bluuebunny/crossref_metadata_2025 huggingface dataset:
Komparasi Metode Deep Learning, Naïve Bayes Dan Random Forest Untuk Prediksi Penyakit Jantung
Authors: Alhabib Ivana
Published: October 2022

Jantung adalah organ yang mempunyai peranan penting dalam kelangsungan hidup manusia karena fungsinya untuk mendistribusikan darah dari paru-paru ke seluruh bagian tubuh, yang dimana darah tersebut mengandung banyak sekali oksigen sehingga dapat membantu proses metabolisme di dalam tubuh manusia. Ada banyak aktivitas dalam tubuh manusia yang tidak dapat diprediksi dalam bentuk umum. Serangan jantung adalah salah satunya, dan itu adalah aktivitas yang sangat serius dalam tubuh manusia yang menyebabkan kematian manusia. Meskipun tidak terlalu terlihat dalam kondisi normal, itu dilakukan secara tiba-tiba. Jadi ini adalah salah satu kejadian yang sangat tidak terduga dalam tubuh manusia. Dengan kemajuan teknologi beberapa algoritma penambangan data dikembangkan untuk memprediksi serangan jantung. Dalam kelanjutannya, algoritma penambangan data yang berbeda, dengan pendekatan machine learning mampu memprediksi terjadinya serangan jantung dalam tubuh manusia. Ini adalah salah satu tugas diagnosis yang khas, tetapi harus dicapai secara akurat dan efisien dengan bantuan pembelajaran mesin. penelitian ini adalah upaya untuk memodelkan dan memecahkan masalah prediksi serangan jantung. Algoritma mesin yang berbeda seperti Deep Learning, Naives Bayes dan Random Forest diambil di sini untuk membentuk model dalam penelitian ini. pendekatan pembelajaran mesin adalah pendekatan yang baik untuk memprediksi terjadinya serangan jantung. Dataset diambil dari laman Kaggle dengan judul heart attack analysis dan prediction dataset. Akurasi paling tinggi yang dapat dihasilkan adalah menggunakan metode algortma deep learning dimana menghasilkan akurasi yang bernilai, yaitu 83,49%.
Sistem Pendeteksi Kebocoran LPG dan Api Melalui Notifikasi Telegram
Authors: Oktaviani Delvia , Putri Dwi Ismiyana
Published: October 2022

Gas LPG sangat dibutuhkan dalam kehidupan sehari-hari, akan tetapi adanya permasalahan yang terjadi. Yaitu adanya kebocoran gas LPG yang dapat membahayakan pengguna dan masyarakat sekitarnya. Maka dari itu penelitian ini bertujuan untuk mendeteksi terjadinya kebocoran gas LPG yang dapat menyebabkan bahaya seperti kebakaran. Dengan adanya alat ini yang berfungsi untuk mendeteksi kebocoran gas LPG yang dilengkapi sensor gas MQ2 serta yang dilengkapi sensor api. Alat pendeteksi kebocoran gas LPG ini dapat mengirim notifikasi adanya indikasi kebocoran melalui telegram sehingga pengguna alat ini dapat memantau keadaan tabung gas melalui telegram, dengan NodeMCU yang telah diprogram. Dengan adanya alat ini diharapkan dapat membantu pengguna gas LPG dalam mendeteksi kebocoran gas yang terjadi, sehingga dapat melakukan tindakan selanjutnya yang dapat mencegah adanya ledakan akibat kebocoran gas LPG.
Trafik Simülasyon Yazılımlarına Genel Bakış
Authors: AKKAYA Sinem , ENGİN Taylan
Published: October 2022

Bu çalışma, günümüzde araç sayısının artmasına bağlı olarak meydana gelen trafik yoğunluğu durumunu değerlendirmek ve modellemek için kullanılan trafik simülasyon yazılımları arasından seçilen bir grup programın (SUMO, Quadstone Paramics Modeller, Aimsun, Trafficware SimTraffic, CORSIM TRAFVU, PTV Vissim) genel özelliklerini incelemek amacıyla hazırlanmıştır. Trafik bilgisayar simülasyonu, trafik ağlarının ve sistemlerinin trafik modellemesi, planlaması ve geliştirilmesi araştırmalarında yaygın olarak kullanılan bir yöntemdir. Mikroskobik, makroskopik, mezoskopik ve nanoskopik olarak dört kategoride incelenirler. Bazıları sadece bir kategori özelliğine sahipken bazıları da entegre sistemlerdir. Taşıt trafiği sistemleri ve ürettiği sorunlar, küresel olarak artan bir ilgi ve endişe kaynağı olarak öne çıkmaktadır. Karmaşık trafik sistemlerinin modellenmesi ve oluşan karmaşanın çözümü zor bir problemdir. Gerçek hayatta yapılacak denemeler pratik ve kısa zamanlı olmaz. Trafik simülasyon yazılımları uygulama açısından oldukça pratik ve daha kısa zamanlıdır. Bununla birlikte de öngörülen uygulamaların simülasyon ortamında denenmesi ve taşıt trafiğine uyarlanması çok daha çabuk olur. Dolayısıyla oluşan karmaşanın veya sıkışıklığının daha kısa zamanda çözümlenmesi hem mali açıdan hemde zamandan tasarruf sağlar.
Akıllı Otopark Sistemlerinde Kullanılan Araç Tanıma Teknolojileri Üzerine Bir İnceleme
Authors: DOĞAROĞLU Bora , ÇALIŞKANELLİ S.pelin
Published: October 2022

