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.

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.

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.

Abstract Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and widely-adopted approach toward thorough verification and validation of such algorithms in practical scenarios. We tackle this issue and propose a comprehensive validation approach that can shed more light on functional and non-functional abilities of the solvers. Additionally, we propose novel similarity metrics to measure the distance between the routing schedules that can be used in verifying the convergence abilities of randomized techniques. To reflect practical aspects of intelligent transportation systems, we introduce an algorithm for elaborating solvable benchmark instances for any vehicle routing formulation, alongside the set of quality metrics that help quantify the real-life characteristics of the delivery systems, such as their profitability. The experiments prove the flexibility of our approach through utilizing it to the NP-hard pickup and delivery problem with time windows, and present the qualitative, quantitative, and statistical analysis scenarios which help understand the capabilities of the investigated techniques. We believe that our efforts will be a step toward the more critical and consistent evaluation of emerging vehicle routing (and other) solvers, and will allow the community to easier confront them, thus ultimately focus on the most promising research avenues that are determined in the quantifiable and traceable manner.

Hyperspectral unmixing is one of the most challenging tasks in the analysis of such data. There have been an array of algorithms proposed for this problem so far, but they are virtually always verified using random sampling, where training and test examples are drawn from the same image. Since such samples are spatially correlated and may be positioned close to each other, random sampling can induce the training-test information leak in the techniques that exploit spatial information during the unmixing process. We want to raise the attention of the community about this validation flaw in the context hyperspectral unmixing. We introduce the algorithm for unbiased validation of the unmixing techniques through splitting hyperspectral images into training and test samples that do not suffer from the training-test information leak. The experiments showed that the widely-used random sampling verification strategy leads to overly optimistic conclusions concerning the algorithm’s performance. This problem was mitigated with the proposed approach which allows us to rigorously validate unmixing techniques.

Improving agricultural practices through exploiting the recent imaging and machine learning advancements plays a key role nowadays to ensure sustainable food security, and to help us deal with the climate change. Quantifying soil parameters can lead to optimizing the fertilization process but it is cumbersome, time-consuming and difficult to scale, as it requires performing in-situ soil measurements that are later analyzed in the laboratory settings. In the HYPER-VIEW challenge, we aim at automating the soil analysis thanks to the utilization of hyperspectral images that capture very detailed information about the scanned objects in hundreds of contiguous hyperspectral bands. Such imagery can be effectively analyzed using an array of classical and deep machine learning approaches. Also, the AI techniques can be deployed on-board the imaging satellites— it opens new doors related to the scalability of the solution. The winners of the challenge will be offered a unique opportunity to run their proposed solution in orbit, on-board the Intuition-1 satellite, equipped with a hyperspectral imager and on-board AI capabilities.

The recent advances in Earth observation and artificial in-telligence allow us to improve the agricultural management practices through effectively exploiting the spectral, spatial, and temporal characteristics of the area of interest captured by satellite images. In this paper, we tackle the problem of extracting high-resolution (2.5-meter) cultivated land maps from Sentinel-2 multispectral images, and propose a machine learning algorithm for this task. It aggregates the spectral, spatial, and temporal features of the upsampled images, and is independent from the number of observations captured for a given scene. The experimental results, performed within the framework of the Enhanced Sentinel-2 Agriculture chal-lenge show that our technique manifests high generalization abilities over the unseen data and elaborates high-quality cul-tivated land maps. Finally, utilizing this algorithm led us to taking the $6^{\text{th}}$ place in the aforementioned challenge.

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5m cultivated land maps from 10m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.

Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter, we follow this research pathway and propose a multi-branch convolutional neural network that benefits from fusing spectral, spatial, and spectral-spatial features in the unmixing process. The results of our experiments, backed up with the ablation study, revealed that our techniques outperform others from the literature and lead to higher-quality fractional abundance estimation. Also, we investigated the influence of reducing the training sets on the capabilities of all algorithms and their robustness against noise, as capturing large and representative ground-truth sets is time-consuming and costly in practice, especially in emerging Earth observation scenarios.

Hyperspectral unmixing allows us to estimate the endmember abundances in each pixel of an input hyperspectral image. Although there exist deep learning-powered end-to-end methods for this task, the lack of labeled ground-truth data is a challenging problem which makes the adoption of such techniques extremely difficult in emerging practical use cases where the ground truth is costly to capture. In this paper, we investigate and quantify the impact of the training set size on the quality of unmixing provided by deep learning models of conceptually different architectures.

Although deep learning is gaining more widespread use in hyperspectral image analysis, it is challenging to train high-capacity models in a supervised way—ground-truth sets are expensive to obtain, and they are practically always extremely imbalanced. To deal with the problem of missing ground-truth data, its high dimensionality and potential redundancy, we introduce a novel unsupervised feature learning technique to extract discriminative features from the original data. It exploits recurrent neural network-based asymmetric autoencoders (AEs) to learn the compressed representation of unlabeled data, and can elaborate both spectral and spectral–spatial features. Our extractors can be incorporated into the unsupervised segmentation pipeline—they can be followed by any clustering algorithm. The experiments revealed that our approaches deliver high-quality segmentation without any prior class labels, and are one order of magnitude faster than 3-D convolutional AEs. Our algorithms outperform or work on par with other approaches while allowing for significant data reduction.

Hyperspectral images provide detailed information about the scanned objects, as they capture their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability. In this paper, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our experiments showed that the proposed method works in real time and outperforms other spectral-spatial algorithms.