Chronic wounds affect 1–2% of the global population, necessitating objective diagnostic methods. This paper introduces the CURE system, which frames multimodal fusion as a global optimization problem to overcome rigid channel weighting in super-pixel segmentation. Using evolutionary strategies like Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), CURE calibrates a 6-dimensional feature-spatial space (L, a, b, T, x, y)—comprising CIELab color, Temperature (T), and spatial coordinates—to minimize Undersegmentation Error (UE) and precisely align boundaries with physiological edges. Comparing Lab-T (CIELab and Temperature) and RGB-T (Red-Green-Blue and Temperature) configurations, results show that evolutionary optimization universally enhances performance across 13 classifiers. Notably, AdaBoost's Matthews Correlation Coefficient (MCC) rose from 0.651 to 0.726, while Logistic Regression reached 0.875 accuracy. The k-Nearest Bayesian (KNB) classifier achieved a high sensitivity of 0.924, crucial for minimizing false negatives. Derived consensus weights ensure robust generalization for real-time diagnostics on unseen patients. To ensure reproducibility, our code is available at GitHub: https://github.com/awijata/cure.

Gray-box optimization employs Walsh decomposition to obtain non-linear variable dependencies and utilize them to propose masks of variables that have a joint non-linear influence on fitness value. These masks significantly improve the effectiveness of variation operators. In some problems, all variables are non-linearly dependent, making the aforementioned masks useless. We analyze the features of the real-world instances of such problems and show that many of their dependencies may have noise-like origins. Such noise-caused dependencies are irrelevant to the optimization process and can be ignored. To identify them, we propose extending the use of Walsh decomposition by measuring variable dependency strength that allows the construction of the weighted dynamic Variable Interaction Graph (wdVIG). wdVIGs adjust the dependency strength to mixed individuals. They allow the filtering of irrelevant dependencies and re-enable using dependency-based masks by variation operators. We verify the wdVIG potential on a large benchmark suite. For problems with noise, the wdVIG masks can improve the optimizer's effectiveness. If all dependencies are relevant for the optimization, i.e., the problem is not noised, the influence of wdVIG masks is similar to that of state-of-the-art structures of this kind.

Monitoring forest areas is of paramount importance to maintain environmental sustainability. The scalability of forest monitoring solutions is effectively offered by satellite imaging, where images of various modalities are acquired in orbit and cover large areas. However, building machine learning models for such downstream Earth observation (EO) tasks is challenging due to the limited amounts of ground-truth datasets. We tackle this issue and introduce an end-to-end deep learning pipeline to detect forest changes from Sentinel-2 time series of multispectral images (MSIs). It benefits from a foundation model (FM) fine-tuned over a small yet spatially diverse dataset. The experiments showed that not only does it outperform other deep models but also it requires minimal user intervention before the fine-tuning process.

Hyperspectral imaging brings important opportunities in a variety of fields due to the unprecedented amount of information it captures in numerous narrow and contiguous spectral bands. However, the high spectral and spatial dimensionality of hyperspectral images makes them challenging to transfer, store, and ultimately analyze, while only a subset of bands may be significant in specific downstream applications in Earth observation. In this article, we tackle this issue and introduce CANNIBAL---a band selection algorithm based on unsupervised clustering of inter-band dependencies captured in weighted Variable Interaction Graphs, which are a side-effect of the optimization performed by the Genetic Algorithm with Linkage Learning. We apply CANNIBAL to two downstream tasks of hyperspectral unmixing and segmentation. Our experimental study revealed that it outperforms other band selection algorithms and allows us to dramatically reduce the number of bands without negatively affecting the quality of downstream models. Finally, CANNIBAL offers a high level of flexibility, as it can be both parametric and non-parametric, depending on a use case.

Cardiovascular diseases remain one of the leading causes of death worldwide. Therefore, developing and validating automated tools to help identify high-risk patients are of paramount clinical utility. In this article, we tackle this task and introduce a genetic programming algorithm (called GIRAFFE) to build (deep) machine learning classification ensembles for arrhythmia classification from two-dimensional images of 12-lead electrocardiogram (ECG) tracings. GIRAFFE evolves the architecture, content, and fusion scheme of the ensemble, to obtain an accurate yet lightweight classification system. The experimental study performed over a large-scale dataset of ECG images revealed that our approach outperforms other ensemble methods and carefully fine-tuned deep models, elaborates compact heterogeneous ensembles, and does not require any user intervention hence it is easy to apply to other classification tasks.