Günümüz şehir planlama ve yönetimi için trafik talebindeki artışın paralelinde otopark talebindeki artış acil çözüm bekleyen sorunlar arasında yer almaktadır. Özellikle otopark talebinin yüksek olduğu şehir merkezlerinde yeterli alan bulunmaması otopark altyapı tesislerinin inşasına olanak tanımamaktadır. Soruna yaygın çözüm olarak kullanılan yöntem mevcut kapasitenin etkin kullanımı ve talep yönetimidir. Akıllı ulaşım sistemleri özellikle Akıllı Otopark Sistemleri (AOS) özelinde düşünüldüğünde kapasitenin uygun kullanımı ve talep yönetimi için en güncel yöntem olarak tercih edilmektedir. AOS’un etkin uygulanabilmesi de etkin araç tanıma ve detektör teknolojilerine bağlıdır. Bu çalışmada AOS’un verimliliği için etken olan araç tanıma detektör sistemleri kategorize edilerek tanıtılmış ayrıca yaygın kullanılan detektör teknolojilerinin avantajları ve dezavantajları dikkate alınarak kıyaslamaları yapılmıştır. Sunulan sınıflandırmalar ile AOS tasarımı yapılırken kullanılacak etkin teknoloji seçimi konusunda yardımcı olmak amaçlanmıştır.
Ground solar absorption observations of total column CO, CO2, CH4, and aerosol optical depth from California’s Sequoia Lightning Complex Fire: Emission factors and modified combustion efficiency at large scales
Authors: Frausto-Vicencio Isis , Heerah Sajjan , Meyer Aaron G. , Parker Harrison A. , Dubey Manvendra , Hopkins Francesca M.
Published: October 2022

Abstract. With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory, and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on large scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols in the 2020 wildfire season of smoke plumes from the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EF) and modified combustion efficiency (MCE) for these fires, and evaluate relationships between them based on combustion phase at large scales. We demonstrate that the EM27/SUN effectively detects changes of CO, CO2 and CH4 in the atmospheric column at ~10 km scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5 ± 12.2 and EFCH4 of 4.3 ± 0.8 for a large smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR derived AOD was compared to a nearby AERONET station and observed ratios of AOD to XCO were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOMI satellite-based instrument are 9.7 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7 ± 49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.6 % of the total state CH4 emissions in 2019. Our novel application of an EM27/SUN solar spectrometer to quantify wildfire emission ratios at large scales follows predictive relationships that are consistent with in situ studies, offering promise for extensive monitoring from ground networks and satellite remote sensing.
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland
Authors: Aichinger-Rosenberger Matthias , Brockmann Elmar , Crocetti Laura , Soja Benedikt , Moeller Gregor
Published: October 2022

Abstract. Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP).
However, one of the phenomena rarely studied using GNSS are foehn winds.
Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence.
Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations.
However, detecting such signals becomes increasingly difficult for large datasets.
Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products.
This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products.
Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics.
The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups.
The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used.
Detection- and alarm-based measures reach levels between 66 %–80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP.
For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist.
However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future.
Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.
3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field
Authors: Gires Auguste , Tchiguirinskaia Ioulia , Schertzer Daniel
Published: October 2022

