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Claudia Paris
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2020 – today
- 2024
- [j23]Surbhi Sharma
, Rocco Sedona
Sen4Map: Advancing Mapping With Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17: 13893-13907 (2024) - [c26]Claudia Paris, Manuka Khan, Marco Cattaneo, Yue Dou:
A Data-Driven Approach for Estimating Regional Food Flows Fusing Earth Observation and Geospatial Data. IGARSS 2024: 897-900 - [c25]Surbhi Sharma, Rocco Sedona, Morris Riedel, Gabriele Cavallaro, Claudia Paris:
Enhancing Land Cover Mapping: A Novel Automatic Approach To Improve Mixed Spectral Pixel Classification. IGARSS 2024: 1017-1020 - [c24]Belise Dusabe, Yue Dou, Rhys Manners, Claudia Paris:
EO-Derived Geospatial Data for Monitoring Food and Nutrition Security: A Case Study of Rwanda. IGARSS 2024: 3860-3863 - [i1]Cássio Fraga Dantas, Raffaele Gaetano, Claudia Paris, Dino Ienco:
Reuse out-of-year data to enhance land cover mapping via feature disentanglement and contrastive learning. CoRR abs/2404.11114 (2024) - 2023
- [j22]Claudio Persello
AI4SmallFarms: A Dataset for Crop Field Delineation in Southeast Asian Smallholder Farms. IEEE Geosci. Remote. Sens. Lett. 20: 1-5 (2023) - [j21]Rocco Sedona, Claudia Paris
Toward the Production of Spatiotemporally Consistent Annual Land Cover Maps Using Sentinel-2 Time Series. IEEE Geosci. Remote. Sens. Lett. 20: 1-5 (2023) - [j20]Giulio Weikmann
Multiyear Mapping of Water Demand at Crop Level: An End-to-End Workflow Based on High-Resolution Crop Type Maps and Meteorological Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16: 6758-6775 (2023) - [c23]Florian Appel, Heike Bach, Silke Migdall, Manolis Koubarakis, George Stamoulis, Dimitris Bilidas, Despina-Athanasia Pantazi, Lorenzo Bruzzone, Claudia Paris
ExtremeEarth: Managing Water Availability for Crops Using Earth Observation and Machine Learning. EDBT 2023: 749-756 - [c22]Liang Tian, Rocco Sedona, Amirpasha Mozaffari
End-to-End Process Orchestration of Earth Observation Data Workflows with Apache Airflow on High Performance Computing. IGARSS 2023: 711-714 - [c21]Adel Abbas, Michele Linardi, Etienne Vareille, Vassillis Christophides, Claudia Paris
Towards Explainable AI4EO: An Explainable Deep Learning Approach for Crop Type Mapping using Satellite Images Time Series. IGARSS 2023: 1088-1091 - [c20]Rocco Sedona, Jan Ebert, Claudia Paris
Enhancing Training Set Through Multi-Temporal Attention Analysis in Transformers for Multi-Year Land Cover Mapping. IGARSS 2023: 5411-5414 - 2022
- [j19]Daniele Marinelli
A Triangulation-Based Technique for Tree-Top Detection in Heterogeneous Forest Structures Using High Density LiDAR Data. IEEE Geosci. Remote. Sens. Lett. 19: 1-5 (2022) - [j18]Daniele Marinelli
An Approach Based on Deep Learning for Tree Species Classification in LiDAR Data Acquired in Mixed Forest. IEEE Geosci. Remote. Sens. Lett. 19: 1-5 (2022) - [j17]Claudia Paris
An Interactive Strategy for the Training Set Definition Based on Active Self-Paced Learning Implemented on a Cloud-Computing Platform. IEEE Geosci. Remote. Sens. Lett. 19: 1-5 (2022) - [j16]Claudia Paris
A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian Country Case Study. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15: 9146-9159 (2022) - [c19]Rocco Sedona, Claudia Paris
An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. IGARSS 2022: 203-206 - [c18]Claudia Paris
A Novel Approach for Environmental Monitoring Based on the Integration of Multi-Temporal Multi-Source Earth Observation Data and Field Surveys in a Spatio-Temporal Framework. IGARSS 2022: 5897-5900 - 2021
- [j15]Giulio Weikmann
TimeSen2Crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop-Type Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 4699-4708 (2021) - [j14]Desta Haileselassie Hagos
ExtremeEarth Meets Satellite Data From Space. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 9038-9063 (2021) - [j13]Rocco Sedona
A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 10134-10146 (2021) - [j12]Claudia Paris
A Novel Approach to the Unsupervised Extraction of Reliable Training Samples From Thematic Products. IEEE Trans. Geosci. Remote. Sens. 59(3): 1930-1948 (2021) - [j11]Aravind Harikumar
A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data. IEEE Trans. Geosci. Remote. Sens. 59(5): 4444-4453 (2021) - [c17]Iwona Podsiadlo, Claudia Paris
