# Entropy-Centric Explainable AI for Remote Sensing Image Segmentation

arXiv: https://arxiv.org/abs/2608.11064 · HTML: https://mlsanity.com/paper/2608.11064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis](https://mlsanity.com/paper/2111.04138/) (2021)
- [Attribution-based XAI Methods in Computer Vision: A Review](https://mlsanity.com/paper/2211.14736/) (2022)
- [Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images](https://mlsanity.com/paper/2111.11397/) (2021)
- [Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation](https://mlsanity.com/paper/2310.01828/) (2023)
- [Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation](https://mlsanity.com/paper/2310.01837/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains. Many concerns arise regarding the decision-making process of its models, mainly due to deep neural networks outperforming their peers at the cost of ambiguity in feature extraction and prediction. Consequently, in critical domains such as remote sensing, where high-resolution imagery must be analyzed using black-box models, the lack of transparency limits trust in these models and, thus, their adoption. In light of this reality, explaining and understanding the complex decision-making process of AI models has become essential. Explainable AI (XAI) aims to bridge this gap by providing insights into how and why certain decisions are made. While significant progress has been achieved in explaining image classification tasks, image segmentation still offers considerable room for improvement. In this context, this paper proposes an entropy-centric XAI method for semantic segmentation. Moreover, a new XAI evaluation methodology is proposed to efficiently measure the relevance of the regions highlighted by the proposed XAI method. Experimental results demonstrate the superiority of the proposed XAI method compared with recently adapted XAI methods for semantic segmentation.