Pub Date : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-227-2020
M. Ennaji, H. Boukachour, M. Machkour, Y. Kabbadj
Abstract. An Intelligent Tutorial System (ITS) is a learning computer environment. Many ITSs do not integrate human tutor since they are designed to use in autonomy by the learner. One of the reasons to increase the rate of desertion in a distance training framework compared to that of a face-to-face course is the absence of the human killer. Besides, the existing ITSs are dedicated to a single learning object based on domain-dependent modelling. Our contribution consists in proposing an ITS, independent of the learning domain, capable of initiating learning, of managing an articulation between machine tutoring and human tutoring (teaching and peers) to offer an individualized and personalized follow-up, and ensure certification of the learner’s assessment.
{"title":"COLLABORATIVE TUTORING: A MULTI-TUTOR APPROACH","authors":"M. Ennaji, H. Boukachour, M. Machkour, Y. Kabbadj","doi":"10.5194/isprs-archives-xliv-4-w3-2020-227-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-227-2020","url":null,"abstract":"Abstract. An Intelligent Tutorial System (ITS) is a learning computer environment. Many ITSs do not integrate human tutor since they are designed to use in autonomy by the learner. One of the reasons to increase the rate of desertion in a distance training framework compared to that of a face-to-face course is the absence of the human killer. Besides, the existing ITSs are dedicated to a single learning object based on domain-dependent modelling. Our contribution consists in proposing an ITS, independent of the learning domain, capable of initiating learning, of managing an articulation between machine tutoring and human tutoring (teaching and peers) to offer an individualized and personalized follow-up, and ensure certification of the learner’s assessment.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"25 1","pages":"227-232"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85503208","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-343-2020
B. Prima, M. Bouhorma
Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.
{"title":"USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION","authors":"B. Prima, M. Bouhorma","doi":"10.5194/isprs-archives-xliv-4-w3-2020-343-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-343-2020","url":null,"abstract":"Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"42 1","pages":"343-349"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79873054","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-355-2020
N. Ridzuan, U. Ujang, S. Azri, T. Choon
Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.
{"title":"VISUALISING URBAN AIR QUALITY USING AERMOD, CALPUFF AND CFD MODELS: A CRITICAL REVIEW","authors":"N. Ridzuan, U. Ujang, S. Azri, T. Choon","doi":"10.5194/isprs-archives-xliv-4-w3-2020-355-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-355-2020","url":null,"abstract":"Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"94 1","pages":"355-363"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83910252","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-233-2020
M. Fouad, R. Mali, A. Lmouatassime, M. Bousmah
Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.
{"title":"MACHINE LEARNING AND IOT FOR SMART GRID","authors":"M. Fouad, R. Mali, A. Lmouatassime, M. Bousmah","doi":"10.5194/isprs-archives-xliv-4-w3-2020-233-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-233-2020","url":null,"abstract":"Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"51 1","pages":"233-240"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88007834","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-255-2020
G. Ikrissi, T. Mazri
Abstract. The smart campus is a sustainable and well-connected environment that aims to improve experience, efficiency and education. It uses a variety of interconnected components, smart applications and networked technologies to facilitate communication, make more efficient use of resources, improve performance, security and quality of campus services. However, as with many other smart environments, the smart campus is vulnerable to many security issues and threats that make it face many security-related challenges that limit its development. In our paper, we intend to provide an overview of smart campuses by highlighting the main applications and technologies used in this environment, presenting several vulnerabilities and susceptible attacks that affect data and information security in the smart campus. Moreover, we discuss the major challenges of smart campus and we conclude by overviewing some current security solutions to deal with campus security issues.