Abstract. Weather radars measure rainfall in altitude, whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind, which affects the location of the measured field. The governing equation of a rain drop's motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. It depends non-linearly on the instantaneous relative velocity between the drop and the local wind, which yields complex behaviour. Here, the drag force is expressed in a standard way with the help of a drag coefficient expressed as a function of the Reynolds number. Corrections accounting for the oblateness of drops greater than 1–2 mm are suggested and validated through a comparison of the retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature. An explicit numerical scheme is then implemented to solve this equation for a 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. It appears that multifractal features of the input wind are simply transferred to the drop velocity with an additional fractional integration whose level depends on the drop size, and a slight time shift. Using an actual high-resolution 3D sonic anemometer and a scale invariant approach to simulate realistic fluctuations of wind in space, trajectories of drops of various sizes falling form 1500 m are studied. For a strong wind event, drops located within a radar gate in altitude during 5 min are spread on the ground over an area of the size of a few kilometres. The spread for drops of a given diameter is found to cover a few radar pixels. Consequences on measurements of hydro-meteorological extremes that are needed to improve the resilience of urban areas are discussed.
Evaluation of the distributions of hydroxylated glycerol dibiphytanyl glycerol tetraethers (GDGTs) in Holocene Baltic Sea sediments for reconstruction of sea surface temperature: the effect of changing salinity
Authors: Sinninghe Damsté Jaap S. , Warden Lisa A. , Berg Carlo , Jürgens Klaus , Moros Matthias
Published: October 2022

Abstract. Hydroxylated glycerol dibiphytanyl glycerol tetraethers (OH-GDGTs)
produced by both marine and freshwater thaumarchaea are increasingly used
for the reconstruction of past sea surface temperature (SST). They occur
throughout the modern Baltic Sea, but it is unknown if OH-GDGTs can be used for assessing past SST in this area, where salinity has changed
considerably over the Holocene. Three commonly applied OH-GDGT proxies for
SST reconstruction, i.e., the OH-GDGT%, RI-OH, and RI-OH′ indices, were tested using a thaumarchaeotal culture enriched from the Baltic Sea grown at 4 and 22 ∘C, as well as 12 surface sediments from the Baltic Sea and the adjacent Skagerrak. In the culture experiments all three proxies showed the expected response with the rise in temperature, but their absolute values were not always in line with existing marine core-top
calibrations, especially for the OH-GDGT% index. Of the two proxies based on the distribution of OH-GDGTs, the RI-OH index shows no increase with increasing mean annual SST, whilst the RI-OH′ index shows only
a linear correlation with SST when the sediments from the Bothnian Sea and
Bothnian Bay area, with a reduced salinity and increased lateral sediment influx, are omitted from the dataset. Two sedimentary Holocene records from the Arkona and Gotland basins were studied, the latter in high resolution. In the brackish phase of the Baltic Sea (the Littorina Sea stage), the
RI-OH′ index shows a good correlation with the TEX86L, an established temperature proxy in the Baltic Sea, and can be used to identify important climatic events. However, during the preceding Ancylus Lake phase the RI-OH′ (and RI-OH) index records values that are far too high, resulting in anomalously high SST estimates. This is probably because freshwater thaumarchaea adjust their OH-GDGTs differently, as has been shown for thaumarchaea in Lake Lugano's water column. In the Littorina Sea, Ancylus Lake, and Yoldia Lake phases of the Baltic basin, the record of the RI-OH′ index thus most likely reflects changes in both temperature and salinity. Overall, our study indicates that a reduced salinity increases the values of the RI-OH′ (and RI-OH) indices substantially, and this should be considered when applying these proxies in other settings.
Seasonal to interannual variabilities of sea–air CO2 exchange across Tropical Maritime Continent indicated by eddy–permitting coupled OGCM experiment
Authors: Amri Faisal , Nakamura Takashi , Watanabe Atsushi , Kartadikaria Aditiya R. , Nadaoka Kazuo
Published: October 2022

Abstract. The lack of long–term observational data has limited research on sea–air CO2 exchange variabilities in the Tropical Maritime Continent (TMC). To address the issue, we utilized a three–dimensional high–resolution physical–biogeochemical ocean numerical model and applied it to simulate sea–air CO2 exchange in the region over the last decade (2010–2019). Some key features like atmospheric CO2 source signature and high sea surface pCO2 environment inside the TMC were captured by the model. Within the TMC, model results indicated strong CO2 degassing along the south of Java associated with the seasonal cycle of the upwelling system. Abundant supply of inorganic carbon during upwelling season and strong wind speed results in CO2 degassing that could reach as high as 30 gC m–2 year–1 around the area. In addition to the region acting as a full–year atmospheric CO2 source, the TMC also exhibited interannual modulation in both sea–air CO2 flux and sea surface pCO2 which can be related to the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Large–scale anomalous strong CO2 degassing and high sea surface pCO2 from 2015 to 2016 in response to the 2015/2016 El Niño evolution was observed and dominated by modulation within the TMC. It is further found that modulation of CO2 degassing related to IOD were confined along the west of south of Java with a higher magnitude compared with anomalies related to ENSO which shows larger spatial scale but lower in the magnitude. Study conducted here may provide insight about possible variabilities of sea–air CO2 exchange in the area that still poorly represented in many global–scale modelling and reconstruction efforts.
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2005
Authors: Li Jia-Hao , Li Zhao-Liang , Liu Xiangyang , Duan Si-Bo
Published: October 2022

