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Over the past ten years, large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate. Such growing data stock helps researchers understand the human-Earth system across different fields. A substantial amount of this data is published by geoscientists as open-access in authoritative journals. If the information stored in this literature is properly extracted, there is significant potential to build a domain knowledge base. However, this potential remains largely unfulfilled in geoscience, with one of the biggest obstacles being the lack of publicly available related corpora and baselines. To fill this gap, the Earth Science Data Corpus (ESDC), an academic text corpus of 600 abstracts, was built from the international journal Earth System Science Data (ESSD). To the best of our knowledge, ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of literature. The production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic literature. Furthermore, annotation guidelines and procedures tailored for Earth science data are formulated to ensure reliability. ChatGPT with zero- and few-shot prompting, BARTNER generative, and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics, with the highest achieved by BARTNER. Performance metrics for various entity types output by each model were also assessed. We utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus, aiming to automatically extract a broader and richer set of entity information. Subsequently, the extracted entity information was mapped and associated with the Earth science data knowledge graph. Around this knowledge graph, this paper validates multiple downstream applications, including hot topic research analysis, scientometric analysis, and knowledge-enhanced large language model question-answering systems. These applications have demonstrated that the ESDC can provide scientists from different disciplines with information on Earth science data, help them better understand and obtain data, and promote further exploration in their respective professional fields.

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of “monuments and groups of buildings” (73.2%), “sites” (19.3%), and “cultural landscapes” (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

This paper provides an overview of the opportunities that image analysts, archaeologists and conservation scientists currently have to use space-borne Synthetic Aperture Radar (SAR) imagery for prospection of cultural landscapes and investigation of environmental, land surface and anthropogenic processes that can alter the condition of heritage assets. The benefits of the recent developments in SAR satellite sensors towards higher resolution (up to less than 1m) and shorter revisiting times (up to a few days) are discussed in relation to established techniques using the two key SAR parameters – amplitude and phase. Selected case studies from Middle East to South America illustrate how SAR can be effectively used to detect subtle archaeological features in modern landscapes, monitor historic sites and assess damage in areas of conflict. These examples form the basis to highlight the current trends in archaeological remote sensing based on space-borne SAR data in the era of the European Space Agency's Sentinel-1 constellation and on-demand high resolution space missions such as TerraSAR-X.

By analysing the technical advantages and characteristics of imaging radar in cultural heritage, we provide new insights for the future development of cutting-edge Digital Heritage approaches through technical integration and interdisciplinary synergy.