The use of autonomous unmanned aerial vehicle (UAV) swarms for area coverage requires efficient coverage path planning (CPP) strategies that ensure complete exploration while minimizing maneuvering effort, energy consumption, and collision risk. This paper proposes a distributed computational framework for swarm-based patrolling using CPP algorithms. The framework integrates Bézier-curve trajectory smoothing and safety-distance constraints to generate dynamically feasible and collision-free paths. A capability-aware space decomposition method partitions the target region into convex subareas, enabling parallel coverage while accounting for UAV configuration and platform capabilities. Swarm-adapted versions of Parallel, Square, LMAT, and SCAN strategies are developed to generate intra- and inter-subregion coverage paths. Experimental validation using a homogeneous swarm of four quadcopters demonstrates reduced computational complexity and turning maneuvers while producing smooth and continuous trajectories, enabling efficient large-area coverage with improved operational endurance.

We argue that traditional 2D hyperspectral imaging is not adapted to many modern challenges. With the rise of high spatial resolution, hyperspectral sensors mounted on different platforms (e.g. drones, terrestrial, satellites) and innovative applications (e.g. urban mapping, mining monitoring), projections, occlusions, perspective effects and data processing limit the use of 2D hyperspectral imaging. We propose that 3D hyperclouds, in which Lidar or photogrammetric point clouds are augmented with hyperspectral attributes, can address numerous of these challenges. We demonstrate the benefits of hyperclouds and dedicated machine learning architectures with several realistic examples.

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

The widespread application of drones and associated miniaturization of imaging sensors has led to an explosion of remote sensing applications with very high spatial and spectral resolutions. The 3-D ultrahigh-resolution digital outcrop models created using drones and oblique imagery from ground-based sensors are now commonly used in the academic and industrial sectors, while the generation of spatially accurate models has been greatly facilitated by the development of computer vision tools, such as structure from motion, and the correction of spectral attributes to achieve material reflectance measurements remains challenging. Following the development of a topographical correction toolbox (mephysto), we now propose a series of new tools that can leverage the detailed geometry captured by digital outcrop models to correct for illumination effects caused by oblique viewing angles and the interaction of light with complex 3-D surfaces. This open-source code is integrated intohylite, a python toolbox for the full 3-D processing and fusion of digital outcrop models with hyperspectral imaging data. We validate the performance of our novel method using a case study at an open-pit mine in Tharsis, Spain, and demonstrate the importance of accurate illumination corrections for quantitative spectral analyses. Significantly, we show that commonly applied spectral analysis techniques can yield erroneous results for data corrected using current state-of-the-art approaches. Our proposed method ameliorates many of the issues with these established approaches.

Drones are getting more and more used to replace piloted platforms to reduce the costs and increase safety of activities such as monitoring, delivery or warfare. So far though, drones have barely been used as more than single-sensor platforms. In order to be used in mineral exploration we need to ensure that the data acquired by drones are versatile, accurate and adapted to the tasks but also that the platforms are robust and low-maintenance to ensure an operational use in remote locations. During the last years we developed and tested a series of workflows to rapidly provide relevant information to exploration teams. It starts with multi-source data acquisition, data integration and preprocessing. We then use machine learning to process the data and generate relevant geological information.

Geological mapping in difficult-to-access terrain such as open pit mines often relies on remotely sensed data. Hyperspectral data yield valuable geological information, especially when spectral ranges of multiple sensors are used in conjunction. In this contribution we project a number of hyperspectral datasets of an open pit mine covering the visible to near-infrared (VNIR), short-wave infrared (SWIR), and long-wave infrared (LWIR) range from airborne, drone-borne and ground-based acquisitions into a photogrammetric point cloud. The resulting hyperspectral digital outcrop is then used as a basis for data integration in a 3D environment. To discriminate geologic materials in the pit we apply a Gaussian deconvolution to identify the position of diagnostic absorption features in the SWIR and LWIR, and then apply a support vector machine-based classification. Our results agree with known lithologic units and alteration patterns and can be used to guide exploration targeting and mine planning.

