bluuebunny/crossref_metadata_2025 huggingface dataset:
Stepped Care Models for Chronic Pain
Authors: Barbara Angela M. , Severn Melissa
Published: May 2022

CADTH included 6 reports describing the potential clinical benefits of 5 stepped care models for chronic pain in this review.

The randomized controlled trial of the stepped care model for patients who are overweight or obese with chronic pain due medial tibiofemoral osteoarthritis described the impact on pain remission, pain intensity, and function.

The before-after and retrospective cohort study of the Stepped Care Model of Pain Management described the effects on pain, quality of chronic pain care, pharmacotherapy use, and health care utilization in US veterans.

The before-after study of the biopsychosocial stepped care model for non-cardiac chest pain described the potential benefits on chest pain, depression, anxiety, quality of life, and health care use.

The prospective cohort study of the Stepped Care Strategy compared a cohort of patients who received stepped care strategy-consistent care for hip or knee pain due to osteoarthritis with a cohort who received stepped care strategy-inconsistent care and described the results on pain physical function, self-efficacy, active pain coping, and health care use.

The Evaluation of Stepped Care for Chronic Pain randomized controlled trial described the effects on pain, disability, and pharmacotherapy use in US veterans.

CADTH included 1 cost-effectiveness analysis that described the cost implications of a stepped care pathway for treating sciatica pain.

While all included models used a stepped care approach to chronic pain management, the models were customized, and the evaluation methods were heterogeneous.
SUPERVISI KLINIS MENGGUNAKAN PENDEKATAN PERSUASI, IDENTIFIKASI SERTA SOLUSI SEBAGAI UPAYA PENINGKATAN KUALITAS PEMBELAJARAN TENAGA PENDIDIK
Authors: HADI DINO LESMANA
Published: May 2022

The purpose of this study was to determine the improvement of the quality of teaching for teaching staff through clinical supervision using a persuasion, identification and solution approach at SMKS Bina Dharma Jakarta. The subjects of this study were 8 educators. Data collection techniques through learning activity documents and learning observations carried out by researchers using clinical supervision instruments, learning implementation plan evaluation sheets and learning implementation assessment sheets. The results of the study show that all educators can prepare the design and implementation well and have neatly presented learning documents.
ABSTRAKTujuan penelitian ini adalah untuk mengetahui peningkatan kualitas pembelajaran tenaga pendidik melalui supervisi klinis menggunakan pendekatan persuasi, identifikasi dan solusi di SMKS Bina Dharma Jakarta. Subyek penelitian ini adalah 8 orang tenaga pendidik. Teknik pengumpulan data melalui dokumen aktivitas pembelajaran serta observasi (pengamatan) pembelajaran yang dilakukan oleh peneliti dengan memakai instrumen supervisi klinis, lembar evaluasi rencana pelaksanaan pembelajaran dan lembar penilaian pelaksanaan pembelajaran. Hasil penelitian menunjukkan, semua tenaga pendidik bisa menyiapkan rancangan serta pelaksanaan dengan baik dan mempunyai dokumen pembelajaran yang tersaji rapih.
PEMBELAJARAN PROBLEM PASING STRATEGIS CARD SORT UNTUK MENINGKATKAN MINAT DAN HASIL RAB KELAS XII DPIB SMK NEGERI 1 CALANG
Authors: MOULIANA DEWI RINA
Published: May 2022

The results showed that from the results of the questionnaire regarding students' interest in learning, there was an increase in learning interest from cycle I to cycle II. This can be seen from the student's interest in learning in the first cycle there are 1 student (2.85%) for the high interest criteria there are 25 students (71.42%) for the moderate interest criteria, while in the second cycle there are 31 students (88.57%) with high interest criteria, based on the assessment criteria, namely 76% - 100% said to be of high interest. While the results of the tests carried out, there was an increase in student learning outcomes, from 35 students only 25 students (71.42%) who finished studying in the first cycle to 33 students (94.28%) in the second cycle. This shows that the second cycle has reached the classical KKM standard, which is 70% of students must get a score of 70. And there is a significant difference between cycle I and cycle II with the obtained value of tcount 10,8 > ttable 2,0322. Based on the above analysis, it can be concluded that the application of the Problem Posing learning model with the Card Sort learning strategy can increase students' interest and learning outcomes in the Budget Plan for the competency standard to understand the project management cycle in class XI of Building Drawing Engineering at SMKN.1 Calang in the 2021/2022 academic year.
ABSTRAKHasil penelitian menunjukkan bahwa dari hasil angket mengenai minat belajar siswa menunjukkan adanya peningkatan minat belajar dari siklus I ke siklus II. Hal ini terlihat dari minat belajar siswa pada siklus I terdapat 1 siswa (2,85%) untuk kriteria minat tinggi ada 25 siswa (71,42%) untuk kriteria minat sedang , sedangkan pada siklus II terdapat 31 siswa (88,57%) dengan kriteria minat tinggi, berdasarkan kriteria penilaian yaitu 76% - 100% dikatakan minat tinggi. Sedangkan hasiltes yang dilaksanakan, terdapat peningkatan hasil belajar siswa yaitu dari 35 siswa hanya 25 siswa (71,42%) yang tuntas belajar pada siklus I menjadi 33 siswa (94,28%) pada siklus II. Hal ini menunjukan siklus II sudah mencapai standar KKM secara klasikal yaitu 70% siswa harus memperoleh nilai ? 70. Dan adanya perbedaan yang signifikan antara siklus I dan siklus II dengan diperoleh nilai thitung10,8 > ttabel 2,0322. Berdasarkan analisis diatas dapat disimpulkan bahwa penerapan Model pembelajaran Problem Posing dengan strategi pembelajaran Card Sort dapat meningkatkan minat dan hasil belajar Rencana Anggaran Biaya siswa pada standar kompetensi memahami siklus managemant proyek di kelas XI Teknik Gambar Bangunan SMKN.1 Calang Tahun Pembelajaran 2021/2022.
EFEKTIVITAS PENGGUNAAN CLASSROOM DAN ZOOM MEETING MAPEL PRODUK KREATIF DAN KEWIRAUSAHAAN DI SMKN 7 PALANGKA RAYA
Authors: HAMBALI AKHMAD
Published: May 2022

