the avarice of the power of the day as to ignore the ordinary people’s sufferings. Big multinational company has succeeded in buying off the officials to misuse their power for private gains at the expense of environmental deterioration. Here the Papal encyclical rings true: Environmental damage is inseparable from human and ethical degradation. It is a big lie that the earth’s supply is infinite. As Mahatma Gandhi says, Nature is accessible for people’s need but not for people’s greed.