研究計畫
| 年度 | 計畫名稱 | 擔任工作 |
|---|---|---|
| 114 | 深度學習於都市電動車充電樁使用狀態即時空間預測之應用 | 計畫主持人 |
| 113 | 以生成式人工智慧保護人類移動軌跡之隱私 II | 計畫主持人 |
| 112 | 以生成式人工智慧保護人類移動軌跡之隱私 | 計畫主持人 |
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研究著作
| 作者 | 出版年月 | 著作名稱 | 收錄出處 |
|---|---|---|---|
| Alessandro Crivellari, Yuhui Chi | 2026-01 | Federated LSTM-based deep learning model for privacy-preserving predictions of human trajectories across multiple data providers | Annals of GIS |
| Alesssandro Crivellari, Yuhui Shi | 2025-01 | Generative adversarial deep learning model for producing location-based synthetic trajectory data | Connection Science |
| Omid Ghorbanzadeh, Hejar Shahabi, Sepideh Tavakkoli Piralilou, Alessandro Crivellari, Laura Elena Cué la Rosa, Clement Atzberger, Jonathan Li, Pedram Ghamisiu | 2024-08 | Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection | IEEE Access |
| Lixia Chu, Jeroen Nelen, Alessandro Crivellari, Dainius Masiliūnas, Carola Hein, Christoph Lofi | 2024-05 | Relationships between geo-spatial features and COVID-19 hospitalisations revealed by machine learning models and SHAP values | International Journal of Digital Earth |
| Chunzhu Wei, Hong Wei, Alessandro Crivellari, Taichang Liu, Yuanmei Wan, Wei Chen, and Yang Lu | 2023-11 | Gaofen-2 satellite image-based characterization of urban villages using multiple convolutional neural networks | International Journal of Remote Sensing |
| Alessandro Crivellari, Hong Wei, Chunzhu Wei, Yuhui Shi | 2023-07 | Super-resolution GANs for upscaling unplanned urban settlements from remote sensing satellite imagery–the case of Chinese urban village detection | International Journal of Digital Earth |
| Omid Ghorbanzadeh, Alessandro Crivellari, Dirk Tiede, Pedram Ghamisi, Stefan Lang | 2022-12 | Mapping dwellings in IDP/refugee settlements using deep learning | Remote Sensing |
| Hao Jing, Xin He, Yong Tian, Michele Lancia, Guoliang Cao, Alessandro Crivellari, Zhilin Guo, Chunmiao Zheng | 2022-11 | Comparison and interpretation of data-driven models for simulating site-specific human-impacted groundwater dynamics in the North China Plain | Journal of Hydrology |
| Alessandro Crivellari, Bernd Resch | 2022-06 | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions | Computational Urban Science |
| Alessandro Crivellari, Euro Beinat, Sandor Caetano, Arnaud Seydoux, Thiago Cardoso | 2022-02 | Multi-target CNN-LSTM regressor for predicting urban distribution of short-term food delivery demand | Journal of Business Research |
| Alessandro Crivellari, Bernd Resch, Yuhui Shi | 2022-02 | TraceBERT — A feasibility study on reconstructing spatial–temporal gaps from incomplete motion trajectories via BERT training process on discrete location sequences | Sensors |
| Omid Ghorbanzadeh, Hejar Shahabi, Alessandro Crivellari, Saeid Homayouni, Thomas Blaschke, Pedram Ghamisi | 2022-01 | Landslide detection using deep learning and object-based image analysis | Landslides |
| Omid Ghorbanzadeh, Alessandro Crivellari, Pedram Ghamisi, Hejar Shahabi, Thomas Blaschke | 2021-07 | A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan) | Scientific Reports |
| Alessandro Crivellari, Alina Ristea | 2021-04 | CrimeVec — Exploring spatial-temporal based vector representations of urban crime types and crime-related urban regions | ISPRS International Journal of Geo-Information |
| Alessandro Crivellari, Euro Beinat | 2020-12 | Forecasting spatially-distributed urban traffic volumes via multi-target LSTM-based neural network regressor | Mathematics |
| Alessandro Crivellari, Euro Beinat | 2020-06 | Trace2trace — A feasibility study on neural machine translation applied to human motion trajectories | Sensors |
| Alessandro Crivellari, Euro Beinat | 2020-01 | LSTM-based deep learning model for predicting individual mobility traces of short-term foreign tourists | Sustainability |
| Alessandro Crivellari, Euro Beinat | 2019-08 | From motion activity to geo-embeddings: Generating and exploring vector representations of locations, traces and visitors through large-scale mobility data | ISPRS International Journal of Geo-Information |
| Alessandro Crivellari, Euro Beinat | 2019-07 | Identifying foreign tourists’ nationality from mobility traces via LSTM neural network and location embeddings | Applied Sciences |
| Anna Kovacs-Györi, Alina Ristea, Ronald Kolcsar, Bernd Resch, Alessandro Crivellari, Thomas Blaschke | 2018-09 | Beyond spatial proximity — Classifying parks and their visitors in London based on spatiotemporal and sentiment analysis of Twitter data | ISPRS International Journal of Geo-Information |
研究領域
GeoAI / Geospatial Data Science (Geoinformatics)
任教科目
Introduction to Data Science and Machine Learning
學術經歷
EDUCATION
Feb 2018 – May 2021:
•PhD in Applied Geoinformatics, University of Salzburg (Salzburg, Austria).
PhD Thesis: Artificial neural networks for human mobility analysis and spatial-temporal activity modeling.
(Supervisors: Prof. Euro Beinat, Dr. Pavlos Kazakopoulos).
• Oct 2015 – Dec 2017:
MSc in Biomedical Engineering, Politecnico di Milano (Milan, Italy).
Master Thesis: Identification of atrial fibrillation from RR intervals: a feasibility study on Long Short-Term Memory neural networks.
(Supervisors: Prof. Manuela Ferrario, Prof. Joseph Randall Moorman).
• Sep 2011 – Feb 2015:
BSc in Biomedical Engineering, Politecnico di Milano (Milan, Italy).
Bachelor Thesis: Biomedical sensor system for physical activity monitoring.
(Supervisor: Prof. Giambattista Gruosso).
WORK AND RESEARCH EXPERIENCE
•Aug 2021 – July 2023:
Postdoctoral Research Fellow in Artificial Intelligence at the Department of Computer Science and Engineering, Southern University of Science and Technology (Shenzhen, China).
Research topic: GeoAI – Artificial Intelligence for geospatial applications.
• Feb 2018 – May 2021:
PhD researcher in Applied Geoinformatics at the Doctoral College “GIScience”, Department of Geoinformatics - Z_GIS, University of Salzburg (Salzburg, Austria).
Fully-funded position by the Austrian Science Fund (FWF).
Research topic: Artificial neural networks for human mobility analysis and spatial-temporal activity modeling.
•Mar 2020 – Jul 2020:
Research intern in Naspers and Prosus AI team (Amsterdam, The Netherlands), and collaborations with iFood AI team (Sao Paulo, Brazil) on behalf of Prosus.
Paid internship position by Prosus, within the context of PhD research stay abroad.
Research topic: Predicting urban distribution of short-term food delivery demand (side works also comprise restaurant churn forecasting, demand shaping strategies
and customer recommendations).
• Mar 2017 – Sep 2017:
Visiting researcher at the University of Virginia Health System (Charlottesville, VA, USA).
Invited research guest for developing the experimental part of the Master Thesis.
Research topic: Deep learning for automatic cardiac arrhythmias detection