Needle localization in ultrasound images is pivotal for the successful execution of ultrasound-guided core needle biopsies. Automating the needle detection process can decrease the procedure time and lead to a more precise diagnosis. In this article, we introduce an automatic method for detecting the core needle and determining its trajectory in 2D ultrasound images. In our approach, the Vision Transformer architecture, renowned for its self-attention mechanisms is used for needle detection and segmentation, and is followed by the analysis of the Radon transformed segmentation mask to identify the needle’s trajectory. The experiments, performed over two clinical datasets of more than 600 ultrasound images rigorously split into various training-test subsets and backed up with a variety of statistical analyses revealed that our approach offers highquality needle segmentation, and significantly outperforms other techniques in identifying the needle’s trajectory, with the trajectory localization errors reduced up to more than $5 \times$ when compared to the most competitive deep learning algorithm. We believe that our work may pave the way for more accurate and efficient ultrasoundguided procedures, ultimately improving patient outcomes.

Recent advances in remote sensing and artificial intelligence offer exciting opportunities in an array of fields, with precision agriculture being a notable use case. Here, estimating soil parameters from remotely-sensed hyperspectral imagery at a global scale can play a pivotal role in day-to-day operations, as it may help optimize agricultural management processes, hence positively affecting our planet. In this paper, we tackled the problem of estimating soil parameters from hyperspectral images and introduced heterogeneous regression ensembles for this task. They not only benefit from both classic and deep machine learning models but were also thoroughly investigated and fine-tuned in our rigorous experimental study, performed over a well-established HYPERVIEW benchmark dataset. The experiments showed that such heterogeneous ensembles outperform other techniques and offer a high level of model flexibility.

Current technological advancements bring exciting opportunities in hyperspectral image analysis across various Earth observation applications, with precision agriculture being one of their notable examples. However, gathering high-quality and representative ground-truth data for training supervised machine learners is extremely challenging in emerging use cases, as it is cost-inefficient and user-dependent. Thus, building (deep) machine learning models from very few training samples is of paramount practical importance. To tackle this issue, we propose a semi-supervised learning pipeline for elaborating deep learning models for multi-class hyperspectral image classification, while benefiting from the available unlabeled samples, leveraging pseudo-labeling and consistency regularization. Although our technique is model-agnostic, we exploit a deep residual network for classifying hyperspectral images according to the magnesium content in the imaged soil. The experimental study performed over a real-world dataset (HYPERVIEW) encompassing environmental tasks crucial to food sustainability showed that the proposed technique significantly outperforms the pre-trained models fine-tuned in a fully supervised way.

Bare soil detection is an important step in soil composition analysis, as it can prune the areas that should be excluded from more expensive processing aimed at extracting selected soil parameters from hyperspectral images acquired in orbit. This is of paramount importance for on-board applications, where hardware constraints of an edge device (a satellite), such as computational and memory requirements or energy consumption need to be considered while processing big data in space. In this paper, we present a simple yet effective bare soil detection algorithm exploiting vegetation indices that is ready for in-orbit deployment. Our experimental study performed over the airborne hyperspectral data shows that this approach can be robustly used for simulated bands, i.e., wide bands aggregating several narrow neighboring bands within the spectrum. Therefore, we can apply our technique to sensors with lower spectral resolution. Finally, it offers high-quality bare soil delineation reaching the Dice Index of 0.85.

An unprecedented amount of data generated in space missions triggers lots of practical challenges and concerns with its transfer, storage, and analysis. As Lunar and deep space missions emerge, we need to also face the challenges of distributed computing and big data analytics. In this paper, we outline these issues and discuss how to design and analyze Lunar data centers, being space data centers designed for distributed computing, and data analysis for (not only) Lunar missions. We investigate the opportunities and chances of such space architectures to lay the foundations for practical space data centers and real-life use cases.