This study aims to determine the results of online learning carried out using google classroom and zoom meeting in the classroom where the research takes place. The actions taken in this study were online learning through google classroom and zoom meetings which were held according to the lesson schedule. Data collection techniques were carried out in each cycle starting from the beginning to the end of the first cycle of action until the third cycle. Data collection was carried out using prepared instruments. It was held at SMK Negeri 7 Palangka Raya, in class XII of the Department of Computer Network Engineering (TKJ) with 17 students. This research is divided into 3 (three) cycles with each cycle consisting of four stages, namely: 1) planning, 2) action, 3) observation/evaluation, and 4) reflection, each cycle consists of 2 meetings. The results of this study indicate that there is an increase in learning outcomes for creative products and entrepreneurship (PKK) subjects in each cycle. The number of students who complete learning in the cycle is 71%, cycle II is 82% and cycle III is 100% so that in cycle III it is complete. This shows that online learning using google classroom combined with zoom meetings can provide students with a better understanding.
ABSTRAKPenelitian ini bertujuan untuk mengetahui hasil pembelajaran selama daring yang dilakukan menggunakan google classroom dan zoom meeting di kelas tempat berlangsungnya penelitian. Tindakan yang dilakukan dalam penelitian ini adalah pembelajaran daring melalui google classroom dan zoom meeting yang dilaksanakan sesuai jadwal pelajaran. Teknik pengumpulan data dilakukan pada setiap siklus dimulai dari awal sampai akhir tindakan siklus I sampai dengan siklus III. Pengumpulan data dilakukan dengan menggunakan instrumen yang sudah disiapkan. Dilaksanakan di SMK negeri 7 Palangka Raya, di kelas XII Jurusan Teknik Komputer Jaringan (TKJ) dengan jumlah peserta didik 17 orang. Penelitian ini dibagi dalam 3 (tiga) siklus dengan masing-masing siklus terdiri dari empat tahapan yaitu: 1) perencanaan, 2) tindakan, 3) observasi/evaluasi, dan 4) refleksi, tiap siklus terdiri dari 2 kali pertemuan. Hasil penelitian ini menunjukkan bahwa terjadi peningkatan hasil belajar mata pelajaran produk kreatif dan kewirausahaan (PKK) tiap siklus. Jumlah peserta didik yang tuntas dalam pembelajaran pada siklus yaitu 71%, siklus II sebesar 82% dan siklus III sebesar 100% sehingga pada siklus III sudah tuntas. Hal ini menunjukkan bahwa dalam pembelajaran daring menggunakan google classroom yang dikombinasikan dengan zoom meeting dapat memberikan pemahaman yang lebih baik kepada peserta didik.
PENERAPAN METODE BRAINSTORMING UNTUK MENINGKATKAN HASIL BELAJAR SISWA SMK PADA PEMBELAJARAN KEIMANAN PADA TAQDIR ALLAH
Authors: MARPIAH MARPIAH
Published: May 2022

This study aims to determine the improvement of learning outcomes for the subjects of Religious Education and Character Education through the application of the Brainstorming learning method in class XII TPM-3 students in the 2021/2022 academic year. This research is a classroom action research conducted in two cycles. Each cycle includes planning, action, observation and reflection. Learning outcomes are obtained through a description test, then the test results are collected for analysis using quantitative descriptive analysis. The results showed that, (1) the application of the Brainstorming learning method could improve student learning outcomes, it was proven that in the first cycle there were 25 students completed (76%). Likewise in the second cycle with a total of 34 students completed (97%) then student learning outcomes increased which previously the average value was 74 after the second cycle increased with an average value of 84.
ABSTRAKPenelitian ini bertujuan untuk mengetahui peningkatan hasil belajar mata pelajaran Pendidikan Agama dan Budi Pekerti melalui penerapan metode pembelajaran Brainstorming pada siswa kelas XII TPM-3 tahun ajaran 2021/2022. Penelitian ini merupakan penelitian tindakan kelas yang dilaksanakan dalam dua siklus. Setiap siklus meliputi perencanaan, tindakan, observasi serta refleksi. Hasil belajar didapatkan melalui tes uraian, kemudian hasi tes dikumpulkan untuk dianalisis dengan menggunakan análisis deskriptif kuantitatif. Hasil penelitian menunjukkan bahwa, (1) penerapan metode pembelajaran Brainstorming dapat meningkatkan hasil belajar siswa terbukti pada siklus I terdapat 25 siswa tuntas (76%). Begitu juga pada siklus II dengan jumlah 34 siswa tuntas (97%) lalu hasil belajar siswa meningkat yang sebelumnya nilai rata-rata 74 setelah siklus II meningkat yaitu dengan nilai rata-rata 84.
UMA APLICAÇÃO WEB PARA MONITORAMENTO E ALERTA DE ALTAS TEMPERATURAS, GAS GLP E FUMAÇA DE UMA RESIDÊNCIA
Authors: Oliveira Tasso Chaves de , Lima Edilson Carlos Silva
Published: May 2022

