A review on explainability in multimodal deep neural nets

G Joshi, R Walambe, K Kotecha - IEEE Access, 2021 - ieeexplore.ieee.org
Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

D Tuia, K Schindler, B Demir, XX Zhu… - … and Remote Sensing …, 2024 - ieeexplore.ieee.org
Earth observation (EO) is increasingly used for map** and monitoring processes
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …

Understanding deep learning in land use classification based on Sentinel-2 time series

M Campos-Taberner, FJ García-Haro, B Martínez… - Scientific reports, 2020 - nature.com
The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is
rapidly increasing. DL techniques have provided excellent results in applications ranging …

Interpretable image classification with differentiable prototypes assignment

D Rymarczyk, Ł Struski, M Górszczak… - … on Computer Vision, 2022 - Springer
Existing prototypical-based models address the black-box nature of deep learning.
However, they are sub-optimal as they often assume separate prototypes for each class …

[LIBRO][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences

G Camps-Valls, D Tuia, XX Zhu, M Reichstein - 2021 - books.google.com
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …

Icicle: Interpretable class incremental continual learning

D Rymarczyk, J van de Weijer… - Proceedings of the …, 2023 - openaccess.thecvf.com
Continual learning enables incremental learning of new tasks without forgetting those
previously learned, resulting in positive knowledge transfer that can enhance performance …

Artificial intelligence to advance Earth observation: a perspective

D Tuia, K Schindler, B Demir, G Camps-Valls… - arxiv preprint arxiv …, 2023 - arxiv.org
Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …

Protoseg: Interpretable semantic segmentation with prototypical parts

M Sacha, D Rymarczyk, Ł Struski… - Proceedings of the …, 2023 - openaccess.thecvf.com
We introduce ProtoSeg, a novel model for interpretable semantic image segmentation,
which constructs its predictions using similar patches from the training set. To achieve …

Toward a collective agenda on ai for earth science data analysis

D Tuia, R Roscher, JD Wegner… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
In past years, we have witnessed the fields of geosciences and remote sensing and artificial
intelligence (AI) become closer. Thanks to the massive availability of observational data …

Geoinformation harvesting from social media data: A community remote sensing approach

XX Zhu, Y Wang, M Kochupillai… - … and Remote Sensing …, 2022 - ieeexplore.ieee.org
As unconventional sources of geoinformation, massive imagery and text messages from
open platforms and social media form a temporally quasi-seamless, spatially …