Aligning Analytical Capabilities with Mining Industry Needs: Impact Analysis of Emerging Technologies from the Goldeneye Project at Five Mine Sites

Earth Observation (EO) data have the macro-level decision and analysis capabilities, which plays a key role in Earth science and significant scientific discoveries [1]. With the rapid development of space technologies, more and more EO data are accessed freely and shared openly. However, it is increasingly difficult, also more complex, to mine the information and knowledge among these massive-temporal datasets. EO Data Cubes (DC) provides a best-of-breed technology to build a spatial data infrastructure to fill this gap [2]. Among them, Australian Geoscience Data Cube (AGDC) [3, 4], as the leader and contributor, has supported data processing and analytical capability by dividing and restructuring grids. Following this open source software and work experience, we design and develop China Data Cube (CDC) system in recent years. Based on the new OGC DGGS standard and cloud computing technologies, CDC has got better system performance, more EO data types and richer local application cases. According to current and future months of work, this paper will describe and share the lessons learned from design and implement of CDC system. It will include following several aspects: 1) Overview of CDC system; 2) parallel EO data ingestion technology; 3) Cloud based EO data storage strategy; 4) Extension of more EO data types, especially China EO data; 5) The future work for CDC.

Assessment of Earth Observations Data Harmonization

Development of satellite remote sensing platforms and sensors enables massive Earth Observation (EO) data available. Satellite earth observation data has been one of the most important scientific data for Global Change Research (GCR) studies. This paper focuses on developing a cloud computing infrastructure for environmental monitoring using inter-agency EO data. A test bed of collaborative computing platform based on cloud computing is introduced to coordinate EO data and computing resources to implement on-demand EO data discovery, access and data-intensive processing, which are time-consuming work for the GCR scientists to. EO data are commonly distributed archived and most of the non-commercial data can be accessed by internet. With more usage of EO data in the GCR, researchers must spend a lot of time to collect distributed data via limited internet access. And several steps such as data download, preprocessing, information extraction with specific algorithms or software, visualization and mapping should be done mostly by manual to get useful output for further applications, which has been the heavy burden for the research work. In this paper, a Geo-computing platform is introduced to harness multi-source EO data and coordinate computing resources for on-demand processing. Cloud computing and workflow technique are the key components of the platform. And several case studies such drought monitoring are used to test the performance of the platform.

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

When spatial land tenure relations are not available, the only effective alternative data method is to rely on the agricultural census at the regional or national scale, based on household surveys and a participatory mapping at the local scale. However, what if even these are not available, which is typical for conflict-affected countries, administrations suffering from a lack of data and resources, or agencies that produce a sub-standard quality. Would it, under such circumstances, be possible to rely on remotely sensed Earth Observation (EO) data? We hypothesize that it is possible to qualify and quantify certain types of unknown land tenure relations based on EO data. Therefore, this study aims to standardize the identification and categorization of certain objects, environments, and semantics visible in EO data that can (re-)interpret land tenure relations. The context of this study is the opportunity to mine data on North Korean land tenure, which would be needed in case of a Korean (re-)unification. Synthesizing land tenure data in conjunction with EO data would align land administration practices in the respective parts and could also derive reliable land tenure and governance variables. There are still many unanswered questions about workable EO data proxies, which can derive information about land tenure relations. However, this first exploration provides a relevant contribution to bridging the semantic gap between land tenure and EO data.

