List of scientific publications for bark beetle damage assessment developed using the datasets of this repository in fulfillment of the research objectives of the SWIFTT Project (GA 101082732)
Complete list of publications and citations related to the SWIFTT project developed for Bark Beetle Detection Semantic Segmentation.
Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory
@inproceedings{DBLP:conf/sac/AndresiniAM24,
author = {Giuseppina Andresini and
Annalisa Appice and
Donato Malerba},
editor = {Jiman Hong and
Juw Won Park},
title = {Leveraging Sentinel-2 time series for bark beetle-induced forest dieback
inventory},
booktitle = {Proceedings of the 39th {ACM/SIGAPP} Symposium on Applied Computing,
{SAC} 2024, Avila, Spain, April 8-12, 2024},
pages = {875--882},
publisher = {{ACM}},
year = {2024},
doi = {10.1145/3605098.3635908}
}
A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data
@article{DBLP:journals/staeors/AndresiniAM24,
author = {Giuseppina Andresini and
Annalisa Appice and
Donato Malerba},
title = {A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in
Sentinel-2 Data},
journal = {{IEEE} Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {17},
pages = {17075--17086},
year = {2024},
doi = {10.1109/JSTARS.2024.3460981}
}
Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images
@inproceedings{DBLP:conf/igarss/AndresiniAIMR24,
author = {Giuseppina Andresini and
Annalisa Appice and
Dino Ienco and
Donato Malerba and
Vito Recchia},
title = {Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback
Due to Bark Beetle Hotspots in Sentinel-2 Images},
booktitle = {{IGARSS} 2024 - 2024 {IEEE} International Geoscience and Remote Sensing
Symposium, Athens, Greece, July 7-12, 2024},
pages = {5227--5230},
publisher = {{IEEE}},
year = {2024},
doi = {10.1109/IGARSS53475.2024.10641479}
}
An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images
@inproceedings{DBLP:conf/dis/RecchiaAAFM24,
author = {Vito Recchia and
Giuseppina Andresini and
Annalisa Appice and
Gianpietro Fontana and
Donato Malerba},
editor = {Dino Pedreschi and
Anna Monreale and
Riccardo Guidotti and
Roberto Pellungrini and
Francesca Naretto},
title = {An Attention-Based {CNN} Approach to Detect Forest Tree Dieback Caused
by Insect Outbreak in Sentinel-2 Images},
booktitle = {Discovery Science - 27th International Conference, {DS} 2024, Pisa,
Italy, October 14-16, 2024, Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
pages = {183--199},
publisher = {Springer},
year = {2024},
doi = {10.1007/978-3-031-78980-9\_12}
}
DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images
@article{DBLP:journals/jiis/AndresiniAIR24,
author = {Giuseppina Andresini and
Annalisa Appice and
Dino Ienco and
Vito Recchia},
title = {{DIAMANTE:} {A} data-centric semantic segmentation approach to map
tree dieback induced by bark beetle infestations via satellite images},
journal = {Journal of Intelligent Information Systems},
volume = {62},
number = {6},
pages = {1531--1558},
year = {2024},
doi = {10.1007/s10844-024-00877-6}
}
Deep Change Vector Analysis to Map Bark Beetle Outbreaks in Open Sentinel-2 Data
@inproceedings{DBLP:conf/ijcnn/AndresiniAMR25,
author = {Giuseppina Andresini and
Annalisa Appice and
Donato Malerba and
Vito Recchia},
title = {Deep Change Vector Analysis to Map Bark Beetle Outbreaks in Open Sentinel-2
Data},
booktitle = {International Joint Conference on Neural Networks, {IJCNN} 2025, Rome,
Italy, June 30 - July 5, 2025},
pages = {1--8},
publisher = {{IEEE}},
year = {2025},
doi = {10.1109/IJCNN64981.2025.11229013}
}
GANDALF: A LLM-based approach to map bark beetle outbreaks in semantic stories of Sentinel-2 images
@inproceedings{DBLP:conf/sac/Pasquadibisceglie25,
author = {Vincenzo Pasquadibisceglie and
Vito Recchia and
Annalisa Appice and
Donato Malerba and
Giuseppe Fiameni},
editor = {Jiman Hong and
Sebastiano Battiato and
Christian Esposito and
Juw Won Park and
Adam Przybylek},
title = {{GANDALF:} {A} LLM-based approach to map bark beetle outbreaks in
semantic stories of Sentinel-2 images},
booktitle = {Proceedings of the 40th {ACM/SIGAPP} Symposium on Applied Computing,
{SAC} 2025, Catania International Airport, Catania, Italy, 31 March
2025 - 4 April 2025},
pages = {1074--1081},
publisher = {{ACM}},
year = {2025},
doi = {10.1145/3672608.3707751}