: Atualmente a grande maioria dos sensores de detecção de gás GLP e fumaça disponíveis no mercado são eficientes somente quando há pessoas na residência, e isso acabando sendo uma grande barreira, uma vez que as pessoas necessitam sair de casa para trabalhar ou para tratar de interesses pessoais. Esse artigo buscou desenvolver uma aplicação web baseada em internet das coisas (IoT), com o intuito de garantir uma maior prevenção contra incêndios residenciais, através do monitoramento remoto de temperatura, gás GLP e fumaça, bem como a emissão de alertas por mensagens de WhatsApp. Para tal propósito, foi utilizado a linguagem de programação Java (versão 11) com módulos do Spring Framework, juntamente com a placa de desenvolvimento NodeMCU e sensores para Arduino. Além do mais, durante os alertas é registrado em um banco de dados, as altas temperaturas e as detecções de gás e fumaça com data e horário do ocorrido. Permitindo, assim, que o usuário tenha acesso ao histórico de detecção de cada mês. Os testes realizados em uma maquete foram bastante satisfatórios, e a aplicação web demonstrou eficácia tanto no monitoramento quando nos alertas.
SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN KARYAWAN PADA PT PELINDO I MENGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW)
Authors: Yusman Yanti , Nadriati Sri , Putra Nursaka
Published: May 2022

ABSTRAKBanyak cara dalam menyelesaiakan suatu permasalahan yang terjadi di sebuah PT didalam penyeksian karywan salah satunya bisa mengunakan metode Simple Additive Weighting (SAW) karna didalam metode SAW adalah sebuah metode yang mengunakan pemberian bobot pada setiap alternative dan melakukan perkalian jumlah bobot alternatif terhadap keriteria. Metode pengumpulan data yang digunakan dalam penelitian ini adalah wawancara dengan adanya wawancara langsung ke PT Pelindo I terdapat permasalahan dimana Pihak PT Pelindo terkedala didalam penyeleksian karyawan. Oleh sebab itu penulis tertarik untuk memberikan solusi terhadap permasalah yang terjadi sehingga bisa memberikan manfaat bagi penguna.Kata kunci: Sistem Penunjang Keputusan , SAW, Seleksi Karyawan
SISTEM INFORMASI AKUNTANSI PENJUALAN KREDIT MENGGUNAKAN METODE ACCRUAL BASIS PADA CV. SINAR ABADI
Authors: Pratiwi Yulia Indah , Norhan Linda , Kanivia Aan , Bangun Giovanny , Kartika Viar Dwi
Published: May 2022

ABSTRAKCV. Sinar Abadi merupakan perusahaan dagang yang berperan sebagai distributor makanan dari suplier di beberapa kota. Produk yang dijual adalah berbagai varian saus Mc Lewis, dan berbagai varian minyak goreng tropical. Perancangan sistem informasi akuntansi sangat diperlukan dalam perusahaan ini mengingat pencatatan yang dilakukan masih secara manual dinilai tidak efektif dan efisien karena rawan terjadi human error sehingga informasi yang dihasilkan kurang akurat.Tahap analisa dan perancangan digambarkan menggunakan Flowchart, lalu dibuat secara komputerisasi ke dalam diagram konteks dan diagram aliran data level 0 dan level 1 disertai dengan hubungan entitas data dan relasi antar tabel-tabel yang digunakan. Tahap selanjutnya adalah perancangan sistem perangkat lunak. Perancangan tersebut menggunakan PHP (Hypertext Pre Processor) sebagai bahasa pemrograman dan MySQL sebagai manajemen database. Dengan permasalahan yang ada pada perusahaan ini, tujuan dari penelitian ini adalah membuat sistem informasi akuntansi penjualan kredit menggunakan metode accrual basis pada CV. Sinar Abadi. Hasil dari penelitian ini adalah mengetahui pengolahan data penjualan kredit dan dapat menghasilkan laporan berupa jurnal penjualan, jurnal penerimaan kas, dan faktur penjualan, yang dapat digunakan perusahaan untuk pengambilan keputusan.Kata kunci: Penjualan, Kredit, Accrual Basis, Akuntansi
LANDSLIDE EVOLUTION PATTERN REVEALED BY MULTI-TEMPORAL DSMS OBTAINED FROM HISTORICAL AERIAL IMAGES
Authors: Santangelo M. , Zhang L. , Rupnik E. , Deseilligny M. P. , Cardinali M.
Published: May 2022

