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  • 1.
    Jouybari, Arash
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad.
    Amiri, Hadi
    School of Engineering Science, College of Engineering, University of Tehran, Iran.
    Ardalan, Alireza A.
    School of Surveying and Geomatics Engineering, University of Tehran, Iran.
    Zahraee, Niloofar K.
    School of Surveying and Geomatics Engineering, University of Tehran, Iran.
    Methods comparison for attitude determination of a lightweight buoy by raw data of IMU2019Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 135, s. 348-354Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Today, one of the most important issues is the determination of instantaneous sea level and distinguishing the Tsunami by floating buoy in the ocean. Usually, gyroscopes are used to measure the angular velocity of a buoy. On the other hand, considering the advancement of various technologies in the field of precise accelerometers, make it possible to use these kinds of sensors for navigation purpose. In this research, stable and optimal methods for determining the orientation of a moving buoy is presented using a combination of the gyroscope, accelerometers, and magnetic sensors data. In order to prove the effectiveness of the proposed methods, the raw data were collected from accelerometers, gyroscopes, and magnetometers of (Xsens MTI-G-700) mounted on a Buoy in coastal waters of Kish Island, Iran. Then, by using the proposed methods, the Euler angles of the buoy are determined, while the Euler angles are derived from the Xsens sensor we are considered as a reference. Based on the results, RMSD for Madgwick algorithm are 0.57° 0.37° and 0.50° for Mahony algorithm are 0.56° 0.37° and 0.50° and finally for Complementary algorithm is 0.63° 0.26° and 2.38° which these values are for roll, pitch, and yaw angles respectively. Thus Mahony algorithm for determining roll and yaw Euler angles is more accurate than other algorithms; however, this differences is negligible compared to the Madgwick algorithm. The Complementary algorithm is less accurate than the other two algorithms, especially for determining the yaw angle of the buoy.

  • 2.
    Khodaverdi, Niloofar
    et al.
    School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
    Rastiveis, Heidar
    School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
    Jouybari, Arash
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad.
    Combination of Post-Earthquake LiDAR Data and Satellite Imagery for Buildings Damage Detection2019Inngår i: Earth Observation and Geomatics Engineering, ISSN 2588-4352, Vol. 3, nr 1, s. 12-20Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Earthquakes are known as one of the deadliest natural disasters that have caused many fatalities and homelessness through history. Due to the unpredictability of earthquakes, quick provision of buildings damage maps for reducing the number of losses after an earthquake has become an essential topic in Photogrammetry and Remote Sensing. Low-accuracy building damage maps waste the time that is required to rescue the people in destructed areas by wrongly deploying the rescue teams toward undamaged areas. In this research, an object-based algorithm based on combining LiDAR raster data and high-resolution satellite imagery (HRSI) was developed for buildings damage detection to improve the relief operation. This algorithm combines classification results of both LiDAR raster data and high-resolution satellite imagery (HRSI) for categorizing the area into three classes of “Undamaged,” “Probably Damaged,” and “Surely Damaged” based on the object-level analysis. The proposed method was tested using Worldview II satellite image and LiDAR data of the Port-au-Prince, Haiti, acquired after the 2010 earthquake. The reported overall accuracy of 92% demonstrated the high ability of the proposed method for post-earthquake damaged building detection.

  • 3.
    Khodaverdizahraee, Niloofar
    et al.
    University of Tehran.
    Rastiveis, Heidar
    University of Tehran.
    Jouybari, Arash
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad. University of Tehran.
    Akbarian, Sharare
    University of Tehran.
    Segment-by-segment comparison technique for generation of an earthquake-induced building damage map using satellite imagery2020Inngår i: International Journal of Disaster Risk Reduction, E-ISSN 2212-4209, Vol. 46, artikkel-id 101505Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Known as an unpredictable natural disaster, earthquake is one of the most devastating natural disasters that causes significant life losses and damages, every year. After an earthquake, quick and accurate buildings damage identification for rescuing can reduce the number of fatalities. In this regard, Remote Sensing (RS) technology is an efficient tool for rapid monitoring of damaged buildings. This paper proposes a novel method, titled segment-by-segment comparison (SBSC), to generate buildings damage map using multi-temporal satellite images. The proposed method begins by extracting image-objects from pre- and post-earthquake images and equalizing them through segmentation intersection. After the extraction of various textural and spectral descriptors on pre- and post-event images, their differences are used as an input feature vector in a classification algorithm. Also, the Genetic Algorithm (GA) is used to find the optimum descriptors in the classification process. The accuracy of the proposed method was tested on two different datasets from different sensors. Comparing the damage maps obtained from the proposed method with the manually extracted damage map, above 92% of the buildings were correctly labelled in both datasets.

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