An Approach Based on Low Resolution Land-Cover-Maps and Domain Adaptation to Define Representative Training Sets at Large Scale. IGARSS 2021: 313-316 - 2020
- [j10]Iwona Podsiadlo
Integrating Models and Remote Sensing Data for Distributed Glacier Mass Balance Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 13: 6177-6194 (2020) - [c16]Antonis Troumpoukis, Stasinos Konstantopoulos, Giannis Mouchakis, Nefeli Prokopaki-Kostopoulou, Claudia Paris, Lorenzo Bruzzone, Despina-Athanasia Pantazi, Manolis Koubarakis:
GeoFedBench: A Benchmark for Federated GeoSPARQL Query Processors. ISWC (Demos/Industry) 2020: 228-232
2010 – 2019
- 2019
- [j9]Daniele Marinelli
An Approach to Tree Detection Based on the Fusion of Multitemporal LiDAR Data. IEEE Geosci. Remote. Sens. Lett. 16(11): 1771-1775 (2019) - [j8]Claudia Paris
A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data. IEEE Trans. Geosci. Remote. Sens. 57(1): 76-92 (2019) - [j7]Claudia Paris
A Novel Sharpening Approach for Superresolving Multiresolution Optical Images. IEEE Trans. Geosci. Remote. Sens. 57(3): 1545-1560 (2019) - [j6]Claudia Paris
A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images. IEEE Trans. Geosci. Remote. Sens. 57(7): 4259-4277 (2019) - [c15]Manolis Koubarakis, Konstantina Bereta
From Copernicus Big Data to Extreme Earth Analytics. EDBT 2019: 690-693 - [c14]Daniele Marinelli
An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation. IGARSS 2019: 94-97 - [c13]Claudia Paris
Automatic Extraction of Weak Labeled Samples From Existing Thematic Products For Training Convolutional Neural Networks. IGARSS 2019: 5722-5725 - [c12]Manuel Bertoluzza, Claudia Paris
A Fast Method for Cloud Removal and Image Restoration on Time Series of Multispectral Images. MultiTemp 2019: 1-4 - 2018
- [j5]Claudia Paris
A Sensor-Driven Hierarchical Method for Domain Adaptation in Classification of Remote Sensing Images. IEEE Trans. Geosci. Remote. Sens. 56(3): 1308-1324 (2018) - [j4]Daniele Marinelli
A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas. IEEE Trans. Geosci. Remote. Sens. 56(6): 3030-3046 (2018) - [c11]Claudia Paris
A Novel Method Based on Source Domain Understanding and Modeling to Transfer Labels from Land-Cover Vector Maps to Classifiers for Multispectral Images. IGARSS 2018: 3619-3622 - [c10]Daniele Marinelli
Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds. IGARSS 2018: 3999-4002 - 2017
- [j3]Claudia Paris
A Novel Automatic Method for the Fusion of ALS and TLS LiDAR Data for Robust Assessment of Tree Crown Structure. IEEE Trans. Geosci. Remote. Sens. 55(7): 3679-3693 (2017) - [c9]Claudia Paris
A novel automatic approach to the update of land-cover maps by unsupervised classification of remote sensing images. IGARSS 2017: 2207-2210 - [c8]Claudia Paris
A hierarchical approach to superresolution of multispectral images with different spatial resolutions. IGARSS 2017: 2589-2592 - [c7]Lorenzo Bruzzone, Francesca Bovolo, Claudia Paris
Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions. MultiTemp 2017: 1-4 - 2016
- [j2]Claudia Paris
A Hierarchical Approach to Three-Dimensional Segmentation of LiDAR Data at Single-Tree Level in a Multilayered Forest. IEEE Trans. Geosci. Remote. Sens. 54(7): 4190-4203 (2016) - [c6]Daniele Marinelli
Fusion of high and very high density LiDAR data for 3D forest change detection. IGARSS 2016: 3595-3598 - [c5]Claudia Paris
A data-driven identification of growth-model classes for the adaptive estimation of single-tree stem diameter in LiDAR data. IGARSS 2016: 6918-6921 - 2015
- [j1]Claudia Paris
A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images. IEEE Trans. Geosci. Remote. Sens. 53(1): 467-480 (2015) - [c4]Claudia Paris
A precise estimation of the 3D structure of the forest based on the fusion of airborne and terrestrial lidar data. IGARSS 2015: 49-52 - [c3]Claudia Paris
A hierarchical approach to the segmentation of single dominant and dominated trees in forest areas by using high-density LiDAR data. IGARSS 2015: 65-68 - 2014
- [c2]Claudia Paris
A sensor-driven domain adaptation method for the classification of remote sensing images. IGARSS 2014: 185-188 - 2013
- [c1]Claudia Paris
A novel technique for tree stem height estimation by fusing low density LiDAR data and optical images. IGARSS 2013: 3022-3025
Coauthor Index
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last updated on 2024-10-07 21:25 CEST by the dblp team
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