A

Abdulmuttalib, H. M.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Agugiaro, G.

• Semantic 3D city models as support for urban flood resilience: experiences from Rotterdam, pages 5–12

Ali, A. A. A. A.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Andriessen, L.

• Semantic 3D city models as support for urban flood resilience: experiences from Rotterdam, pages 5–12

B

Baira, M.

• Simulation of Urban Density Scenario according to the Cadastral Map using K-Means unsupervised classification, pages 57–63

Ballouch, Z.

• Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds, pages 13–20

Balta, E.

• Institutional and spatial constraints on locating VoloPorts in Greek metropolitan areas, pages 177–183

Banerjee, S.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Betz, M.

• Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API, pages 209–216

Billen, R.

• Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds, pages 13–20

Biswas, R.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Borgers, A.

• Semantic 3D city models as support for urban flood resilience: experiences from Rotterdam, pages 5–12

C

Carnot, M. L.

• Enhancing Roadway Safety: Lidar-Based Tree-Clearance Analysis, pages 21–28

Chakraborty, A.

• Modelling multi-density urban expansion using Cellular Automata for Brussels Metropolitan Development Area, pages 29–34

Chanfray, D.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114

Coors, V.

• Preface, pages 1–4
• A Multi-Flow Visual Analytics Web Platform for Detecting High Wind Energy Potential in Urban Environments, pages 89–97
• A Concept for 3D Geological and Urban Subsurface Modeling with a Unified Voxel Model Examined by a Case Study for the City Center of Stuttgart (Baden-Württemberg), Germany, pages 193–200
• Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API, pages 209–216

D

Dakov, V.

• Urban Tree Detection from Remote Sensing Data based on DeepForest Model, pages 35–41

Daras, P.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Daverona, A. C.

• Smart Cities: From Theory to Reality - The Athens Case, pages 153–160

Duplančić Leder, T.

• Local Climate Zoning Interaction on Land Surface Temperature Determination - City of Split Case Study, pages 43–48

E

Eckhardt, J.-D.

• A Concept for 3D Geological and Urban Subsurface Modeling with a Unified Voxel Model Examined by a Case Study for the City Center of Stuttgart (Baden-Württemberg), Germany, pages 193–200

Ellul, C.

• Preface, pages 1–4
• (m)App my data! Developing a Map-ability Rating and App to Rapidly Communicate Data Quality and Interoperability Potential of Open Data, pages 49–56

Eslahi, M.

• Simulation of Urban Density Scenario according to the Cadastral Map using K-Means unsupervised classification, pages 57–63

F

Franczyk, B.

• Enhancing Roadway Safety: Lidar-Based Tree-Clearance Analysis, pages 21–28
• Machine Learning Based Mobile Capacity Estimation for Roadside Parking, pages 99–106

G

Gesquiere, G.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114

Gesquière, G.

• Towards an Automated Transformation of an nD Urban Data Model to a Computational Ontology Network: From UML to OWL, From CityGML 3.0 to “CityOWL”, pages 231–238

Gourgourini, M.

• Smart Cities, G.I.S. and the Phenomenon of Piracy, pages 65–71
• Smart Cities: From Theory to Reality - The Athens Case, pages 153–160

Grammalidis, N.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

H

Ha, L. H.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Hajji, R.

• Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds, pages 13–20

Hattula, E.

• Building extraction in urban and rural areas with aerial and LiDAR DSM, pages 73–79
• Study on the effect of color space in deep multitask learning neural networks for road segmentation, pages 201–208

Hurka, J.

• Centralised monitoring and control of buildings using open standards, pages 169–176

I

Ibrahim, H. F. S.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Inazawa, T.

• A Novel Algorithm to Estimate Solar Irradiance of Urban Buildings for Photovoltaic Potential Estimation System Using a 3D City Model, pages 115–122
• Estimation of Photovoltaic Potential of Urban Buildings Considering a Solar Panel Arrangement Using a 3D City Model, pages 123–130

J

Jeddoub, I.

• Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds, pages 13–20

K

Karanikolas, N. N.

• Preface, pages 1–4

Karteris, A.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Kasprzyk, J.-P.

• Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds, pages 13–20

Kelesakis, D.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Kishimoto, M.

• Spatiotemporal Interpolation Method for Population Flow Data in Urban Areas, pages 145–152

Koch, S.

• Centralised monitoring and control of buildings using open standards, pages 169–176

Kolkos, G.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Koukofikis, A.

• A Multi-Flow Visual Analytics Web Platform for Detecting High Wind Energy Potential in Urban Environments, pages 89–97

Kramm, A.

• Machine Learning Based Mobile Capacity Estimation for Roadside Parking, pages 99–106

L

Leder, K.

• Local Climate Zoning Interaction on Land Surface Temperature Determination - City of Split Case Study, pages 43–48

Leder, N.

• Local Climate Zoning Interaction on Land Surface Temperature Determination - City of Split Case Study, pages 43–48

Livebardon, M.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114

Logg, A.

• The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges, pages 131–136

Ludwig, A.

• Machine Learning Based Mobile Capacity Estimation for Roadside Parking, pages 99–106

M

El Meouche, R.

• Simulation of Urban Density Scenario according to the Cadastral Map using K-Means unsupervised classification, pages 57–63

Machado, V.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114

Marinov, I.

• Volunteered Citizen Contribution to Smart Cities, pages 217–222

Marthoglou, K.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Matsuoka, R.

• A Novel Algorithm to Estimate Solar Irradiance of Urban Buildings for Photovoltaic Potential Estimation System Using a 3D City Model, pages 115–122
• Estimation of Photovoltaic Potential of Urban Buildings Considering a Solar Panel Arrangement Using a 3D City Model, pages 123–130

Mickrenska, C.

• Volunteered Citizen Contribution to Smart Cities, pages 217–222

Middelberg, J.

• Centralised monitoring and control of buildings using open standards, pages 169–176

Mitkov, R.

• The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges, pages 131–136

Mittal, A. S.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Moussas, V.

• Smart Cities: From Theory to Reality - The Athens Case, pages 153–160

Mustafa, A.

• Modelling multi-density urban expansion using Cellular Automata for Brussels Metropolitan Development Area, pages 29–34

N

Nan, X.

• Spatiotemporal Interpolation Method for Population Flow Data in Urban Areas, pages 145–152

Naserentin, V.

• The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges, pages 131–136

Nuaimi, M. A. A.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Nurunnabi, A.

• Airborne Cross-Source Point Clouds Fusion by Slice-to-Slice Adjustment, pages 161–168

O

Osaragi, T.

• Spatiotemporal Interpolation Method for Population Flow Data in Urban Areas, pages 145–152

P

Padsala, R.

• Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API, pages 209–216

Pantazis, D. N.

• Preface, pages 1–4
• Smart Cities: From Theory to Reality - The Athens Case, pages 153–160

Pantusheva, M.

• The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges, pages 131–136

Parvaz, S.

• Airborne Cross-Source Point Clouds Fusion by Slice-to-Slice Adjustment, pages 161–168

Paulau, P.

• Centralised monitoring and control of buildings using open standards, pages 169–176

Pauwels, P.

• Semantic 3D city models as support for urban flood resilience: experiences from Rotterdam, pages 5–12

Perperidou, D. G.

• Institutional and spatial constraints on locating VoloPorts in Greek metropolitan areas, pages 177–183

Petridis, C.

• Detecting Hull Fouling using Machine Learning Algorithms trained on Ship Propulsion Data to Improve Resource Management and Increase Environmental Benefits, pages 185–192

Petrova-Antonova, D.

• Urban Tree Detection from Remote Sensing Data based on DeepForest Model, pages 35–41
• The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges, pages 131–136

Peukert, E.

• Enhancing Roadway Safety: Lidar-Based Tree-Clearance Analysis, pages 21–28
• Machine Learning Based Mobile Capacity Estimation for Roadside Parking, pages 99–106

Pusacker, K.