Abstract. Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. In this study, we developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer (AVHRR) Level-1b Global Area Coverage (GAC) data from 1981 to 2005. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and pre-processing using open-source Python libraries; (2) cloud detection using the AVHRR-Phase I algorithm; (3) land surface emissivity estimation using an improved method considering annual land cover changes; and (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K, and nighttime LSTs were typically better than daytime LSTs. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference (RMSD) was approximately 3.2 K, a positive bias was obtained for GT-LST, and relatively large RMSDs were obtained during the daytime, spring and summer. Furthermore, we compared our newly generated dataset with a global AVHRR daytime LST product at the selected measurements of SURFRAD sites (i.e., measurements of these two satellite datasets were valid), which revealed similar accuracies for the two datasets. However, GT-LST can additionally provide nighttime LST, which can be combined with daytime observations estimating relatively accurate monthly mean LST under all-sky conditions, with RMSE of 4.1 K. Finally, we compared GT-LST with a regional twice-daily AVHRR LST product over continental Africa in different seasons, with RMSDs ranging from 2.1 to 4.3 K. Considering these advantages, the proposed dataset provides a better data source for a range of research applications. GT-LST is freely available at https://doi.org/10.5281/zenodo.7113080 (1981–2000) (Li et al., 2022a) and https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b).
LOCAL CLIMATE ZONES: LESSONS FROM SINGAPORE AND POTENTIAL IMPROVEMENT WITH STREET VIEW IMAGERY
Authors: Ignatius M. , Xu R. , Hou Y. , Liang X. , Zhao T. , Chen S. , Wong N. H. , Biljecki F.
Published: October 2022

Abstract. Urban heat island (UHI) is considered a serious environmental issue in highly urbanized cities such as Singapore. To better quantify the UHI intensity, the local climate zones (LCZ) classification scheme was adopted to characterize land covers, and describe and compare their thermal performance. There are three commonly used LCZ classification approaches: manual sampling,World Urban Database and Access Portal Tools (WUDAPT) processing method using remote sensing, and geographical information system (GIS)-based method. Based on the current implementation of WUDAPT Level 0 method in the classification work in Singapore, the principal limitations are expounded. To overcome the deficiencies, street view imagery (SVI), which carries substantial urban spatial information, is regarded as a promising data source. This paper reviews the potential of SVI to better estimate certain LCZrelated properties, such as sky view factor (SVF). As it allows a detailed view on the ground objects, SVI opens up the possibility of identifying surface properties such as albedo, as well as anthropogenic heat sources. Although it is not a novel idea, there has been a lack of a comprehensive use of SVI in assisting LCZ classification from the ground up, especially in a high-density city such as Singapore. This paper overviews potential ways to incorporate SVI and identifies challenges such as coarse temporal resolution and spatial coverage constrained to drivable roads.
A FRAMEWORK TO INTEGRATE BIM WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-BASED PROPERTY VALUATION METHODS
Authors: Jafary P. , Shojaei D. , Rajabifard A. , Ngo T.
Published: October 2022

Abstract. Property valuation is of extreme importance since variations in the real estate market enormously influence people’s life. The main goal of Automated Valuation Models (AVMs) is to calculate the market value of a large number of properties with an acceptable accuracy. The Hedonic Price Model (HPM) is the most widely used AVM for the valuation purposes. Despite its simplicity, ease of use and straightforwardness, HPM lacks the capability to address the non-linear relationships between different value-related factors. Hence, researchers have developed other state-of-the-art property valuation methods based on the advancements in computer science including Artificial Intelligence (AI), Machine Learning (ML), computer vision and deep learning. Design, development, and validation of such advanced AVMs require establishment of a database including data on the different influential factors. Two types of factors are used in the literature, including textual and visual features. Reliable data sources are required for the implementation of AVMs since the accuracy of the provided valuations is definitely linked to the reliability of the used real estate databases. Building Information Modelling (BIM) provides precise information on different components of properties. Although some scholars have tried to use BIM for property valuation, BIM benefits in different valuation procedures have not been fully investigated. Hence, this paper provides a framework that consider BIM capabilities to be integrated with different stages and processes in property valuation, especially in relation to advanced AVMs based on AI and ML.
INTEGRATED STORMWATER ANALYSIS MODEL TO SUPPORT SUSTAINABLE URBAN GREEN SPACE DESIGN
Authors: Jia J. , Zlatanova S. , Zhang K. , Liu H.
Published: October 2022