Abstract. Landslides are a widespread natural hazard that cause damages to people and to the built up environment. Accurate knowledge of landslide distribution is crucial to develop planning strategies, prevention and resilient communities worldwide. One of the most diffuse way of reporting landslides distribution in a territory is by preparing landslide inventory maps. Such a task is mostly accomplished by expert photo-interpretation of historical aerial photographs, which are an invaluable source of information because they portray the landscape when the anthropic pressure was lower than the present day, providing an observation of the landscape closer to the natural conditions. Despite such a common use of aerial photographs, they are poorly exploited to obtain quantitative measures to support landslide mapping activities. In this paper we present a comparison of two photogrammetric approaches to measure elevation changes in a 50-years period for an area densely affected by landslides in Southern Italy. The obtained results allowed to revisit the original expert mapping proving that such a method is a useful tool to support geomorphological mapping and to improve the overall accuracy of landslide inventories.
DENSE RECONSTRUCTION FOR TUNNELS BASED ON THE INTEGRATION OF DOUBLE-LINE PARALLEL PHOTOGRAPHY AND DEEP LEARNING
Authors: Zhang R. , Jing M. , Yi X. , Li H. , Lu G.
Published: May 2022

Abstract. In scenes of tunnels and underground engineering projects, the 3D modeling based on the traditional horizontal baseline photography method is difficult to make a trade-off between the modeling efficiency and image overlap under narrow space and close photography constraint conditions. Parallel photography provides a better alternative. Furthermore, the undulating tunnel surfaces make occlusion unavoidable, and the pixel scales vary evidently in parallel photography, both of which make it difficult to obtain expected models neither by Semi-Global Matching nor by Patch-based Multi-view Stereo techniques. Comparatively, more accurate 3D reconstruction results can be achieved by using learning techniques, which consider the global semantic information such as specular prior and reflective prior to making the matching more robust. Besides, it is convenient for geologists to acquire photos with smartphones or cameras by a single-line parallel photography method at the tunnel sites. But enough image overlap for reconstruction is still difficult for the above photography way. Therefore, this paper proposes a dense reconstruction method for tunnels using deep learning and double-line parallel photography techniques, and respectively makes comparisons with single-line parallel photography and traditional modeling methods. Experimental results show the feasibility and robustness of the proposed method, especially for tunnel surfaces with complicated textures and occlusions. Moreover, both the completeness and accuracy of the tunnel model with double-line photography are better than with single-line photography.
CREATING AND USING MASK IMAGES FOR SEGMENTATION IN POINT CLOUD DATA
Authors: Öztürk B. , Özkar M.
Published: May 2022

Abstract. The task of preparing the training data for machine learning is tedious but crucial for accurate results. Aiming at labelling meaningful features semi-automatically rather than manually in order to reduce time, we hereby present initial results for two cases of 13th century Seljuk brick-ornamentation. As our broader research involves machine learning methods for the segmentation of digital survey data for creating meaningful three-dimensional models, the primary goal here is to determine the parts of the patterns from the whole composition and to use this data for different buildings of the genre. Prior to any machine learning, labelling the data of either a whole pattern or pieces of a pattern is a time-consuming task prone to errors. We propose a semi-automated mask generating model for labelling. In order to create the black and white mask images of the original photographs, we utilise the colour difference between the pattern parts. Examined samples have at least three visually distinguishable colours that are turquoise, black and natural. We use photogrammetry-based survey data and image processing to create attributed point clouds and eventually 3D digital models. Using the ready batch processing of a commercial software, we create a distinct mask and apply it to all images of the photogrammetry process. Point cloud data is then created with RAW images, and the generated masks are used to filter desired patterns. As such, we are able to easily label the bricks in the point cloud towards a machine learning training set.
3D DIGITIZATION OF MUSEUM ARTEFACTS WITHIN THE INTERREG IRON AGE DANUBE PROJECT
Authors: Vuković M. , Balen J. , Potrebica H. , Mađerić M. , Španiček V.
Published: May 2022

Abstract. Iron Age Danube project dealt with a wide variety of topics and sites dating back to the Iron age along the Danube river. Primary goals set by the Archaeological museum in Zagreb were the creation of a cultural route for the site of Kaptol, presentation of the site itself and digitization of artefacts from the necropolis of Kaptol – Čemernica. The latter were a part of the permanent museum exhibition in the Iron age section at the Archaeological museum in Zagreb. This paper will present the workflow used to generate image-based 3D models of more than 100 artefacts. The museum itself doesn’t have a digitization laboratory or allocated space, a problem which was encountered in other cultural heritage digitization projects, which meant that the setup had to be quickly assembled and disassembled as needed. This paper will present the problems encountered during the entire process, from image acquisition to data processing, as well as the potential solutions. For the purposes of the project we used Agisofts Metashape software which at the time required masks for all photographs involved in the image-based modelling process, in a turntable static camera setup. The newer versions of the software have two additional options one of which ignores the surrounding reference points and uses the markers generated by Metashape to complete the initial camera alignment. The sheer amount of artefacts we aimed to digitize meant that we had to streamline the process of acquiring and processing the photographs and their respective masks. This process was meticulously documented and optimized to take the least possible time while obtaining a satisfactory level of detail on the resulting 3D model. Although the new versions of the software dispense with the need for masking the photographs, the process of removing the unwanted points from the point cloud and aligning different chunks can also be time consuming. This paper will present our results with various photo alignment methods and try to provide an objective comparison. We will also explore the possibilities of utilizing the finished models in computer generated reconstructions of sites, and their usability in promoting the museum and potential exhibitions. Finally, the paper will assess the value of digitizing larger parts of museum collections in light of the recent earthquake in Zagreb which seriously damaged most of the artefacts in the museum.
POST-ERUPTION LAVA DOME EMPLACEMENT MEASURED BY UAV PHOTOGRAMMETRY: AN INVESTIGATION ONE YEAR AFTER THE 2017–2019 MT. AGUNG ERUPTIONS
Authors: Andaru R. , Rau J.-Y. , Syahbana D. K. , Purnamasari H. D.
Published: May 2022