• A Concept for 3D Geological and Urban Subsurface Modeling with a Unified Voxel Model Examined by a Case Study for the City Center of Stuttgart (Baden-Württemberg), Germany, pages 193–200

R

Raninen, J.

• Building extraction in urban and rural areas with aerial and LiDAR DSM, pages 73–79
• Study on the effect of color space in deep multitask learning neural networks for road segmentation, pages 201–208

Reynolds, P.

• (m)App my data! Developing a Map-ability Rating and App to Rapidly Communicate Data Quality and Interoperability Potential of Open Data, pages 49–56

Rupf, I.

• A Concept for 3D Geological and Urban Subsurface Modeling with a Unified Voxel Model Examined by a Case Study for the City Center of Stuttgart (Baden-Württemberg), Germany, pages 193–200

S

Salem, E. H.

• Deep Learning-Based Assessment of Urban and Vegetation Changes Using High-Resolution Khalifasat Satellite Imagery over Dubai, pages 137–143

Samuel, J.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114
• Towards an Automated Transformation of an nD Urban Data Model to a Computational Ontology Network: From UML to OWL, From CityGML 3.0 to “CityOWL”, pages 231–238

Santhanavanich, T.

• Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API, pages 209–216

Servigne, S.

• Towards an Automated Transformation of an nD Urban Data Model to a Computational Ontology Network: From UML to OWL, From CityGML 3.0 to “CityOWL”, pages 231–238

Simeonova, G.

• Volunteered Citizen Contribution to Smart Cities, pages 217–222

Sogo, S.

• A Novel Algorithm to Estimate Solar Irradiance of Urban Buildings for Photovoltaic Potential Estimation System Using a 3D City Model, pages 115–122
• Estimation of Photovoltaic Potential of Urban Buildings Considering a Solar Panel Arrangement Using a 3D City Model, pages 123–130

Stergiadou, A.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

T

Takahashi, G.

• A Novel Algorithm to Estimate Solar Irradiance of Urban Buildings for Photovoltaic Potential Estimation System Using a 3D City Model, pages 115–122
• Estimation of Photovoltaic Potential of Urban Buildings Considering a Solar Panel Arrangement Using a 3D City Model, pages 123–130

Takemoto, T.

• A Novel Algorithm to Estimate Solar Irradiance of Urban Buildings for Photovoltaic Potential Estimation System Using a 3D City Model, pages 115–122
• Estimation of Photovoltaic Potential of Urban Buildings Considering a Solar Panel Arrangement Using a 3D City Model, pages 123–130

Teferle, F.

• Airborne Cross-Source Point Clouds Fusion by Slice-to-Slice Adjustment, pages 161–168

Teller, J.

• Modelling multi-density urban expansion using Cellular Automata for Brussels Metropolitan Development Area, pages 29–34

Tokmaktsi, E.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Toussaint, J.-Y.

• IMUV: A Digital Twin for Mediation to Discover and Exchange on Territories, pages 107–114

Tsiros, E.

• Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation, pages 81–88

Tsotra, A.

• Critical Comparative Analysis of UAS Legislative Developments, pages 223–230

V

Vassilakopoulos, M.

• Preface, pages 1–4
• Detecting Hull Fouling using Machine Learning Algorithms trained on Ship Propulsion Data to Improve Resource Management and Increase Environmental Benefits, pages 185–192

Vilardo, L.

• (m)App my data! Developing a Map-ability Rating and App to Rapidly Communicate Data Quality and Interoperability Potential of Open Data, pages 49–56

Vinasco-Alvarez, D.

• Towards an Automated Transformation of an nD Urban Data Model to a Computational Ontology Network: From UML to OWL, From CityGML 3.0 to “CityOWL”, pages 231–238

Voyiatzis, I.

• Preface, pages 1–4

Z

Zhu, L.

• Building extraction in urban and rural areas with aerial and LiDAR DSM, pages 73–79
• Study on the effect of color space in deep multitask learning neural networks for road segmentation, pages 201–208