ABSTRACTThe challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

Doing Safer Masculinities: Addressing at-Risk Gendered Behaviours on Mine Sites

Earth Observation (EO), as a tool to improve efficiency of mineral exploration and mine site monitoring, requires easily accessible robust highly automated data processing algorithms. The Horizon Europe funded Research and Innovation Action project “Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring” (MultiMiner, 2023–2036) develops innovative machine learning solutions to support the critical raw material (CRM) independency of EU. We develop and utilize self-supervised or weakly supervised machine learning solutions which require a low number of in situ reference data. This presentation showcases the recent advancements of the MultiMiner project and highlights of application of the novel machine learning algorithms in selected case studies for mineral exploration and mine site monitoring.In the MultiMiner project, robust, transferable, scalable and automated tools are developed for mineral exploration. These tools are based on multi-source EO data at multiple data scales and platforms and are implemented into a stand-alone software. The tools include a Mineral Mapping Algorithm (MMA) to perform an automatic spectral feature extraction from deposit-type related reference spectra from a customized reference mineral spectral library. Additionally, workflows to perform automated machine learning interpretation of the multiscale EO data mapping results are developed to produce value added mineral maps of alteration zone or proxy minerals. Finally, a Mineral Prospectivity Wizard GUI is developed, facilitating multi-scale mineral mapping and automatic data interpretation in a guided step-by-step process to analyse EO data even usable for non-remote sensing experts.  The developed algorithms are expected to improve accuracy and time-efficiency of direct mineral identification of CRMs and other raw materials.To reduce disruptions to mining operations and monitor environmental aspects of operating and closed mine sites, MultiMiner creates timely mine site monitoring methods. A novel Generic Mine Site Monitoring (GMSM) algorithm, capable of combining multi-source EO data at various temporal, spatial and spectral resolutions, and requiring only a limited amount of in situ data, is developed. The GMSM algorithm leverages EO foundation models for different modalities, and includes support of temporal information as well. The GMSM algorithm can automatically monitor impacts of mining on the environment, such as water quality and acid mine drainage mapping, or combined monitoring of atmospheric and surface dust. Furthermore, success of rehabilitation activities, including monitoring the revegetation status and Tailings Storage Facility (TSF) dismantling are researched. EO-based solutions for improving mining safety and mitigating operational risks are proposed in terms of ground moisture monitoring and open pit and TSF dam stability monitoring.To unlock the potential of EO data, including Copernicus Sentinel-1 and Sentinel-2, EnMAP, drone-borne hyperspectral, radiometric and multiband SAR as well as in situ collected spectral data, we present case studies to demonstrate and validate the use of the MultiMiner machine learning -based algorithms at five test sites in Europe. The acquired field data are harmonized following project-specific guidelines and subsequently, the metadata of the thus acquired field data are safeguarded in a project database. In the presentation, we give a brief overview of the guidelines and the database.  

Accounting of the hydrologic process of evapotranspiration (ET) or consumptive use of water is important for water resources allocation, irrigation management, drought early warning, climate change impact assessment as well as in agro-water-climate nexus modeling. In fact, monitoring the United Nations' sustainable development goals (SDGs) that emphasize on improved food security, access to clean water, promotion of sustainable habitats and mitigation of natural disasters (droughts) hinge upon access to better quality data of ET. Though numerous studies have targeted accurate estimation of potential evapotranspiration (PET) using earth observation (EO) data; hydrologists are yet to reach consensus on the best set of predictor variables that can be used irrespective of spatio-temporal scale. This can be attributed to the nonlinear and complex nature of the process of ET. When it comes to the estimation of actual ET (AET), studies employing Eddy Covariance (EC) towers have been successful in different regions of the world. However, the developing countries of the world lack access to EC observations, requiring viable economical methods for accurate ET measurement, even using reliable estimates of PET. The proposed study explored fusion of regional climate reanalysis data, EO data, and machine learning techniques for high-resolution PET estimation. In this analysis, owing to the documented success of data-driven models in hydrological studies, performance of two machine learning models- tree based Random Forest (RF) and regressor Multivariate Adaptive Regression Splines (MARS), are evaluated for estimating monthly PET. A suite of input predictors are chosen to describe three model categories: meteorological-, EO- and hybrid-based predictor models. There are about 10 input combinations that can be generated for the PET model development, particularly for an agriculture-dominated study region - Dhenkanal district, located in Odisha in eastern part of India. In this study, reanalysis-based (meteorological) inputs at a grid resolution of 0.12° and Sentinel 2A (EO) products at spatial resolution of 20 m have been used. Results of the analysis indicate that solar radiation is the most important meteorological variable that controls PET estimation. Among the vegetation indices obtained from remote sensing data, we find that the Normalized Difference Water Index (NDWI) that represents availability of water in plants and soil, is particularly useful. The best PET estimation model that uses only solar radiation and few vegetation indices (NDVI, NDWI) gave coefficient of determination (R2) 0.88 and root mean square error (RMSE) of 0.14 during validation stage, whereas the use of hybrid predictor model that utilize temperature and vegetation indices information further reduced the error and increased the prediction accuracy (6.86%). When the meteorological inputs: precipitation and wind speed are only used, model did not perform well. Mapping the ET using the proposed models can facilitate reporting of progress in SDG with regard to water use, crop water stress, adaptation to agricultural droughts and food security. In this context, the Evaporative Demand Drought Index (EDDI) is computed across the study region to understand the drought patterns in the region. Keywords: Potential Evapotranspiration, Agricultural Drought, Food Security, EO Data, Random Forest, Machine Learning, Vegetation Indices

This paper presents the results of research designed to explore the challenges involved in the use of Earth Observation (EO) data to support environmental management Brazil. While much has been written about the technology and applications of EO, the perspective of end-users of EO data and their needs has been under-explored in the literature. A total of 53 key informants in Brasilia and the cities of Rio Branco and Cuiaba were interviewed regarding their current use and experience of EO data and the expressed challenges that they face. The research builds upon a conceptual model which illustrates the main steps and limitations in the flow of EO data and information for use in the management of land use and land cover (LULC) in Brazil. The current paper analyzes and ranks, by relative importance, the factors that users identify as limiting their use of EO. The most important limiting factor for the end-user was the lack of personnel, followed by political and economic context, data management, innovation, infrastructure and IT, technical capacity to use and process EO data, bureaucracy, limitations associated with access to high-resolution data, and access to ready-to-use product. In general, users expect to access a ready-to-use product, transformed from the raw EO data into usable information. Related to this is the question of whether this processing is best done within an organization or sourced from outside. Our results suggest that, despite the potential of EO data for informing environmental management in Brazil, its use remains constrained by its lack of suitably trained personnel and financial resources, as well as the poor communication between institutions.