Satellite imaging brings exciting opportunities in an array of fields, with precision agriculture being a notable example. Soil analysis at scale with the use of Earth observation satellites coupled with on-board and on-the-ground artificial intelligence algorithms offers actionable items that may be exploited by practitioners to optimize their operations, including the fertilization process. Here, bare soil detection is a pivotal step in the processing chain to limit the detailed analysis to the areas of interest. In this paper, we tackle this task with quantum-kernel support vector machines and verify the utility of quantum machine learning in practical Earth observation. Our experimental study, performed over a real-world hyperspectral scene, indicates that the proposed quantum-kernel models are competitive with well-established classical support vector machines, as well as with approaches based on thresholding spectral indices that are widely exploited in the field.

Maintaining proper water supply is crucial for agriculture and efficient plant cultivation. In-situ soil moisture measurements lack scalability, necessitating non-invasive systems to estimate soil moisture from remotely-sensed hyperspectral images (HSIs). We propose a root-zone soil moisture estimation system using deep architectures on HSIs acquired via unmanned aerial vehicles. Experiments conducted on a meticulously prepared dataset of in-situ measurements and HSIs, collected across multiple agronomic seasons with diverse plant varieties, soil profiles, and watering scenarios, revealed that our approach achieved a mean absolute error, mean squared error, and R2of 0.60, 0.64, and 0.80, outperforming sensor-based methods.

Localization of a biopsy needle in ultrasound (US) images is an important medical image analysis task, as it may help clinicians reduce the risk of damaging the tissue surrounding the cancer and spreading cancerous cells. Despite numerous studies dedicated to segmenting the needle from US, virtually all of them build upon the strong assumption that the needle is present in the image, which does not hold in clinical settings. We address this research gap and propose an end-to-end machine learning approach for biopsy needle detection in US images. The rigorous experimental study revealed that our approach delivers high-quality and fast operation, while offering a high level of flexibility—not only does it allow to update all blocks of the pipeline, but also to build a detection cascade for multi-scale analysis which dramatically reduces the number of sub-images undergoing classification, hence speeds up the detection process.

The rapid growth of the space industry is creating an increasing amount of data in orbit, which at the current time is not matched yet by a corresponding increase in data download capacity. Using data analytics at the edge, i.e., data processing on-board satellites, holds the promise to significantly mitigate this problem. Newly available and highly efficient artificial intelligence (AI) hardware accelerators and other off-the-shelf compute-hardware has already been successfully exploited for demonstrating satellite on-board deep learning. Aggregating such components and technologies at scale and introducing resource sharing concepts beyond individual spacecrafts, will yield the equivalent of a space data center—a space-based system that will collect, process, store, and relay data from own sensors or from "client" satellites and work in orchestration with other SDCs in a network. In this paper, we outline new opportunities for such data- and compute-sharing schemes in space and how current limitations of existing systems can be overcome.

Detecting methane in satellite hyperspectral images (HSIs) can play a key role in environmental monitoring, as taking timely actions to reduce its emission and handle (unexpected) super emitters is of paramount importance. We tackle this issue and propose a machine learning pipeline for this task, with the ultimate goal of deploying it on board a satellite. Such solutions can offer global scalability, and they can act as a smart data prioritization step, as only those HSIs which contain methane can be downlinked for further analysis. However, the on-board deployment induces additional practical challenges—such algorithms should be resource-frugal, and should effectively operate on the target image data which may not be available during their development, since the satellite is not in orbit yet. Our experimental study revealed that the data-driven approaches can effectively detect methane in original airborne HSIs, as well as in HSIs emulating the target sensor and generated through data-level simulations.

Automatic localization of the biopsy needle in ultrasound images has become an important medical image analysis task, because it can directly translate to reducing the risk of damage to the tissues surrounding the lesion and spreading cancer cells. Although the algorithms to tackle this problem has been emerging at a steady pace, we lack a standardized way of validating them. In this paper, we tackle this issue and raise the attention of the research community to this experimental flaw which is especially important for the applications that can be ultimately deployed in the clinical settings. Our study, which involves a range of different training-test dataset splits performed over heterogeneous image data, showed that the incorrectly designed validation procedures can easily lead to overly optimistic conclusions concerning the abilities of such algorithms. We believe that our efforts will be an important step toward designing reproducible, rigorous, and fair approach for confronting the needle localization techniques in an unbiased way.