{"title":"A STUDY OF SMART CAMPUS ENVIRONMENT AND ITS SECURITY ATTACKS","authors":"G. Ikrissi, T. Mazri","doi":"10.5194/isprs-archives-xliv-4-w3-2020-255-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-255-2020","url":null,"abstract":"Abstract. The smart campus is a sustainable and well-connected environment that aims to improve experience, efficiency and education. It uses a variety of interconnected components, smart applications and networked technologies to facilitate communication, make more efficient use of resources, improve performance, security and quality of campus services. However, as with many other smart environments, the smart campus is vulnerable to many security issues and threats that make it face many security-related challenges that limit its development. In our paper, we intend to provide an overview of smart campuses by highlighting the main applications and technologies used in this environment, presenting several vulnerabilities and susceptible attacks that affect data and information security in the smart campus. Moreover, we discuss the major challenges of smart campus and we conclude by overviewing some current security solutions to deal with campus security issues.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"6 1","pages":"255-261"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89254669","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-143-2020
D. Capkın, U. Isikdag, T. Tong
Abstract. Building Information Modelling (BIM) has been the most popular Architecture, Engineering and Construction (AEC) technology and information management approach for the last 5 years. The popularity of the approach not only comes from its role in enabling an efficient exchange of information and collaboration between the stakeholders of a construction project, but also is related with the benefits it has provided in detecting possible errors in the design phase, and providing means for the elimination of these errors as early as possible and thus minimizing the time and financial loss in the project. The training related to BIM is provided in the undergraduate and post-graduate levels, also with certification programs. The viewpoint of the students/trainees is closely related to their attitude towards BIM. The research explained in this paper aimed to identify the viewpoint of design school students on Building Information Modelling. The study started with a literature review on the foundational concepts regarding BIM. The second stage of the study included data collection with a questionnaire survey. Later this data is explored through descriptive statistics, and some foundational hypothesis on the impact of group differences on the BIM viewpoint were tested. The findings indicate that the viewpoint of design school students shows a positive tendency towards using and implementing BIM in real-life projects. Besides, the group differences (such as gender, level, department) do not appear to have an impact on the viewpoint.
{"title":"DESIGN STUDENTS VIEWPOINT ON BIM: A PRELIMINARY ASSESSMENT OF THE INDICATORS","authors":"D. Capkın, U. Isikdag, T. Tong","doi":"10.5194/isprs-archives-xliv-4-w3-2020-143-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-143-2020","url":null,"abstract":"Abstract. Building Information Modelling (BIM) has been the most popular Architecture, Engineering and Construction (AEC) technology and information management approach for the last 5 years. The popularity of the approach not only comes from its role in enabling an efficient exchange of information and collaboration between the stakeholders of a construction project, but also is related with the benefits it has provided in detecting possible errors in the design phase, and providing means for the elimination of these errors as early as possible and thus minimizing the time and financial loss in the project. The training related to BIM is provided in the undergraduate and post-graduate levels, also with certification programs. The viewpoint of the students/trainees is closely related to their attitude towards BIM. The research explained in this paper aimed to identify the viewpoint of design school students on Building Information Modelling. The study started with a literature review on the foundational concepts regarding BIM. The second stage of the study included data collection with a questionnaire survey. Later this data is explored through descriptive statistics, and some foundational hypothesis on the impact of group differences on the BIM viewpoint were tested. The findings indicate that the viewpoint of design school students shows a positive tendency towards using and implementing BIM in real-life projects. Besides, the group differences (such as gender, level, department) do not appear to have an impact on the viewpoint.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"30 1","pages":"143-150"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88138776","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 : 2020-11-23DOI: 10.5194/isprs-archives-xliv-4-w3-2020-421-2020
Zhengrong Wu, Hao Wang, Wenhui Yu, J. Xi, W. Lei, T. Tang
Abstract. Constructing the transmission tower from LiDAR point clouds is a fundamental step for smart grid. However, currently the transmission tower construction method relies heavily on manual editing, which is far from the practical industrial application. This paper proposes a model-driven based method to realize 3D construction of transmission tower fast and accurately. This method first generates different types of 3D tower models. Then, it calculates the direction characteristic of point clouds distribution using the obtained transmission towers point clouds. While finding the principal direction of transmission towers, the local coordinates of the transmission towers are settled. And then the key points are captured in a semi-automatically way. According to these key points, the transmission tower model that best matches the point clouds is selected using the model matching algorithm. Comparing with the existing traditional manual editing methods, the method proposed in this paper can ensure the integrity and accuracy of the reconstructed tower model using the model-driven based strategy. The proposed method makes a trade-off between manual editing and efficiency, which guarantees the quality of tower modelling. And the feasibility and practicability of the proposed method are verified by the experiments on real-world point clouds data.