Accurately mapping lithology and geological structures remains a challenge in rough terrain or in active mining areas. We propose that the integration of terrestrial and drone-borne multi-sensor remote sensing techniques can significantly boost the reliability, safety, and efficiency of geological activities in exploration and for the monitoring of mining activities. We have now developed a complete procedural chain to jointly and accurately process Structure-from-Motion Multi-View Stereo point clouds and hyperspectral data cubes in the visible to near-infrared (VNIR) and short-wave infrared (SWIR), as well as long-wave infrared (LWIR) ranges acquired by terrestrial sensors. Hyperspectral data are processed using spectroscopic and machine learning algorithms to generate meaningful 2.5D (i.e., surface) maps that are available to geologists on the ground shortly after data acquisition. We classify the geological information content using innovative machine learning techniques. We validate the remote sensing data with in-situ mineralogical and structural measurements. Repeated acquisitions allow then to integrate a time component.

Traditional exploration techniques usually rely on extensive geological field work complemented by geophysical ground surveying. However, this approach can be limited by field accessibility, financial status, area size and climate and can be confronted with public rebuff. We recommend the use of multi-scale hyperspectral remote sensing to mitigate the limitations of traditional exploration. Multi-scale remote sensing is particularly beneficial, especially in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally non-invasive surveying. Additionally, Unmanned Aerial Systems (UAS) coupled with various sensors provide an opportunity to conduct noninvasive exploration in socially sensitive areas and in relatively inaccessible locations. The development of operational technologies and the evaluation of appropriate processing techniques in different mineral deposit environments contribute to a rapidly evolving field at the cutting edge of exploration technologies. Ultimately, this study provides the opportunity to advance the discovery of critical raw material deposits.

We propose to develop an efficient and integrated exploration workflow that includes remote sensing data obtained by multiple types of sensors at different altitudes, a combination that has been identified as potentially disruptive technology for the mineral exploration sector. The fusion of multi-source and multi-temporal data is, therefore, a key challenge for a successful data integration. Ultimately, the objective is to boost the competitiveness, growth, sustainability, and attractiveness of the raw material sector.

The high demand for raw materials in our post-industrial societies contrasts the increasing difficulties to find new mineral deposits. In Europe, accessible and high-grade deposits are mostly exhausted or currently mined. Hence, future exploration must focus on the remaining, more remote locations or penetrate much deeper into the Earth's crust. Sustaining mining activities in Europe would allow the development of key technologies but also sustainable and ethical production of technological metals. Thus, we suggest to focus research on advances in multi-scale and multi-sensor remote sensing-based Earth integration techniques. The scale should range from satellite to air- and drone-borne systems and include ground validation. Multi-sensor downscaling methods involving SAR and optical data are particularly promising. We demonstrate that the integration with other sensors and/or measures such as geophysical/geochemical data as well as non-conventional remote sensing features such as textures and geometries are of interest. Thus, ultimately, our objective is to boost the competitiveness, growth, sustainability and attractiveness of the raw material sector in Europe. While we focus on the raw material sector as it is currently of strategic importance, the required methods are transferable to most environmental studies.

Hyperspectral long-wave infrared imaging (LWIR HSI) adds a promising complement to visible, near infrared, and shortwave infrared (VNIR and SWIR) HSI data in the field of mineral mapping. It enables characterization of rock-forming minerals such as silicates and carbonates, which show no detectable or extremely weak features in VNIR and SWIR In the last decades, there has been a steady increase of publications on satellite, aerial, and laboratory LWIR data. However, the application of LWIR HSI for ground-based, close-range remote sensing of vertical geological outcrops is sparsely researched and will be the focus of the current study. We present a workflow for acquisition, mosaicking, and radiometric correction of LWIR HSI data. We demonstrate the applicability of this workflow using a case study from a gravel quarry in Germany. Library spectra are used for spectral unmixing and mapping of the main lithological units, which are validated using sample X-ray diffraction (XRD) and thin section analysis as well as FTIR point spectrometer data.