RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product

no code implementations • 26 Oct 2022 • Priyash Bhugra, Benjamin Bischke, Christoph Werner, Robert Syrnicki, Carolin Packbier, Patrick Helber, Caglar Senaras, Akhil Singh Rana, Tim Davis, Wanda De Keersmaecker, Daniele Zanaga, Annett Wania, Ruben Van De Kerchove, Giovanni Marchisio

The results demonstrate an improvement of approximately 0. 89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the mono-temporal approach.

Change Detection Classification +3

DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

no code implementations • CVPR 2022 • Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Andrés Camero, Jingliang Hu, Ariadna Pregel Hoderlein, Çağlar Şenaras, Timothy Davis, Daniel Cremers, Giovanni Marchisio, Xiao Xiang Zhu, Laura Leal-Taixé

These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.

Earth Observation Semantic Segmentation +2

RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

no code implementations • 5 Oct 2021 • Giovanni Marchisio, Patrick Helber, Benjamin Bischke, Timothy Davis, Caglar Senaras, Daniele Zanaga, Ruben Van De Kerchove, Annett Wania

Under the sponsorship of the European Union Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products.

Time Series Time Series Analysis

Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images

no code implementations • 10 Mar 2021 • Jack White, Alex Codoreanu, Ignacio Zuleta, Colm Lynch, Giovanni Marchisio, Stephen Petrie, Alan R. Duffy

Modern deep Super-Resolution (SR) networks have established themselves as valuable techniques in image reconstruction and enhancement.

Image Reconstruction Super-Resolution