Abstract. Urban Green Space (UGS) has been broadly treated as a valuable and limited resource to handle the challenges brought by high-density urban environment. Green stormwater management has been prompt around the world. To reduce the potential conflicts between stormwater management and other requirements on UGS (especially human needs), the research community and governments encourage the urban planners and designers to integrate stormwater analysis into UGS design. However, the professional term and operation of the traditional hydrological model is a huge challenge to the designers who haven’t touched hydrological knowledge. This study developed a method to simulate and quantify stormwater in UGS by the particle system in the design platform- Rhinoceros +Grasshopper. In this method, we adopted five groups of urban objects such as terrain, building footprint etc. to ease procedures and performance. To overcome the issue the abstracted particle movement cannot reflect the infiltration features of land cover, we introduced categorising locations of the particles and the UGS retaining stormwater hypothesis. To test the feasibility of the method, we tested the three parameters of the integrated model: iteration times, rainfall depth ( selected rainfall event) and particle radius. The comparison tests prove: (1) too small iteration times would lead the particles to stop on the way to the bottom of the terrain, so we set at least 4000 iteration times for simulation; (2) the sensitivity to the selection of rainfall event and particle size is relatively low, the simulation results vary by 1%; (3) too small particle numbers will impact the accuracy of analysis, so we should balance accuracy and work efficiency of work; (4) the stormwater volume estimation based on the particle system is acceptable. The experiments confirm the method can effectively support preliminary UGS design work.
DEEP LEARNING FOR SEMANTIC 3D CITY MODEL EXTENSION: MODELING ROOF SUPERSTRUCTURES USING AERIAL IMAGES FOR SOLAR POTENTIAL ANALYSIS
Authors: Krapf S. , Willenborg B. , Knoll K. , Bruhse M. , Kolbe T. H.
Published: October 2022

Abstract. On a global scale, semantic 3D city models with Level of Detail 2 become more and more available. Automated generation of higher Level of Detail models is an active field of research, but low coverage of dense LiDAR or photogrammetric point clouds is a barrier. Therefore, this paper presents a novel approach for enriching semantic 3D city models with roof superstructures extracted from aerial images using deep learning. The method maps and classifies superstructures in 2D and subsequently transforms them to 3D. Furthermore, we examine the benefit of the enriched model for solar potential analysis. The accuracy of solar potential analysis is improved by avoiding invalid simplifications of slope, shadow and panel placement. The enriched model reduces overestimation of accumulated solar potential by around 20% compared to an analysis based on aerial images only. The novel method contributes to increasing the availability of Level of Detail 3 models for larger areas, while posing further research opportunities.
COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION
Authors: Wysocki O. , Grilli E. , Hoegner L. , Stilla U.
Published: October 2022

Abstract. Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.
SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUDS BY JOINT OPTIMIZATION OF GEOMETRIC FEATURES AND NEURAL NETWORKS
Authors: Yao M. M. , Li X. M. , Wang W. X. , Xie L. F. , Tang S. J.
Published: October 2022

Abstract. Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds and neural networks to address the problem of incomplete and inconsistent segmentation objectives in existing semantic segmentation methods. Using neural networks, semantic labels are extracted from indoor structural information as the first step. The paper proposes a probabilistic model to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. Three sets of indoor point clouds data from simple to complex indoor scenes are used to test the accuracy and validity of the segmentation method proposed in this paper. The experimental results demonstrate that the method proposed in this paper can effectively improve the semantic segmentation accuracy of indoor 3D point clouds.
LIVEABLE CITY DIGITAL TWIN: A PILOT PROJECT FOR THE CITY OF LIVERPOOL (NSW, AUSTRALIA)
Authors: Diakite A. A. , Ng L. , Barton J. , Rigby M. , Williams K. , Barr S. , Zlatanova S.
Published: October 2022

Abstract. In recent years, the concept of Digital Twin (DT) for cities is increasingly at the core of most smart city initiatives, as it has been identified as a critical tool for tackling the challenges of this century. A robust city modelling framework is essential if local, state and national governments are to move towards sustainable built environments and work together across complex multi-sectoral problems to drive impacts that improve urban liveability and climate adaptability. Furthermore, the level of collaboration and interoperability required to address these cannot be achieved without proper standardisation of DT components. The aim of this project is to develop a demonstration DT that integrates existing data using a standardised 3D format based on CityGML and that embeds analytics, such as sun exposure and tree coverage, to assess liveability within a 3D city modelling framework. Common urban features such as buildings, roads, railways, vegetation and water bodies are also processed and incorporated. Additionally, IoT sensors are integrated into the model and all processes are performed using open-source tools to improve accessibility and repeatability. Details of the workflow, including the storage of the city features in a 3D City Database (3DCityDB), the 3D upgrading of urban features commonly available as 2D data as well as a few use cases are illustrated and discussed in this paper.
AN EXPLORATION OF INTERACTIONS BETWEEN URBAN HERITAGES AND TOURIST’S DIGITAL FOOTPRINT: NETWORK AND TEXTUAL ANALYSIS VIA GEOTAGGED FLICKR DATA IN AMSTERDAM
Authors: Karayazi S. S. , Dane G. , Arentze T.
Published: October 2022

