Sebastian Hafner

Orcid: 0000-0003-3560-638X

According to our database1, Sebastian Hafner authored at least 11 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Cross-Modal Hashing With Feature Semi-Interaction and Semantic Ranking for Remote Sensing Ship Image Retrieval.
IEEE Trans. Geosci. Remote. Sens., 2024

Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration.
CoRR, 2024

2023
Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data.
Remote. Sens., November, 2023

Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda.
Proceedings of the Joint Urban Remote Sensing Event, 2023

Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping.
Proceedings of the Joint Urban Remote Sensing Event, 2023

Multi-Modal Deep Learning for Multi-Temporal Urban Mapping with a Partly Missing Optical Modality.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

2022
Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net.
IEEE Geosci. Remote. Sens. Lett., 2022

Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles.
Int. J. Appl. Earth Obs. Geoinformation, 2022

A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments.
Int. J. Appl. Earth Obs. Geoinformation, 2022

Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Exploring the Fusion of Sentinel-1 SAR and Sentinel-2 MSI Data for Built-Up Area Mapping Using Deep Learning.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021


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