scholarly journals The role of detail spatial data resulted from unmanned aerial vehicle for tourism area planning in Karst Area, Gunungkidul

2019 ◽  
Author(s):  
Warsini Handayani ◽  
Yudhistira Tri Nurteisa ◽  
Mohammad Isnaini Sadali
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Karst Area ◽  
Aerial Vehicle ◽  
Area Planning

scholarly journals Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia

2021 ◽  
Vol 11 (23) ◽  
pp. 11310
Author(s):  
Muhammad Yudhi Rezaldi ◽  
Ambar Yoganingrum ◽  
Nuraini Rahma Hanifa ◽  
Yoshiyuki Kaneda ◽  
Siti Kania Kushadiani ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
3D Modeling ◽  
Spatial Data ◽  
Three Dimensional ◽  
Megathrust Earthquakes ◽  
End Point ◽  
Aerial Vehicle ◽  
Particle Visualization ◽  
Evacuation Routes

Three-dimensional (3D) modeling of tsunami events is intended to promote tsunami safety. However, the developed 3D modeling methods based on Computational Fluid Dynamics and photorealistic particle visualization have some weaknesses, such as not being similar to the original environment, not measuring the wave’s end point, and low image accuracy. The method for 3D modeling of tsunamis that results from this research can fulfil those weaknesses because it has advantages, such as being able to predict the end point of waves, similar to the original environment, and the height and area of inundation. In addition, the method produces more detailed and sharper spatial data. Modeling in this research is conducted using Agisoft Metashape Professional software to a produce 3D orthomosaic from pictures taken with Unmanned Aerial Vehicle (UAV) technique or drone (photogrammetry), and 3ds max software is used for wave simulation. We take a sample of an area in Cilacap, Indonesia that was impacted by the 2006 southwest coast tsunamis and may be vulnerable to future big megathrust earthquakes and tsunamis. The results could be used to provide several benefits, such as the creation of evacuation routes and the determination of appropriate locations for building shelters.

scholarly journals KLASIFIKASI MANGROVE BERBASIS OBJEK DAN PIKSEL MENGGUNAKAN CITRA SENTINEL-2B DI SUNGAI LIONG, BENGKALIS, PROVINSI RIAU

2018 ◽  
Vol 10 (3) ◽  
pp. 601-615
Author(s):  
. Rosmasita ◽  
Vincentius P. Siregar ◽  
Syamsul B. Agus
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Data Availability ◽  
Support Vector ◽  
Object Based ◽  
Svm Algorithm ◽  
Aerial Vehicle ◽  
Increase In Accuracy

ABSTRAK Penelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan  (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutru-turut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek. ABSTRACTResearch on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community.

The Role of Affective Computing for Improving Situation Awareness in Unmanned Aerial Vehicle Operations

2019 ◽  
pp. 295-305
Author(s):  
Jonathan Bishop
Keyword(s):  
Missing Data ◽  
Cognitive Load ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Affective Computing ◽  
Affect Recognition ◽  
Aerial Vehicles ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs), commonly known as drones, are a robotic form of military aircraft that are remotely operated by humans. Due to lack of situation awareness, such technology has led to the deaths of civilians through the inaccurate targeting of missile or gun attacks. This chapter presents the case for how a patented invention can be used to reduce civilian casualties through attaching an affect recognition sensor to a UAV that uses a database of strategies, tactics and commands to better instruct fighter pilots on how to respond while in combat so as to avoid misinterpreting civilians as combatants. The chapter discusses how this system, called VoisJet, can reduce many of the difficulties that come about for UAV pilots, including reducing cognitive load and opportunity for missing data. The chapter concludes that using UAVs fitted with VoisJet could allow for the reduction of the size of standing armies so that defence budgets are not overstretched outside of peacetime.

The Role of Affective Computing for Improving Situation Awareness in Unmanned Aerial Vehicle Operations

2015 ◽  
pp. 404-414
Author(s):  
Jonathan Bishop
Keyword(s):  
Missing Data ◽  
Cognitive Load ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Affective Computing ◽  
Affect Recognition ◽  
Aerial Vehicles ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs), commonly known as drones, are a robotic form of military aircraft that are remotely operated by humans. Due to lack of situation awareness, such technology has led to the deaths of civilians through the inaccurate targeting of missile or gun attacks. This chapter presents the case for how a patented invention can be used to reduce civilian casualties through attaching an affect recognition sensor to a UAV that uses a database of strategies, tactics and commands to better instruct fighter pilots on how to respond while in combat so as to avoid misinterpreting civilians as combatants. The chapter discusses how this system, called VoisJet, can reduce many of the difficulties that come about for UAV pilots, including reducing cognitive load and opportunity for missing data. The chapter concludes that using UAVs fitted with VoisJet could allow for the reduction of the size of standing armies so that defence budgets are not overstretched outside of peacetime.

scholarly journals Investigating the Role of Meteorological Factors in the Vertical Variation in PM2.5 by Unmanned Aerial Vehicle Measurement

2019 ◽  
Vol 19 (7) ◽  
pp. 1493-1507 ◽  
Author(s):  
Si-Jia Lu ◽  
Dongsheng Wang ◽  
Zhanyong Wang ◽  
Bai Li ◽  
Zhong-Ren Peng ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Meteorological Factors ◽  
Vertical Variation ◽  
Aerial Vehicle

The Role of Human Causal Factors in U.S. Army Unmanned Aerial Vehicle Accidents

2004 ◽  
Author(s):  
Sharon D. Manning ◽  
Clarence E. Rash ◽  
Patricia A. LeDuc ◽  
Robert K. Noback ◽  
Joseph McKeon
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Causal Factors ◽  
Vehicle Accidents ◽  
Aerial Vehicle

scholarly journals Object-based image analysis (OBIA) for mapping mangrove using Unmanned Aerial Vehicle (UAV) on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta Province

2021 ◽  
Vol 944 (1) ◽  
pp. 012037
Author(s):  
R A Pasaribu ◽  
F A Aditama ◽  
P Setyabudi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Design Research ◽  
Direct Role ◽  
Object Based Image Analysis ◽  
Mangrove Ecosystems ◽  
Sensing Technology ◽  
Object Based ◽  
Good Classification ◽  
Aerial Vehicle

Abstract Tidung Kecil Island is a conservation and mangrove cultivation area. Therefore, the potential of mangrove ecosystems on Tidung Kecil Island will have a direct role in coastal ecosystems. Accurate mangrove mapping is necessary for the effective planning and management of ecosystems and resources because mangroves function as protectors of ecological systems. The utilization of remote sensing technology that is near real-time can be used as an alternative in providing spatial data effectively. Mapping earth’s surface objects method is growing especially after the development of design, research, and production of flexible Unmanned Aerial Vehicle (UAV) platforms. The use of object-based classification methods is currently an alternative in classifying an object of the Earth’s surface using both satellite and aerial photo imagery data (orthophoto) that has a high accuracy value. This research aim is to map object based mangrove ecosystems using UAV technology on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta. The K-NN algorithm result was a good classification with 81.081% overall accuracy (OA) at the optimum value of the MRS segmentation scale 300;0,1;0.7 and divided into two classes which are mangrove and non-mangrove for 0.381 ha and 20.912 ha respectively.

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