Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Alessandro Crivellari"'
Autor:
Omid Ghorbanzadeh, Hejar Shahabi, Sepideh Tavakkoli Piralilou, Alessandro Crivellari, Laura Elena Cue la Rosa, Clement Atzberger, Jonathan Li, Pedram Ghamisi
Publikováno v:
IEEE Access, Vol 12, Pp 118453-118466 (2024)
The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The
Externí odkaz:
https://doaj.org/article/88a5beb4e0384828bd81a4204d33afaa
Autor:
Lixia Chu, Jeroen Nelen, Alessandro Crivellari, Dainius Masiliūnas, Carola Hein, Christoph Lofi
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTUncovering relationships between geospatial features and COVID-19 features is a comprehensive, confounding, cross-disciplinary and challenging topic, as the spread and effects of COVID-19 are related to many aspects of our lives, including so
Externí odkaz:
https://doaj.org/article/b7f82c98cc9640b6923f47a35ee4f25c
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 2623-2643 (2023)
The semantic segmentation of informal urban settlements represents an essential contribution towards renovation strategies and reconstruction plans. In this context, however, a big challenge remains unsolved when dealing with incomplete data acquisit
Externí odkaz:
https://doaj.org/article/8d0c03d5baa34871a7d1d35a5f4e779f
Autor:
Alessandro Crivellari, Bernd Resch
Publikováno v:
Computational Urban Science, Vol 2, Iss 1, Pp 1-15 (2022)
Abstract Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to ea
Externí odkaz:
https://doaj.org/article/b3a5fd2fcc9e4b9f8c88b8901eb8cf9c
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
Abstract Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (D
Externí odkaz:
https://doaj.org/article/ed41ca3c6abe431c9ad297276161f66d
Publikováno v:
Remote Sensing, Vol 14, Iss 24, p 6382 (2022)
The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly
Externí odkaz:
https://doaj.org/article/39ebdd612e974406babf8117978bc7f7
Publikováno v:
Sensors, Vol 22, Iss 4, p 1682 (2022)
Trajectory data represent an essential source of information on travel behaviors and human mobility patterns, assuming a central role in a wide range of services related to transportation planning, personalized recommendation strategies, and resource
Externí odkaz:
https://doaj.org/article/df7a647f67ad4628aab819009188a700
Autor:
Alessandro Crivellari, Alina Ristea
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 10, Iss 4, p 210 (2021)
The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats crime types as mutually independent when they do not branch from the same high
Externí odkaz:
https://doaj.org/article/da2ddd91eefd4531a83c5af2e3acdc4f
Autor:
Alessandro Crivellari, Euro Beinat
Publikováno v:
Mathematics, Vol 8, Iss 12, p 2233 (2020)
Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on po
Externí odkaz:
https://doaj.org/article/6a33dac3a3ed4a75bb985be8553bb9b5
Autor:
Alessandro Crivellari, Euro Beinat
Publikováno v:
Sensors, Vol 20, Iss 12, p 3503 (2020)
Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims t
Externí odkaz:
https://doaj.org/article/4fefe2a2f66042b882aaf5e4b6fde91b