{"title":"3D HIGH-EFFICIENCY AND HIGH-PRECISION MODEL-DRIVEN MODELLING FOR POWER TRANSMISSION TOWER","authors":"Zhengrong Wu, Hao Wang, Wenhui Yu, J. Xi, W. Lei, T. Tang","doi":"10.5194/isprs-archives-xliv-4-w3-2020-421-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-421-2020","url":null,"abstract":"Abstract. Constructing the transmission tower from LiDAR point clouds is a fundamental step for smart grid. However, currently the transmission tower construction method relies heavily on manual editing, which is far from the practical industrial application. This paper proposes a model-driven based method to realize 3D construction of transmission tower fast and accurately. This method first generates different types of 3D tower models. Then, it calculates the direction characteristic of point clouds distribution using the obtained transmission towers point clouds. While finding the principal direction of transmission towers, the local coordinates of the transmission towers are settled. And then the key points are captured in a semi-automatically way. According to these key points, the transmission tower model that best matches the point clouds is selected using the model matching algorithm. Comparing with the existing traditional manual editing methods, the method proposed in this paper can ensure the integrity and accuracy of the reconstructed tower model using the model-driven based strategy. The proposed method makes a trade-off between manual editing and efficiency, which guarantees the quality of tower modelling. And the feasibility and practicability of the proposed method are verified by the experiments on real-world point clouds data.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"2 1","pages":"421-426"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88741420","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 : 2020-11-18DOI: 10.5194/isprs-archives-xliv-3-w1-2020-107-2020
H. Nho, D. Shin, S. Kim
Abstract. Recently, UAVs are being used in various fields such as photography, precision agriculture, remote monitoring, surveying, mapping, and disaster management. In particular, UAVs can acquire real-time data and access hard-to-reach areas, which is advantageous for rapid spatial information generation. Spatial information can be generated by mounting a camera on the UAV and performing the geocoding process of image data using the location/location information acquired from the GPS/INS sensor. The use of multiple GCPs during the geocoding process can increase the image position accuracy. However, since a lot of time is consumed for surveying, it is disadvantageous to be used in disaster fields that require urgent data generation. Therefore, in this study, fast geocoding process of UAV image using the minimum GCP is proposed. The results obtained through this process can be used as basic data for on-site monitoring and decision-making in disasters and emergencies.
{"title":"UAV IMAGE FAST GEOCODING METHOD FOR DISASTER SCENE MONITORING","authors":"H. Nho, D. Shin, S. Kim","doi":"10.5194/isprs-archives-xliv-3-w1-2020-107-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-3-w1-2020-107-2020","url":null,"abstract":"Abstract. Recently, UAVs are being used in various fields such as photography, precision agriculture, remote monitoring, surveying, mapping, and disaster management. In particular, UAVs can acquire real-time data and access hard-to-reach areas, which is advantageous for rapid spatial information generation. Spatial information can be generated by mounting a camera on the UAV and performing the geocoding process of image data using the location/location information acquired from the GPS/INS sensor. The use of multiple GCPs during the geocoding process can increase the image position accuracy. However, since a lot of time is consumed for surveying, it is disadvantageous to be used in disaster fields that require urgent data generation. Therefore, in this study, fast geocoding process of UAV image using the minimum GCP is proposed. The results obtained through this process can be used as basic data for on-site monitoring and decision-making in disasters and emergencies.\u0000","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"530 1","pages":"107-110"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80163916","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 : 2020-11-18DOI: 10.5194/isprs-archives-xliv-3-w1-2020-37-2020
R. Cong, K. Gomi
Taking the lessons from the Great East Japan Earthquake (GEJE) occurred in March 2011, the nuclear-reliant energy policy in Fukushima Prefecture has been transformed to other energy (fossil fuel, renewable energy) to make their energy system with better resilience toward the future disaster. As the increased concern on the Global Warming, Fukushima Prefecture made more efforts on the promotions of the renewable energy than the fossil fuel power. Nine years has passed since the GEJE, however, the spatial variation of the energy supply facilities is not clarified and the resilience of its energy system has not been evaluated. Therefore, this study focused on spatial analysis on these energy supply facilities before and after the GEJE and discussing the energy resilience in Fukushima Prefecture toward future disasters or climate events. This approach will be helpful for policy makers to spatiotemporally evaluate the sustainable development on the energy system. * Corresponding author
