Pub Date : 2020-12-07DOI: 10.1109/agers51788.2020.9452751
Pubudu Dias, Kithsiri Jayakody
In the modern area of fast-moving technology we can do things that we never thought we couldn't do before and automation has taken over everything in day-to-day life. One such creation is the virtual assistant. It has become a boon for everyone in this new era of the 21st century. The improvement has gone up to the capabilities of becoming a personal companion to humans. We can ask questions from machines and can interact with machines using this technology of virtual assistance. This technology spread rapidly in smartphones, laptops, computers, etc. Some iconic virtual assistants are Siri, Google Assistant, Cortana, and Alexa. Voice recognition, speech identification, and the relevant reaction is the basis for virtual assistance. Some of the issues regarding the available virtual assistances are the incompatibility with certain languages. Sinhala is one such language at the moment not included in a virtual assistance environment. The proposed project is designed to associate some Sinhala commands with their corresponding responses to guide a Sinhala user. Furthermore, for global reach, the project will be deployed on a cloud basis for the public to reach the global Sinhala language users.
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Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452777
B. M. Sukojo, Nurwatik, N. Annisa
Mangrove grows in coastal areas with the soil is resulted from the accumulation of mud substrate from the sedimentation process. Gresik Regency is a downstream area where the Bengawan Solo River flows and there is the Java Sea that carries a lot of sediment material to the coast. As a result, there is sedimentation that is forming new land increasingly that can be a place for mangroves to live. Therefore in this study, the calculation of suspended sediment concentration and mangrove area in Gresik Regency during 2016–2019 is using Sentinel-2A satellite imagery. The purpose of these calculations is to determine the effect of sedimentation as suspended sediment on changes in the mangrove area. Mangrove area is obtained from the Maximum Likelihood supervised classification. While suspended sediment concentrations estimated from remote sensing data are obtained using four prior algorithms but those do not meet the specified accuracy requirement. This research shows that there have been changes in the mangrove area in the form of increasing and decreasing during 2016–2019. The largest addition of area occurred in the period 2016–2017 which is 479.347 Ha and the most reduction in the area occurred in the period 2018–2019 which is 534.087 Ha. The statistical test result proves that the suspended sediment as sedimentation affects the mangrove by 64.9% in the significance level of 5% or 95% confidence level.
红树林生长在沿海地区，土壤是泥沙沉淀过程中基底堆积而成的。Gresik Regency是班加万梭罗河的下游地区爪哇海将大量沉积物带到海岸。因此，沉积形成了越来越多的新土地，可以成为红树林的栖息地。因此，本研究使用Sentinel-2A卫星图像计算2016-2019年Gresik Regency悬沙浓度和红树林面积。这些计算的目的是确定作为悬浮沉积物的沉积对红树林地区变化的影响。红树林面积由最大似然监督分类得到。而根据遥感数据估算的悬沙浓度采用了先前的四种算法，但这些算法不符合规定的精度要求。研究表明，2016-2019年红树林面积呈现增减变化。2016-2017年增加面积最多，为479.347 Ha, 2018-2019年减少面积最多，为534.087 Ha。统计检验结果证明，在5%或95%置信水平的显著性水平上，悬浮泥沙作为沉降对红树林的影响为64.9%。
{"title":"Sedimentation Analysis Towards The Changes of Mangrove Area Using Multitemporal Remote Sensing Technology (Case Study: Gresik Regency)","authors":"B. M. Sukojo, Nurwatik, N. Annisa","doi":"10.1109/AGERS51788.2020.9452777","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452777","url":null,"abstract":"Mangrove grows in coastal areas with the soil is resulted from the accumulation of mud substrate from the sedimentation process. Gresik Regency is a downstream area where the Bengawan Solo River flows and there is the Java Sea that carries a lot of sediment material to the coast. As a result, there is sedimentation that is forming new land increasingly that can be a place for mangroves to live. Therefore in this study, the calculation of suspended sediment concentration and mangrove area in Gresik Regency during 2016–2019 is using Sentinel-2A satellite imagery. The purpose of these calculations is to determine the effect of sedimentation as suspended sediment on changes in the mangrove area. Mangrove area is obtained from the Maximum Likelihood supervised classification. While suspended sediment concentrations estimated from remote sensing data are obtained using four prior algorithms but those do not meet the specified accuracy requirement. This research shows that there have been changes in the mangrove area in the form of increasing and decreasing during 2016–2019. The largest addition of area occurred in the period 2016–2017 which is 479.347 Ha and the most reduction in the area occurred in the period 2018–2019 which is 534.087 Ha. The statistical test result proves that the suspended sediment as sedimentation affects the mangrove by 64.9% in the significance level of 5% or 95% confidence level.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132322727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452780
I. Meilano, Achmad Ikbal Rahadian, D. Suwardhi, Wulan Suminar, F. W. Atmaja, C. Pratama, E. Sunarti, S. Haksama
Assessing the building damage after a tsunami is the first step to quantitatively learn about the amount of damage it caused. Indonesia is an archipelagic country, with two-thirds of its territory consisting of water. It has the second-longest coastline in the world, increasing the potential for tsunami damage in Indonesian territory. In this study, an analysis of building damage due to the tsunamis was carried out and Palu was assigned as the study location. Palu's coastal area suffered a tsunami on September 28, 2018, caused by an earthquake with a magnitude of 7.5. The location and the number of buildings were generated through object detection using deep learning from high-resolution satellite imagery data. Object detection was carried out using pre-trained YOLOv3 models that are trained using 315 satellite images as data sets and produce a model with a loss value of 33.15. Object detection was carried out on satellite imagery before and after the tsunami and produced building distribution data with an accuracy of 76.78% and 74.20%, respectively. Comparisons of building data detected from the two satellite images were then analyzed using a tsunami height zone map to see the correlation between building damage and tsunami height. From spatial and correlations analysis, 1,547 damaged buildings were detected, giving the data a positive correlation type. Using the student's t-test, it was concluded that there was a significant correlation between building damage and tsunami height.
{"title":"Analysis of Damage to Buildings affected by the Tsunami in the Palu Coastal Area Using Deep Learning","authors":"I. Meilano, Achmad Ikbal Rahadian, D. Suwardhi, Wulan Suminar, F. W. Atmaja, C. Pratama, E. Sunarti, S. Haksama","doi":"10.1109/AGERS51788.2020.9452780","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452780","url":null,"abstract":"Assessing the building damage after a tsunami is the first step to quantitatively learn about the amount of damage it caused. Indonesia is an archipelagic country, with two-thirds of its territory consisting of water. It has the second-longest coastline in the world, increasing the potential for tsunami damage in Indonesian territory. In this study, an analysis of building damage due to the tsunamis was carried out and Palu was assigned as the study location. Palu's coastal area suffered a tsunami on September 28, 2018, caused by an earthquake with a magnitude of 7.5. The location and the number of buildings were generated through object detection using deep learning from high-resolution satellite imagery data. Object detection was carried out using pre-trained YOLOv3 models that are trained using 315 satellite images as data sets and produce a model with a loss value of 33.15. Object detection was carried out on satellite imagery before and after the tsunami and produced building distribution data with an accuracy of 76.78% and 74.20%, respectively. Comparisons of building data detected from the two satellite images were then analyzed using a tsunami height zone map to see the correlation between building damage and tsunami height. From spatial and correlations analysis, 1,547 damaged buildings were detected, giving the data a positive correlation type. Using the student's t-test, it was concluded that there was a significant correlation between building damage and tsunami height.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131260938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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