Segmentation of remote sensing data is a critical task in various environmental and geospatial applications. However, the presence of missing modalities, such as the Digital Elevation Model (DEM), poses significant challenges to achieving high segmentation accuracy. In this paper, we propose a novel approach for addressing this issue using Prototype Knowledge Distillation. Our methodology involves training a teacher model with access to all available modalities, including DEM, to generate high-quality segmentation results. Subsequently, we train a student model that performs segmentation without access to the DEM modality. The teacher model distills its learned knowledge into the student model through prototype representations, ensuring that the student model can achieve comparable or better performance despite the missing modality. Experimental results demonstrate the effectiveness of our approach, showing significant improvements in segmentation accuracy over baseline methods that do not utilize knowledge distillation. This work paves the way for robust segmentation of remote sensing data in scenarios where certain modalities are unavailable, enhancing the applicability and reliability of remote sensing analyses.

An explainable AI framework for flood detection in SAR images is proposed. Compact encoder-decoder CNNs are used within the framework to achieve flood segmentation, with their output results fed to a Grad-CAM explainer so as to introduce trustworthiness to a stakeholder from naive thresholding selection during post-processing steps. The proposed framework is evaluated on the ETCi 2021 dataset using three different CNNs, resulting in more than 97% accuracy, while descriptive statistics on the Jaccard score are used to indicate the CNNs improper generalization towards the dataset. Edge cases highlight the importance of using Grad-CAM in complement with the CNN when the latter struggles to segment small regions due to thresholding.

This work proposes an interpretable Deep Learning framework utilizing Vision Transformers (ViT) for the classification of remote sensing images into land use and land cover (LULC) classes. It uses the Shapley Additive Explanations (SHAP) values to achieve two-stage explanations: 1) bandwise feature importance per class, showing which band assists the prediction of each class and 2) spatial-wise feature understanding, explaining which embedded patches per band affected the network's performance. Experimental results on the EuroSAT dataset demonstrate the ViT's accurate classification with an overall accuracy 96.86 %, offering improved results when compared to popular CNN models. Heatmaps in each one of the dataset's existing classes highlight the effectiveness of the proposed framework in the band explanation and the feature importance.

An interpretable deep learning framework for land use and land cover classification (LULC) in remote sensing using SHAP is introduced. It utilizes a compact CNN model for the classification of satellite images and then feeds the results to a SHAP deep explainer so as to strengthen the classification results. The proposed framework is applied to Sentinel-2 satellite images containing 27000 images of pixel size 64 × 64 and operates on three-band combinations, reducing the model’s input data by 77% considering that 13 channels are available, while at the same time investigating on how different spectrum bands affect predictions on the dataset’s classes. Experimental results on the EuroSAT dataset demonstrate the CNN’s accurate classification with an overall accuracy of 94.72%, whereas the classification accuracy on three-band combinations on each of the dataset’s classes highlights its improvement when compared to standard approaches with larger number of trainable parameters. The SHAP explainable results of the proposed framework shield the network’s predictions by showing correlation values that are relevant to the predicted class, thereby improving the classifications occurring in urban and rural areas with different land uses in the same scene.

Links between environmental conditions (e.g., meteo-rological factors and air quality) and COVID-19 infection/mortality have been reported worldwide. However, the existing statistical frameworks are insufficient to investigate the factors that increase the risk for COVID-19 in urban areas. In this paper, we extend the concept of machine learning-based predictive modelling for COVID-19 spread, proposing an explainable AI approach in order to i) prioritize the risk factors, ii) define the interconnections between them and iii) detect positive or negative influence of the factors with respect to COVID-19 morbidity and mortality.