{"title":"EVIDENCE FOR THE DEVELOPMENT OF ENERGY RESILIENCE IN FUKUSHIMA PREFECTURE AFTER THE GREAT EAST JAPAN EARTHQUAKE","authors":"R. Cong, K. Gomi","doi":"10.5194/isprs-archives-xliv-3-w1-2020-37-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-3-w1-2020-37-2020","url":null,"abstract":"Taking the lessons from the Great East Japan Earthquake (GEJE) occurred in March 2011, the nuclear-reliant energy policy in Fukushima Prefecture has been transformed to other energy (fossil fuel, renewable energy) to make their energy system with better resilience toward the future disaster. As the increased concern on the Global Warming, Fukushima Prefecture made more efforts on the promotions of the renewable energy than the fossil fuel power. Nine years has passed since the GEJE, however, the spatial variation of the energy supply facilities is not clarified and the resilience of its energy system has not been evaluated. Therefore, this study focused on spatial analysis on these energy supply facilities before and after the GEJE and discussing the energy resilience in Fukushima Prefecture toward future disasters or climate events. This approach will be helpful for policy makers to spatiotemporally evaluate the sustainable development on the energy system. * Corresponding author","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"35 1","pages":"37-41"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85458983","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 : 2020-11-18DOI: 10.5194/isprs-archives-xliv-3-w1-2020-59-2020
J. Iqbal, M. Ali, Amjad Ali, D. Raza, F. Bashir, F. Ali, S. Hussain, Z. Afzal
Abstract. Glaciers are storehouses for freshwater. Glaciers Monitoring is one of the most important research areas especially when climate change has been accelerated snowmelt process. The major goal of research was to find snow cover trend for glaciated regions of Pakistan followed by estimation of snow mass balance. The area chosen for it was Upper Indus basin, which includes ranges of Hindukush, Karakoram and Himalayas extended in Pakistan, India and China. This region exhibits high topographic relief and climate change variability. Snow cover trend analysis was performed for eleven years ranging from 2004 to 2014 using Moderate Resolution Imaging Spectroradiometer (MODIS) data imagery product with daily temporal resolution. These results were combined with respective year’s average monthly temperature. Further quantitative analysis was performed to relate presence of greater vegetation as an indication of greater snowmelt using Landsat Imagery for these years. Snow mass balance curves reveal that glaciers are regaining their mass balance after losing mass balance in middle of last decade. In addition to that, only freely available data is used for this study. This purpose behind this approach is to prove RS and GIS has an effective and low-cost tool for snow cover monitoring, also mass balance calculations. Continuous monitoring of snow cover dynamics is effective for prediction and mitigation of hazards associated with areas in proximity of glaciated regions. One common hazard is glacial lake outburst phenomenon, which cause severe flash flooding in downstream areas. Year 2004 has the lowest mass snow balance and 2014 has the highest snow mass balance. These different parameters were analysed and results show that snow start melting in months of May and June and faster melting rate observed in months of July and August. With the advancement in computing technologies, it has been easier for computers to handle and manipulate massive datasets. Remote sensing has proved to be an excellent tool for extraction of data from glaciers, snow and oceans for remote areas. In particular, snow cover/snowmelt can tell us continuously changing melting patterns, which helps concerned authorities to take necessary measures for preserving these storehouses of water and to mitigate effect of global warming.
{"title":"INVESTIGATION OF CRYOSPHERE DYNAMICS VARIATIONS IN THE UPPER INDUS BASIN USING REMOTE SENSING AND GIS","authors":"J. Iqbal, M. Ali, Amjad Ali, D. Raza, F. Bashir, F. Ali, S. Hussain, Z. Afzal","doi":"10.5194/isprs-archives-xliv-3-w1-2020-59-2020","DOIUrl":"https://doi.org/10.5194/isprs-archives-xliv-3-w1-2020-59-2020","url":null,"abstract":"Abstract. Glaciers are storehouses for freshwater. Glaciers Monitoring is one of the most important research areas especially when climate change has been accelerated snowmelt process. The major goal of research was to find snow cover trend for glaciated regions of Pakistan followed by estimation of snow mass balance. The area chosen for it was Upper Indus basin, which includes ranges of Hindukush, Karakoram and Himalayas extended in Pakistan, India and China. This region exhibits high topographic relief and climate change variability. Snow cover trend analysis was performed for eleven years ranging from 2004 to 2014 using Moderate Resolution Imaging Spectroradiometer (MODIS) data imagery product with daily temporal resolution. These results were combined with respective year’s average monthly temperature. Further quantitative analysis was performed to relate presence of greater vegetation as an indication of greater snowmelt using Landsat Imagery for these years. Snow mass balance curves reveal that glaciers are regaining their mass balance after losing mass balance in middle of last decade. In addition to that, only freely available data is used for this study. This purpose behind this approach is to prove RS and GIS has an effective and low-cost tool for snow cover monitoring, also mass balance calculations. Continuous monitoring of snow cover dynamics is effective for prediction and mitigation of hazards associated with areas in proximity of glaciated regions. One common hazard is glacial lake outburst phenomenon, which cause severe flash flooding in downstream areas. Year 2004 has the lowest mass snow balance and 2014 has the highest snow mass balance. These different parameters were analysed and results show that snow start melting in months of May and June and faster melting rate observed in months of July and August. With the advancement in computing technologies, it has been easier for computers to handle and manipulate massive datasets. Remote sensing has proved to be an excellent tool for extraction of data from glaciers, snow and oceans for remote areas. In particular, snow cover/snowmelt can tell us continuously changing melting patterns, which helps concerned authorities to take necessary measures for preserving these storehouses of water and to mitigate effect of global warming.","PeriodicalId":14757,"journal":{"name":"ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"32 1","pages":"59-63"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88877405","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：