The papers in this special issue represent a wide range of current research efforts by the scientific community of Earth Observation (EO) data processing and monitoring algorithm developers and end-users of such datasets to address these issues. In particular, these papers reflect research that is designed to exploit information at multiple scales and sensor systems for analysis of local, regional and global EO data. The following situations are represented in this special issue: (1) large-area land mapping and monitoring approaches and applications; (2) using and analyzing data from multiple spatial, temporal and spectral resolutions in synergy; and (3) different surface types, ground-characteristics (e.g., land types: urban, vegetation, biophysical variables, etc.) and applications, including issues of consistency and continuity for applications. The special issue is comprised of papers representing three sensing paradigms: single sensor passive EO data, single sensor active EO data, and multi-sensor configurations that seek synergistic combinations of more than one EO for analysis. The special issue also includes papers that focus on the theory of effective exploitation of these modalities in single and multisensor configurations.

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Abstract. Earth observation (EO) data cubes have revolutionized the way large volumes of EO data can be stored, accessed, and processed. However, users coming from application domains outside of traditional EO research still face some significant technical barriers when querying an EO data cube with the aim to infer knew knowledge about real world entities and events. They have to interpret EO data in order to give them meaning, which is an ill-posed problem that requires advanced expertise in the field of EO analytics. We propose a semantic querying framework in which users query the EO data cube through an ontology, rather than accessing the data values themselves. The ontology formalizes symbolic representations of real-world entities and events, which are mapped to data values in the EO data cube through a mapping component formulated by an EO expert. This takes away the need for users to be aware of the EO data and how to interpret them, and therefore lowers the technical barriers to extract valuable information from EO data. We implemented a proof-of-concept of our approach as an open-source Python package.

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.

Monitoring and mapping open-pit mining activity is essential to identify operation sites and unaffected surfaces of mining areas. Vertical displacements of the earth's surface associated with open pit mining can be detected using high spatial resolution Digital Surface Model (DSM) data or based on all-weather Synthetic Aperture Radar (SAR) Single Look Complex (SLC) satellite images using Differential Interferometry Synthetic Aperture Radar (DInSAR) technique. In some cases, activity in an open pit may not be accompanied by changes in terrain heights but cause violations of land cover integrity accompanied by earth's surface texture changes (for example, deforestation or recultivation, violation of quarries and dump slope integrity, changes in surface conditions, hydrological disturbances, etc.) and can be detected using coherence maps generated from SAR SLC data.Coherence is the modulus of the complex correlation coefficient between two SLC images containing information about the amplitude and phase of the radar signal. If there is no surface change between the two survey dates, the coherence values are close to 1. Mining activities change the surface texture, so the coherence decreases to values close to 0. The frequency approach estimates the total changes in coherence over the season. For example, the Temporal Activity Index (TAI) is a relative coherence frequency below a given threshold across the time series of SAR images. In the case of monitoring open pit mining, activity areas with consistently low coherence over a time series of observations are of primary interest.The study area is an open-pit mining area of the Pyhäsalmi Mine located in the Pohjois-Pohjanmaa region, Finland. It includes an old open pit, a backfill open pit, and several waste dumps [1]. Time series of Sentinel-1 SLC Interferometric Wide (IW) images were used to detect active areas in operation for the study area. Images were collected every 12 days from May to  September 2020-2022 and provided by the GOLDEN-AI platform [2].For each observation year, a time series of Sentinel-1 SLC coherence was generated for the Pyhäsalmi mine. Active areas in operation were identified for open pits and waste dumps based on TAI maps (Fig. 1), providing information about the intensity of surface changes during the observation periods.Figure 1. Temporal Activity Index maps for the Pyhäsalmi Mine area.Funding. This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 869398 “Earth observation and Earth GNSS data acquisition and processing platform for safe, sustainable and cost-efficient mining operations” (Goldeneye).Acknowledgments. The authors gratefully acknowledge Maria Hänninen, Environmental Manager at Pyhäsalmi Mine Oy for specification locations for measurements and study planning, and the OPT/NET BV company (opt-net.eu) and GOLDEN-AI platform for supplying Sentinel-1 data. The authors would like to thank the European Commission, the European Space Agency, and the Copernicus Program for providing Sentinel-1 data.