Abstract. Spatial information retrieved by geotagged social media data allows the analysis of when and where questions and supports a better understanding of people’s spatial interaction. The purpose of this paper is to reveal alternative heritage spots for reducing tourism pressure in certain areas within Amsterdam’s historical core by presenting tourists’ points of interests (POIs), their patterns between POIs, and the sentiments attached to these POIs. To fulfil the research aim, network analysis, textual analysis, and heat maps were conducted with geotagged Flickr data representing tourists’ uploaded images. The results show that the Flickr data set was useful to reveal the existing POIs and their relations. Moreover, the further analysis that is based on the intersection between the revealed spatial patterns and Amsterdam heritage sites showed alternative POIs nearby current POIs. Based on the results, we demonstrated how a dataset constructed from geotagged Flickr data can provide useful practical information for sustainable tourism development. Our research has the potential to support urban heritage and tourism researchers and policy makers with a better understanding of POIs, their relations and possible other POIs. The outcomes of this research can advise heritage tourists on alternative POIs to visit, and policy makers to plan alternative POIs.
PREDICTING OF URBAN EXPANSION USING CONVOLUTIONAL LSTM NETWORK MODEL: THE CASE OF SEOUL METROPOLITAN AREA, KOREA
Authors: Kim J. M. , Park J. S. , Lee C. Y. , Lee S. G.
Published: October 2022

Abstract. As urbanization progresses, many studies about the analysis and prediction of land-use change and urban sprawl have been conducted recently. As the sprawl phenomenon progresses rapidly, the urban expansion phenomenon became uncontrolled and it has affected negatively on the city's environment and transportation finally. So, it is essential to identify lands likely to be urbanized in the future because it aids in establishing land use plans and policies pre-acting the negative impact of spatially urban expansion the sprawl by determining factors affecting the urban sprawl. Previous studies based on statistical models are limited to identifying determining factors, so the prediction performance is low compared to deep learning. On the other hand, existing studies using machine learning and deep learning overlook selecting specific region-focused variables. Therefore, this study aims to analyze and predict changes in the Seoul Metropolitan Area's sprawl in Korea using the Convolutional Long Short-Term Memory Network (ConvLSTM) with factors at the city scale and neighboring factors at the local scale in the Seoul Metropolitan Area (SMA). ConvLSTM is a type of combination model: combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN). This study showed that ConvLSTM with factors at the city and neighboring factors at the local scale predicted the urbanized land. The determinants contain population and roads ratio at the city scale, and neighboring urban lands, distance to the nearest subway stations, slope, and elevation at the local scale. The results reveal that predicted urban lands in 2020 increase over the entire region. In particular, the expected urban lands in 2020 increase by reducing farmlands in the southern part of the SMA. It is consistent with the trend of urbanized lands from 1980 to 2010. In addition, urbanization occurs in areas adjacent to Seoul due to the well-established urban infrastructure. The results of this study can be used as evidence to establish sustainable land use plans and regulations in the future.
ROBUST AND SCALABLE REAL-TIME VEHICLE CLASSIFICATION AND TRACKING: A CASE STUDY OF THAILAND
Authors: Neupane B. , Horanont T. , Pattarapongsin P. , Thapa A.
Published: October 2022

Abstract. An accurate detection, classification, and tracking of vehicles are highly important for intelligent transport systems (ITS) and road maintenance. In recent years, the deep learning (DL)-based approach is highly regarded for real-time vehicle classification from surveillance cameras. However, the practical implementation of such an approach is affected by the adverse lighting conditions and positioning of the cameras. In this research, we develop a DL-based method for near real-time multi-vehicle counting, classifying, and tracking on individual lanes of the road. First, we train a DL network of the You Only Look Once (YOLO) family on a custom dataset that we have curated. The dataset consists of nearly 30000 training samples to classify the vehicles into seven classes, which is more than in the existing benchmark datasets. Second, we fine-tune the trained model into another small dataset collected from the surveillance cameras that are used during the implementation process. Third, we connect the trained model to a tracking algorithm that we have developed to produce a per-lane report with the calculation of the speed and mobility of the vehicles. We test the robustness of the system on different faces of the vehicles and in adverse lighting conditions. The overall accuracy (OA) of classification ranges from 91% to 99% in four faces of vehicles (back, front, driver side, and passenger side). Similarly, in an experiment on adverse lighting conditions, OA of 93.7% and 99.6% is observed in a noisy and clear lighting conditions respectively. The implications of these results will assist in road maintenance with spatial information management and sensing for intelligent transport planning.
WHY ARE PEOPLE STILL NOT WALKING? THE NEED FOR A MICRO-SCALED MULTI-CRITERIA SPATIO-TEMPORAL DESIGN APPROACH TO IMPROVE WALK-QUALITY
Authors: White M. , Huang X. , Langenheim N. , Yang T. , Schofield R. , Young M. , Livesley S. J. , Seneviratne S. , Stevenson M.
Published: October 2022

