Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13791 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913160532852736 |
|---|---|
| author | Höhl, Adrian Obadic, Ivica Torres, Miguel Ángel Fernández Najjar, Hiba Oliveira, Dario Akata, Zeynep Dengel, Andreas Zhu, Xiao Xiang |
| author_facet | Höhl, Adrian Obadic, Ivica Torres, Miguel Ángel Fernández Najjar, Hiba Oliveira, Dario Akata, Zeynep Dengel, Andreas Zhu, Xiao Xiang |
| contents | In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches and emerging directions that tackle specific remote sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights, and reflect on the approaches used for the evaluation of explainable AI methods. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing Höhl, Adrian Obadic, Ivica Torres, Miguel Ángel Fernández Najjar, Hiba Oliveira, Dario Akata, Zeynep Dengel, Andreas Zhu, Xiao Xiang Machine Learning In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches and emerging directions that tackle specific remote sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights, and reflect on the approaches used for the evaluation of explainable AI methods. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field. |
| title | Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.13791 |