Abstract. We present an observation of morphological changes at Mt. Agung lava dome one year after the 2017–2019 eruption crisis using UAV-photogrammetry method. Five time-series UAV datasets involve the images collected during the crisis period and the newest data collection (July 16, 2020) were used to provide a detailed investigation of the changes in morphology inside the crater and land cover on the surrounding slopes. The digital surface models (DSMs) generated by structure-from-motion (SfM) with multi-view stereo (MVS) algorithm were used to quantify the dome growth, the surface emplacement, and the actual remaining deposited material eruption surrounding the summit. Analysis of the last two series orthoimages indicates that the crater surface's texture remarkably unchanged one year after the eruption crisis (the dome still presents rough surfaces that resemble small stones and sand). According to the DSMs difference, it is evident that there were no considerable surface displacements inside the dome. It implies that no significant magma pressure accumulation occurring the dome. However, we found a small-scale growth in the central dome, which has increased the dome height up to 2 m and inflate the dome with a volume of 45,950 m3. We have also observed a new lava lake (e.g., compound lava) with an area of 9,166 m2 in the southeast of the dome edge. This new lava lake uplifts the surface up to 29 m and translated to a 79,623 m3 additional volume. Meanwhile, the depression areas surrounding the central dome were observed with a depth between 0.5 and 4 m. The amount of material deposited on the volcano’s summit was identified with a total volume of 2.93 × 106 m3. This remaining deposited volume could be a potential lahar in the future. The ability to measure spatial and time-series of the lava dome changes from SfM-UAV, therefore, provides effective, detailed, and sometimes sole means of observing and quantifying dome surface emplacement in the period of before, during and after eruptions.
BUILDING FOOTPRINT EXTRACTION FROM SPACE-BORNE IMAGERY USING DEEP NEURAL NETWORKS
Authors: Tejeswari B. , Sharma S. K. , Kumar M. , Gupta K.
Published: May 2022

Abstract. One of the important and high-level detailing contained within basemaps is the ‘building feature’. Though pre-trained Deep Learning (DL) models are available for Building Feature Extraction (BFE), they are not efficient in predicting the buildings in other locations. This study explores the need and the major issue of implementing DL models for BFE from Very High Resolution Remote Sensing (VHRS) satellite data for any given area. Though advanced DL models are invented, in order to implement them, huge amount of potential training data is demanded for feed in. the building typologies are highly subjected to the context of study area including soil characteristics, culture/lifestyle/economy, architectural style and the building byelaws. The study believes that availability of enough training data of contextual buildings as one of the concern for effective model training. The study aims to extract the buildings present in the study area from Pleiades 1A (2019) RGB VHRS data using simple Mask R-CNN instance segmentation model which is training on the native contextual buildings. Here, an automated method of generating the location-specific training data for a given area is followed using Google Maps API (2021). The generated training data when trained on a deep learning architecture and predicted by the input data yielded promising results. The prediction accuracy of about 98.41% specificity, 96.20% predictive accuracy and 0.89 F1 score are achieved. The methods adopted assist the planning/governing bodies to accelerate the qualitative urban map preparation.
AN EFFICIENT HIERARCHICAL IMAGE RETRIEVAL METHOD FOR LARGE SET OF IMAGES USING LEARNING-BASED GLOBAL AND LOCAL IMAGE FEATURES
Authors: Wang Z. , Zhan Z. , Zhou G. , Wang X.
Published: May 2022

Abstract. Image retrieval is one of the supporting technologies for (near) real-time photogrammetry and loop closure detection in visual SLAM, the conventional retrieval strategy is to firstly obtain the image features of the query image and database images, and search for the resulted images based on nearest features retrieval. However, the image retrieval method based on traditional hand-crafted features (SIFT, SURF, GIST) are hard to guarantee both the efficiency of time and precision in practical applications. Nowadays, learning-based features have shown superior performance in ample computer vision tasks. Thus, this paper investigates several popular learning-based global features (ResNet101, VGG16+NetVLAD, Yolov3+VGG16+NetVLAD) and local features (SuperPoint), to take care of both time efficiency and precision, we present hierarchical image retrieval solutions that combines these two kinds of features, in which global feature is for accelerating searching speed and local feature is for precision. Specifically, three sets of hierarchical retrieval solutions are designed by various combinations of learning-based global feature and local feature. Their precision and time efficiency are compared on different public benchmarks (one contains more than 10,000 images), the experimental results show that among the proposed solutions, VGG16+NetVLAD+SuperPoint has the best performance in efficiency, but the precision is slightly lower than the solution preprocessed with Yolov3.
ROLL-SENSITIVE ONLINE CAMERA ORIENTATION DETERMINATION ON THE STRUCTURED ROAD
Authors: Huang Y. , Zhou J. , Li B. , Xiao J. , Cao Y.
Published: May 2022