Abstract. Walking is essential for the health and well-being of communities as well as meeting environmental challenges of the 21st Century. There have been significant research contributions in recent decades to understanding factors that can influence the likelihood of citizens choosing active travel modes, with great advances in understanding the built environment, particularly macro-scaled urban factors such as land-use mix, population density, and street patterns. So why are people still not walking? And is there anything we can do to try to change this? To answer these questions, this paper explores walking environment assessment approaches using a trans-disciplinary narrative approach, touching on multiple critical disciplinary knowledge areas. We find that while much of the research into the built environment’s influence on walking has made positive contributions in helping identify areas of cities that discourage active travel, the capacity to improve these areas has been limited. While macro-scaled factors commonly considered in ‘walkability’ analysis are highly influential to mode choice, unless working on green-field development, these factors may require re-zoning private land, substantial demolition and renewal, and may be financially or politically infeasible and thus extremely difficult to change. There is a need to augment macro-scaled analysis with micro-scaled tools, and to establish multi-criterion spatio-temporal design decision support approaches drawing from emerging technologies to make informed, integrated and effective urban design changes. These methods applied at precinct and streetscape levels have the potential to make feasible improvements to the ‘walk-quality’ of the built environment and contribute to facilitating active travel.
SMART PARKING MANAGEMENT FOR ELECTRICAL VEHICLES: SOLAR PARKING LOTS
Authors: Bokhari A. S.
Published: October 2022

Abstract. Urban population growth, which is associated with a growing prevalence of private car usage, contributes to various contemporary urban issues, including air pollution and fuel costs. The shift of the transportation industry from internal combustion engines to electric vehicles is internationally recognised as an option for addressing these problems and making progress towards more sustainable transport. Makers of electric vehicles have considered many needs of their customers, including locating suitable parking spaces and charging stations. Currently, charging locations and parking spaces can be found with the help of methods based on human intelligence. However, the development of solar parking lots using smart management system is a revolutionary step in this regard, as it provides an optimal solution to internet of things-based (IoT-Based) parking spaces and locating a charging station for electric vehicles. This paper outlines the concept of Eco-parking platforms and discusses their potential implementation in cities. The outcome of this research can inform urban planners and entrepreneurs about how to implement this project and achieve sustainable transport.
A TWO-STAGE SIMULATION APPROACH OF URBAN TRANSPORT EMISSION EVALUATION TOWARDS CARBON PEAK: A CASE STUDY IN SUZHOU, CHINA
Authors: Xu Y. , Ma X. , Pan M. , Jiang K.
Published: October 2022

Abstract. The ever-increasing automotive travel demand is a major source of urban carbon emissions. Therefore, it could be an effective way for local governments to achieve carbon peak by optimizing facility distribution and transport management strategies, that results in lower automotive demand. This study adopts a two-stage approach to evaluate the carbon emission performance of a representative Chinese megacity, i.e. Suzhou. The first stage is to predict the carbon peak through multi-scenario sensitivity analysis with respect to three essential factors for the whole city. The second stage is to estimate the link-based carbon emissions with given traffic flow and vehicle operating mode indicators during the peak hour, so as to locate urban areas and/or facilities with higher emission intensity. Then a correlation analysis is further conducted to explore the possible connections between the built environment factors and transport-related carbon emissions.
COMPARING STREET VIEW IMAGERY AND AERIAL PERSPECTIVES IN THE BUILT ENVIRONMENT
Authors: Gaw L. Y. , Chen S. , Chow Y. S. , Lee K. , Biljecki F.
Published: October 2022

Abstract. Street view imagery (SVI) has gained prominence in the past decade, offering a new perspective to map and understand cities. It supports numerous studies in the built environment, by replacing or supplementing aerial and satellite imagery, where some studies have not yet been possible with traditional platforms and have now been enabled for the first time thanks to the increasing volume of SVI data. However, the two perspectives are often disconnected and there has not been an overarching paper to discuss the pros and cons of each. We provide an overview outlining and discussing the role of SVI in GIS and urban studies spanning six use cases. Our discourse is supported by a systematic literature review of more than 100 papers and our own experiments that reveal the added value and challenges of SVI in extracting information on buildings and other urban features, an increasingly important use case. We find that the key advantages of SVI over aerial imagery are that it represents more closely how streetscapes are perceived by people and that it enables extracting certain information that otherwise cannot be gathered from top-down perspectives. However, the spatial coverage of SVI tends to be limited to the vicinity of driveable roads, and its temporal coverage is comparatively sparse.
AN INNOVATIVE TOOL FOR OPTIMISED DEVELOPMENT ENVELOPE CONTROL (DEC) ANALYSIS AND SCENARIO BUILDING IN DIGITAL TWIN
Authors: Sabri S. , Chen Y. , Lim D. , Rajabifard A. , Zhang Y.
Published: October 2022