}
ULISSE: Parameter-efficient adaptation of earth vision models to monitor forest disturbance in sentinel-2 time series
@article{DBLP:journals/ecoi/RecchiaAAIFM26,
author = {Vito Recchia and
Giuseppina Andresini and
Annalisa Appice and
Dino Ienco and
Giuseppe Fiameni and
Donato Malerba},
title = {{ULISSE:} Parameter-efficient adaptation of earth vision models to
monitor forest disturbance in sentinel-2 time series},
journal = {Ecology and Informatics},
volume = {94},
pages = {103668},
year = {2026},
doi = {10.1016/j.ecoinf.2026.103668}
}
Reusing Pre-trained Semantic Segmentation Models to Map Bark Beetle Outbreaks in Sentinel-2 Images
@InProceedings{10.1007/978-3-032-25311-8_4,
author="Andresini, Giuseppina
and Appice, Annalisa
and Ardimento, Pasquale
and Boffoli, Nicola
and Carlucci, Mauro
and Recchia, Vito",
editor="Cerrato, Mattia
and Kalinauskait{\.{e}}, Danguol{\.{e}}
and Luko{\v{s}}evi{\v{c}}ius, Mantas
and Pechenizkiy, Mykola
and {\v{S}}utien{\.{e}}, Kristina",
title="Reusing Pre-trained Semantic Segmentation Models to Map Bark Beetle Outbreaks in Sentinel-2 Images",
booktitle="Machine Learning and Principles and Practice of Knowledge Discovery in Databases",
year="2026",
publisher="Springer Nature Switzerland",
address="Cham",
pages="45--59",
abstract="The use of supervised deep learning techniques to monitor the health of forests through the analysis of satellite data is rapidly increasing. However, the key challenge with supervised deep learning techniques is that they require big volumes of accurate, error-free annotations to boost accurate model development. Although, several Earth observation satellite datasets are today available free of charge, the fieldwork for collecting their accurate annotations is time-consuming and costly. On the other hand, the emerging Data-Centric Artificial Intelligence (DCAI) paradigm promises to mitigate this issue saving time and money, while gaining accuracy promoting the reuse of foundation semantic segmentation models to specific Earth observation problems. In particular, this study addresses the task of mapping bark beetle outbreaks causing forest tree dieback through the lens of the model reuse in the DCAI paradigm. To this aim, we explore the performance of fine-tuning as a learning strategy to reuse a pre-trained, sophisticated semantic segmentation model developed for land cover segmentation with a big amount of accurately annotated multi-temporal Sentinel-2 data. We assess the effectiveness of the model reuse approach in two case studies regarding forest scenes that were annotated with bark beetle outbreaks observed in October 2028 in the Northeast of France and September 2020 in the Czech Republic.",
isbn="978-3-032-25311-8"
}
Reusing a BigEarthNet Deep Model to Map Bark Beetle Outbreaks in Sentinel-2 Forest Images
@InProceedings{10.1007/978-3-032-19096-3_33,
author="Recchia, Vito
and Andresini, Giuseppina
and Ardito, Luca
and Appice, Annalisa",
editor="Koprinska, Irena
and Mendes-Moreira, Jo{\~a}o
and Branco, Paula",
title="Reusing a BigEarthNet Deep Model to Map Bark Beetle Outbreaks in Sentinel-2 Forest Images",
booktitle="Machine Learning and Principles and Practice of Knowledge Discovery in Databases",
year="2026",
publisher="Springer Nature Switzerland",
address="Cham",
pages="493--508",
abstract="Reusing complex deep neural models trained by leveraging a big amount of annotated data and computation resources is one of the major challenges recently addressed with the emerging Data-Centric Artificial Intelligence paradigm, to pave the way for the effective development of a Green Artificial Intelligence technology. In this paper, we consider the foundation ResNet50 model as it is pre-trained for Sentinel-2 land cover image classification in BigEarthNet. We reuse this pre-trained model as backbone of deep neural models developed for semantic segmentation, pixel image classification, and CVA in the down-stream task of mapping bark beetle outbreaks in Sentinel-2 images of forest areas. The evaluation study explores the effectiveness of the considered solutions to reuse a foundation deep model in a case study regarding forest scenes that are annotated with bark beetle outbreaks observed in September 2020 in the Czech Republic.",
isbn="978-3-032-19096-3"
}