Abstract. Online camera calibration technology can estimate the pose of the camera onboard in real time, playing an important role in many fields such as HD map production and autonomous vehicles. Some researchers use one vanishing point (VP) to calculate the pitch and yaw angle of the onboard camera. However, this method assumes that the roll angle is zero, which is impractical because of the inevitable installation error. This paper proposes a novel online camera orientation determination method based on a longitudinal vanishing point without the zero-roll hypothesis. The orientation of the camera is determined in two steps: calculating the pitch and yaw angles according to vanishing point theory, and then obtaining the roll angle with lane widths constraint which is modeled as an optimization problem. To verify the effectiveness of our algorithm, we evaluated it on the nuScenes dataset. As a result, the rotation error of the roll and pitch angle can achieve 0.154° and 0.116° respectively. Also, we deployed our method in the “Tuyou”, an autonomous vehicle developed by Wuhan University, and then tested it in the urban structured road. Our proposed method can reconstruct the ground space accurately compared with previous methods with zero-roll hypothesis.
COMPARISON OF UNCERTAINTY QUANTIFICATION METHODS FOR CNN-BASED REGRESSION
Authors: Wursthorn K. , Hillemann M. , Ulrich M.
Published: May 2022

Abstract. The evaluation of reliability is not only of high importance for safety-critical deep learning applications but for object pose estimation as well. The uncertainty of the result is one way to express its reliability. In order to better understand existing uncertainty quantification (UQ) methods and their performance on image-based regression tasks, we use a small CNN and various scenarios to evaluate the estimated uncertainties. The evaluation is done on different simplistic synthetic datasets, consisting of gray-scale images of squares on a darker background. We train the CNN to predict the square center position of the square in the image. We compare how different UQ methods perform under dataset shift, rotation, occlusion, noise changes in the images.
HIGH FIDELITY EDGE AWARE NORMAL ESTIMATION FOR LOW RESOLUTION AND NOISY POINT CLOUDS OF HERITAGE SITES
Authors: Abu-Hani J. , Zhang T. , Filin S.
Published: May 2022

Abstract. Surface normals play a pivotal role in most shape analysis applications. As such, their accurate computation has received a considerable amount of attention over the years. In heritage sites, where entities of complex shape are prevalent, the development of reliable means to estimate them becomes even more important. When approaching the computation of normals, not only the shape complexity becomes a factor, but also the nature of the data, specifically the point density and noise level. To handle both shape and data quality-related aspects, this paper proposes a new method for the computation of surface normals, focusing on entities of complex form. We consider portable laser scanners as our data acquisition source, as such scanners offer efficient site coverage. Nonetheless, as portable scans are characterized by relatively sparse point clouds and noisy responses, their processing becomes a challenge. While existing research focused on the development of robust estimation methods, their application scope exhibited some limitations in preserving sharp features. Here, we demonstrate how the use of the L0 norm optimization framework, which features an inherent ability to preserve sharp transitions, with no need to introduce assumptions about the scene structure, can accommodate such a problem. Results show improved performance compared to common normal computation schemes with more reliable and accurate estimations of their form.
“HEALING CRACKS” ON FOSSIL TEETH: IMPROVEMENT OF ODONTOLOGICAL STUDY METHODS IN PALAEONTOLOGICAL RESEARCH
Authors: Knyaz V. A. , Gaboutchian A. V. , Maschenko E. N. , Korost D. V. , Stepanov N. V. , Emelyanov A. V.
Published: May 2022

Abstract. A significant part of fossil findings, which are objects in palaeontological and palaeoanthropological research, is represented by teeth. Even if compared with skeletal remains, they are composed of highly mineralised tissues. This fact considerably increases their potential for being preserved withstanding destructive environmental factors. Nevertheless fossilisation process is accompanied by various changes in teeth including over the centuries with regard to their integrity or deformations. Thus among palaeontological findings there is a noticeable share of fragmented teeth. However we will focus in the current paper on a special group of teeth, which have preserved their most essential morphological features, being at the same time on the way to their fragmentation - cracked teeth.Recent morphological and especially morphometric study methods applied to dental findings have been developed largely in line with high-resolution imaging techniques, such as microfocus x-ray tomographic scanning. They provide diversity of detailed digital reconstructions of teeth and application of image processing. This allows improvements of existing methods in odontological studies as well as and development of new as well, including those using automated algorithms, e.g. automated digital odontometry. This technique is sensitive to reconstructed surface quality, uninterrupted requiring surfaces as cracks hinder running the algorithms. Thus we propose method for reconstructing cracked teeth, which allows to obtain better results in morphological studies of teeth. The method proposed is based on consistent stages of surface curvature analysis and minimizing average distance between points opposing cracks surfaces.
A COMBINATION OF ADVANCED PHOTOGRAMMETRY AND LASER TRIANGULATION TECHNIQUES (VIDEO LASER SCAN™) FOR THE FREE-FLYING 3D INSPECTION OF SUBSEA ASSETS
Authors: Dumont A.
Published: May 2022

Abstract. Over the last years, High-Definition (HD) video cameras have made visual inspections of subsea assets more efficient and a source of massive and critical information. But in many cases, this visual information is incomplete without dimensions and geometry and can be biased by the wrong optical perspective, the lack of scale, or bad water clarity.This paper will describe the development of a new Technology, called Video Laser Scan™ (or VLS™), based on the combination of Photogrammetry and Laser Triangulation techniques, that provides additional Intelligence to such subsea inspections. In addition, we will highlight the innovation recently brought by Artificial Intelligence and 3D Holograms.
DETERMINATION OF 3D WATER TURBIDITY PARAMETER FIELDS FROM LIDAR BATHYMETRY DATA BY VOLUMETRIC DATA ANALYSIS
Authors: Richter K. , Mader D. , Westfeld P. , Maas H.-G.
Published: May 2022