Abstract. Despite the advancements in Planning Support Systems (PSS), Geographical Information Systems (GIS), and multi-dimensional building and city modelling, we found that they are not within the factual urban planning and design practice and often do not generate expected knowledge and wisdom. In addition, there is limited application of computational generated data and visualisation in the current statutory and strategic planning processes. For example, the planning and design rules and guidelines for a compact city development need to be examined and communicated to the professionals and the community to ensure that they are fit for purpose and address the users requirements. However, the planning rules are communicated through texts and diagrams, which are very difficult to understand and there is a risk of misinterpretation, uncertainty, and dissatisfaction in urban development processes. This research leverages the emerging technologies such as Digital Twin to develop a scenario-based PSS to consider the planning controls protecting the public’s interest while optimising economic and spatial yield.
A CASE STUDY OF 3D OWNERSHIP RIGHTS REGISTRATION AS A BASIS OF SMART URBAN GOVERNANCE
Authors: Tomic H. , Vucic N. , Vranic S. , Mastelic Ivic S. , Paar R. , Šantek D.
Published: October 2022

Abstract. The Land Administration System (LAS) is of strategic importance to any country. Effective modern LAS should facilitate land management as an important instrument for the implementation of land policy contributing to comprehensive and sustainable land governance. The infrastructure needed to support increasingly complex requirements or urban space management based on three-dimensional register of rights would bring substantial improvement, especially for management of complex 3D situations. Modern cities are increasingly deploying different types of infrastructure under/on/above ground. The Republic of Croatia decided to establish the Register of Buildings, a transitional register combining data about buildings from existing registers towards the 3D cadastre. The transformation from 2D to 3D LAS should follow international standards, firstly the Land Administration Domain Model (LADM): ISO 19152:2012 but also other ISO and OGC standards. This study analyses the possible re-use of cadastral 3D datasets in smart urban governance procedures, the case of public purpose property valuation. To our opinion the establishment of a 3D LAS should facilitate and enable efficient and sustainable management of urban spaces.The prototype of 3D LAS, based on LADM conceptual model and its extensions, should enable efficient integration of 3D ownership rights register with the data from other domains - other key registers, taxation, and valuation data (property market data) and other sources of linked data. In this paper, the case study is used to assess the current practice of registration of buildings and their particular parts and analyse the differences between different implementation of 3D partitioned parts.
EFFICIENT SPARSE STREET FURNITURE EXTRACTION FROM MOBILE LASER SCANNING POINT CLOUDS
Authors: Truong-Hong L. , Lindenbergh R. C. , Vermeij M. J.
Published: October 2022

Abstract. Current mobile systems are capable of efficiently acquiring dense urban point clouds. Still, operational use of such data is hampered by the lack of efficient object extraction methodology. Notably methodology is lacking for automatically extracting objects that do not belong to the road furniture like street signs and light poles but do belong to the street furniture. As an example, object we consider public garbage bins, that are installed and should be maintained in public areas in every city. However, information about types, locations, and condition of these public garbage bins are rarely updated and only obtained through manual measurements. Therefore, an efficient way of collecting information on such public objects is of interest not only for urban management but also when developing digital twins of a city. This study proposes a new method to automatically extract public garbage bins from large urban mobile laser scanning (MLS) point clouds. The proposed method consists of three main steps: (1) cell-, (2) sub-cell-, and (3) surface-based filtering, in which both spatial information of the point clouds and contextual knowledge of the public garbage bins are incorporated to efficiently remove irrelevant 3D points at an early phase and identify and classify different types of public garbage bins. Contextual knowledge includes shape and dimensions, and the relationship between the public garbage bins and the ground surface. A MLS dataset of the city centre of Rotterdam, the Netherlands, consisting of 2.84 billion points organised in 166 tiles of 50 × 75m, and covering an area of about 750 × 750m was used to test the proposed method. Results show that the method can automatically extract ~90 public garbage bins with an overall detection rate of 89.1%. Moreover, the executing time for the entire dataset was only about 163.6 minutes, which is equivalent to 3.46 seconds per one million points. Although the method was tested here one public garbage bins, it can be easily tuned for the detection of other street furniture objects, like benches, post boxes or bollards.