In this study, we present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches and outperforming state-of-the-art classical methods. Our empirical evaluations on synthetic datasets reveal that Q-Seg offers better runtime performance against the classical optimizer Gurobi. Furthermore, we evaluate our method on segmentation of Earth Observation images, an area of application where the amount of labeled data is usually very limited. In this case, Q-Seg demonstrates near-optimal results in flood mapping detection with respect to classical supervised state-of-the-art machine learning methods. Also, Q-Seg provides enhanced segmentation for forest coverage compared to existing annotated masks. Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical. 

Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation

Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines or quadratic discriminant analysis. By performing a grid-search-based hyperparameter optimization on 23 feature spaces we can show that all considered classifiers are capable of outperforming the current state-of-the-art neural network-based approaches in terms of total IoU on their best-performing feature spaces. With total and mean IoU values of 0.8751 and 0.7031 compared to 0.70 and 0.5873 as the previous best-reported results, we show that a simple gradient boosting classifier can significantly improve over deep neural network based approaches, despite using less training data. Furthermore, an analysis of the regional distribution of the Sen1Floods11 dataset reveals a problem of spatial imbalance. We show that traditional machine learning models can learn this bias and argue that modified metric evaluations are required to counter artifacts due to spatial imbalance. Lastly, a qualitative analysis shows that this pixel-wise classifier provides highly-precise surface water classifications indicating that a good choice of a feature space and pixel-wise classification can generate high-quality flood maps using optical and SAR data. We make our code publicly available at: https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance

On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches

The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across $4\,744$ distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.

https://export.arxiv.org/pdf/2511.21363v1.pdf

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

The utilization of multimodal analysis techniques, combining satellite imagery with terrain information, has gained prominence in flood detection. This study focuses on the inclusion of elevation data into the Sen1floods11 dataset, an open dataset for flood damage detection, to investigate the influence of terrain information on flood detection tasks. Among the considered terrain information, the inclusion of elevation data resulted in a bias of overestimating the presence of water in relatively low-lying areas, without contributing to accuracy improvement. However, the utilization of slope information, derived from differentiating the elevation data, mitigated such bias and yielded a slight improvement in accuracy. This finding aligns with the utilization of derivatives in physical equations describing flood flow, suggesting the explicit incorporation of physics-based principles, such as the flow of water based on slope, to enhance model accuracy in future research endeavors.

Effect Of Terrain Information On Multimodal Deep Learning For Flood Disaster Detection

Floods are one of the most severe natural catastrophes and therefore emergency response operations are crucial in order to save lifes. These operations require information about flooded areas so that rescue missions can precisely and efficiently use their available resources. This requires a quick automated procedure which is able to identify these regions from remote sensing images. To achieve this goal we utilize machine learning and apply our method on the Sen1Floods11 dataset. Our main contribution lies in the fusion of Digital Elevation Maps (DEMs) with Satellite data. We investigate the effect of several different combinations of processing methods of DEMs, such as depression filling, deriving slope and curvature or flow metrics. In total 44 different experiments have been performed where our best performing combination outperformed the benchmark in terms of mean IoU. Lastly, we also publish our code for downloading and processing DEMS as well as running our experiments.

Fusing Digital Elevation Maps with Satellite Imagery for Flood Mapping

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On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

Effect Of Terrain Information On Multimodal Deep Learning For Flood Disaster Detection

Fusing Digital Elevation Maps with Satellite Imagery for Flood Mapping