Abstract. Accurate information on turbidity in water bodies is relevant to numerous limnological and oceanological issues. However, the collection of turbidity parameters using conventional in-situ measurement methods is time-consuming and cost-intensive and therefore usually limited to very small study areas. The use of airborne LiDAR bathymetry data is a promising alternative. However, existing methods for deriving turbidity parameters from airborne LiDAR bathymetry data are limited to the determination of one single turbidity parameter per water column element. The paper presents a novel approach that overcomes the existing limitations enables the determination of 3D water turbidity fields. By volumetric data analysis, the vertical turbidity stratification in the water body can be determined. For validation purposes, the approach was applied to synthetic measurement data generated in a simulation as well as a real measurement data set of a shallow coastal water.
POINT CLOUD ACQUISITION TECHNIQUES BY USING SCANNING LIDAR FOR 3D MODELLING AND MOBILE MEASUREMENT
Authors: Altuntas C.
Published: May 2022

Abstract. Laser scanners collect three-dimensional spatial data of an imaging area in a very short time with high point density. The laser scanners can be classified by different aspects, such as multi beam, single beam, spinning, solid state, single or multi returns, short or long range, and field of view angle. The mapping industry usually looks for range, accuracy, point density, and measurement speed higher while cost, error, energy consumption and weight lower instruments. Particularly, developments in LiDAR photon imaging techniques have enabled laser scanning to be used in three-dimensional modelling, motion detection, autonomous vehicle and mobile measurement. The distance from the instrument to the scan point is measured by the pulse or phase-shift method in laser scanners. The scan beam is directed at a certain angle so that the imaging area can be measured in arrays of points. The orientation of the beam is provided as mechanically in some scanners, and by an opto-electronic mechanism in others. Moreover, some scanners use multiple 2D LiDAR planes for three-dimensional scanning. A mobile three-dimensional measurement can be performed by using multi-beam scanners integrated with the other related sensors. The high performance of the scanners is possible with knowing their measurement properties and technical specifications. In this study, LiDAR techniques, that perform scanning measurements by oriented beam, were investigated and their technical features were examined.
INTEGRATION OF KALMAN FILTERING OF NEAR-CONTINUOUS SURFACE CHANGE TIME SERIES INTO THE EXTRACTION OF 4D OBJECTS-BY-CHANGE
Authors: Anders K. , Winiwarter L. , Schröder D. , Höfle B.
Published: May 2022

Abstract. Automatic extraction of surface activity from near-continuous 3D time series is essential for geographic monitoring of natural scenes. Recent change analysis methods leverage the temporal domain to improve the detection in time and the spatial delineation of surface changes, which occur with highly variable spatial and temporal properties. 4D objects-by-change (4D-OBCs) are specifically designed to extract individual surface activities which may occur in the same area, both consecutively or simultaneously. In this paper, we investigate how the extraction of 4D-OBCs can improve by considering uncertainties associated to change magnitudes using Kalman filtering of surface change time series. Based on the change rate contained in the Kalman state vector, the method automatically detects timespans of accumulation and erosion processes. This renders change detection independent from a globally fixed minimum detectable change value. Considering uncertainties associated to change allows detecting and classifying more occurrences of relevant surface activity, depending on the change rate and magnitude. We compare the Kalman-based seed detection to a regression-based method using a three-month tri-hourly terrestrial laser scanning time series (763 epochs) acquired of mass movements at a high-mountain slope in Austria. The Kalman-based method successfully identifies all relevant changes at the example location for the extraction of 4D-OBCs, without requiring the definition of a global minimum change magnitude. In the future, we will further investigate which kind of change detection method is best suited for which types of surface activity.
EVALUATION OF AZURE KINECT DERIVED POINT CLOUDS TO DETERMINE THE PRESENCE OF MICROHABITATS ON SINGLE TREES BASED ON THE SWISS STANDARD PARAMETERS
Authors: Fol C. R. , Murtiyoso A. , Griess V. C.
Published: May 2022

Abstract. In the last few years, a number of low-cost 3D scanning sensors have been developed to reconstruct the real-world environment. These sensors were primarily designed for indoor use, making them highly unpredictable in terms of their performance and accuracy when used outdoors. The Azure Kinect belongs to this category of low-cost 3D scanners and has been successfully employed in outdoor applications. In addition, this sensor possesses features such as portability and live visualization during data acquisition that makes it extremely interesting in the field of forestry. In the context of forest inventory, these advantages would allow to facilitate the task of tree parameters acquisition in an efficient manner. In this paper, a protocol was established for the acquisition of 3D data in forests using the Azure Kinect. A comparison of the resulting point cloud was performed against photogrammetry. Results demonstrated that the Azure Kinect point cloud was of suitable quality for extracting tree parameters such as diameter at breast height (DBH, with a standard deviation of 2.2cm). Furthermore, the quality of the visual and geometric information of the point cloud was evaluated in terms of its feasibility to identify microhabitats. Microhabitats represent valuable information on forest biodiversity and are included in Swiss forest inventory measurements. In total, five different microhabitats were identified in the Azure Kinect Point cloud. The measurements were therefore comparable to sensors such as terrestrial laser scanning and photogrammetry. Therefore, we argue that the Azure Kinect point cloud can efficiently identify certain types of microhabitats and this study presents a first approach of its application in forest inventories.
RAPID MAPPING AND ASSESSMENT OF DAMAGES DUE TO TYPHOON RAI USING SENTINEL-1 SYNTHETIC APERTURE RADAR DATA
Authors: Meneses III S. F. , Blanco A. C.
Published: May 2022

