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pro vyhledávání: '"Nemni, Edoardo"'
Image generation and image completion are rapidly evolving fields, thanks to machine learning algorithms that are able to realistically replace missing pixels. However, generating large high resolution images, with a large level of details, presents
Externí odkaz:
http://arxiv.org/abs/2211.15303
Autor:
Boehm, Vanessa, Leong, Wei Ji, Mahesh, Ragini Bal, Prapas, Ioannis, Nemni, Edoardo, Kalaitzis, Freddie, Ganju, Siddha, Ramos-Pollán, Raul
This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data annotated
Externí odkaz:
http://arxiv.org/abs/2211.10338
Autor:
Böhm, Vanessa, Leong, Wei Ji, Mahesh, Ragini Bal, Prapas, Ioannis, Nemni, Edoardo, Kalaitzis, Freddie, Ganju, Siddha, Ramos-Pollan, Raul
Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides. Synt
Externí odkaz:
http://arxiv.org/abs/2211.09927
Autor:
Boehm, Vanessa, Leong, Wei Ji, Mahesh, Ragini Bal, Prapas, Ioannis, Nemni, Edoardo, Kalaitzis, Freddie, Ganju, Siddha, Ramos-Pollan, Raul
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that c
Externí odkaz:
http://arxiv.org/abs/2211.02869
Autor:
Bono, Carlo, Pernici, Barbara, Fernandez-Marquez, Jose Luis, Shankar, Amudha Ravi, Mülâyim, Mehmet Oğuz, Nemni, Edoardo
Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-b
Externí odkaz:
http://arxiv.org/abs/2202.12014
Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used architecture in remote sensing interpretation tasks, Convolutional Neural Net
Externí odkaz:
http://arxiv.org/abs/2201.10953
These are the "proceedings" of the 2nd AI + HADR workshop which was held virtually on December 12, 2020 as part of the Neural Information Processing Systems conference. These are non-archival and merely serve as a way to collate all the papers accept
Externí odkaz:
http://arxiv.org/abs/2012.02108
Autor:
Logar, Tomaz, Bullock, Joseph, Nemni, Edoardo, Bromley, Lars, Quinn, John A., Luengo-Oroz, Miguel
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, New York, United States, 2020
Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational suppor
Externí odkaz:
http://arxiv.org/abs/2001.10685
Delivering humanitarian aid is critical in handling crises such as natural disasters and manmade conflicts. However, during these scenarios, it can be difficult to gather any information that could be valuable for an effective humanitarian response.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d971b24ed5157d7cf0addd7397e65a2
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