cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10848081
title: APEIRON-IND4

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.15468/dl.6hk4eq
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lagerstroemia ×amabilis Makino"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041822-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3dwytm
title: Occurrence Download

description: A dataset containing 490 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leontopodium jacotianum Beauverd"
 ]
}

 The dataset includes 490 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041823-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10848156
title: APEIRON-OF1

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10848157
title: APEIRON-OF1

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10848320
title: APEIRON-OF4

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10848321
title: APEIRON-OF4

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.15468/dl.kpfvjw
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lathyrus hallersteinii Baumg."
 ]
}

 The dataset includes 148 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041824-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10848289
title: APEIRON-OF3

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10848290
title: APEIRON-OF3

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.15468/dl.jufadb
title: Occurrence Download

description: A dataset containing 335 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lagerstroemia calyculata Kurz"
 ]
}

 The dataset includes 335 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041825-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10848015
title: APEIRON-IND3

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10848016
title: APEIRON-IND3

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}
DOI: 10.5281/zenodo.10846013
title: APEIRON-IND2

description: This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments.

For more data and details visit: APEIRON (c3lab.github.io)

 

If you use this dataset in an academic context, please cite the following work:



@inproceedings{10.1145/3625468.3652186,    author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},    title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data},    year = {2024},    publisher = {Association for Computing Machinery},    url = {https://doi.org/10.1145/3625468.3652186},    doi = {10.1145/3625468.3652186},    abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/},    booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},    keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera},    location = {Bari, Italy},    series = {MMSys '24}}