Abstract. Typhoon Rai has recently affected central and southern parts of the Philippines. Based on the valuation of the country’s National Disaster Risk Reduction and Management Council (NDRRMC), it is estimated that the typhoon has damaged 1,700 buildings, 2 million houses, and 10 million hectares of agricultural land in the affected locations. Given the effects of the typhoon, in terms of the extent of the area where it has caused destruction, the tremendous economic losses due to its incurred damages, and to the number of people affected by it, it became necessary to create rapid damage assessment maps that could provide the needed geospatial information to emergency responders so they can prioritize areas of most concern. In this effort, Sentinel-1 synthetic aperture radar (SAR) imagery was used due to the data’s temporal resolution (i.e., pre- and post-disaster images are available), relative independence from atmospheric conditions (i.e., unaffected by cloud cover), and open-access availability (i.e., data can be readily downloaded after the typhoon). Complex coherence correlation from stacks of pre- and post-disaster SAR images were analyzed for change detection in order to create the rapid damage assessment maps. In order to validate the results, ancillary data (i.e., aerial photos, local reports, and UNITAR / UNOSAT damage tags and maps) were used to qualitatively and quantitatively assess the maps. Upon analysis, we found that there is good correspondence between the SAR-derived maps and the aerial photos/UNITAR maps and that the damage tags by UNITAR / UNOSAT would match the rapid damage assessment maps to up to 93% if the correlation threshold is set to 0.5 and if the damage classification is set to just two levels (i.e., “damaged” and “not damaged”). It is deemed that the resulting maps of this research will be useful in the on-going efforts to rehabilitate the affected areas of Typhoo Rai. Future work includes further ground validation efforts and use of other datasets and methods in deriving the rapid damage assessment maps.
DROUGHT PATTERN ASSESSMENT OVER MARATHWADA, INDIA THROUGH THE DEVELOPMENT OF MULTIVARIATE ADVANCE DROUGHT RESPONSE INDEX
Authors: Singh T. P. , Deshpande M. , Das S. , Kumbhar V.
Published: May 2022

Abstract. The intensity and frequency with which recent drought events are occurring has made management of food and water a challenge. The situation in Indian subcontinent is no difference. The presented study takes inspiration from such problems to propose a remote sensing based multivariate advance drought response index (ADRI). The proposed multivariate index takes into consideration long term conditions of precipitation, normalized difference vegetation index (NDVI), brightness temperature and soil moisture in a linear way for 8-day drought assessment over drought affected Marathwada region, Maharashtra, India. A value of 33 corresponding to ADRI was found to be normal condition over two decades which corresponds to normal vegetation condition index (VCI) value of 51.1 and 54.2. The last 7 years shows a consistent pattern in the change of regional ADRI values which suggests the years in which agricultural assistance is needed over the region. Drought over the region is found to shift from central to eastern and northern regions in the last 5 years. Temporal analysis for the duration suggests up to 5 percent of the area of Marathwada has been facing severe drought conditions over the last decade while up to 70 percent of the area is experience below normal conditions with varying intensities of water stress. The districts of Latur, Parbhani, Hingoli and Nanded which are downstream river Godavari have been affected the most due to large percentage of land being under agriculture.
RESEARCH ON THE KEY TECHNOLOGY OF UAV IN THE ACCURACY ASSESSMENT OF LAND COVER CLASSIFICATION
Authors: Gao W. , Wang G. , Li H. , Mao W. , Yin S. , Yao B.
Published: May 2022

Abstract. This paper uses UAV low-altitude aerial photography technology, equipped with orthophoto camera, tilt camera and LiDAR camera, to obtain and process high-precision evaluation data, which is used to evaluate the correctness of land cover classification. Through the design of aerial photography scheme and data collection and processing, the rapidly acquired digital orthophoto, oblique 3D model data and laser LiDAR form a complete set of technical processes, which can relatively accurately and objectively monitor the quality of surface data for the existing geographical conditions. situation is assessed. The main purpose of this study is to further enrich the technical means of quality inspection of surveying and mapping products, to improve the technical level of quality inspection and acceptance of geographic condition monitoring data, and to improve the accuracy and reliability of data quality evaluation.
IMPROVING LOCAL ADAPTIVE FILTERING METHOD EMPLOYED IN RADIOMETRIC CORRECTION OF ANALOGUE AIRBORNE CAMPAIGNS
Authors: Lelégard L. , Le Bris A. , Giordano S.
Published: May 2022

Abstract. An orthophotomosaic is as a single image that can be layered on a map. It is produced from a set of aerial images impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as the camera itself, like lens vignetting. These create some unsightly radiometric inhomogeneity in the mosaic that could be corrected by using a local adaptive filter, also named Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This current work introduces two elementary studies. In a first time, in order to quantify the loss of contrast due to the use of Wallis filter, a simple multi-scale score is proposed based on mathematical morphology operations. In a second time, an optimal window size for the filter is identified by considering some systematic radiometric behaviours in the images forming the mosaic through Principal Component Analysis (PCA). These two elementary studies are preliminary steps leading to a method of radiometric correction combining Wallis filtering and PCA.