Land cover is important for global change studies, and its accuracy and reliability are usually verified by field sampling, which costs a lot. A method was proposed for the verification of land cover datasets with the geo-tagged natural scene images using a convolutional neural network. The nature scene images were firstly collected from the Land Use and Cover Area frame Survey (LUCAS) and global crowdsourcing images platform Flickr, then classified according to the Land Cover Classification System. The Nature Scene Image Classification (NSIC) model based on the GoogLeNet Inception network for recognition of natural scene images was then constructed. Finally, in the UK, as a verification area, the European Space Agency Climate Change Initiative Land Cover (ESA CCI-LC) datasets and the Global land-cover product with fine classification system (GLC-FCS) were verified using the NSIC-Inception model with the nature scene image set. The verification results showed that the overall accuracy verified by LUCAS was very close to the accuracy of the land cover product, which was 94.41% of CCI LC and 92.89% of GLC-FCS, demonstrating the feasibility of using geo-tagged images classified by the NSIC model. In addition, the VGG16 and ResNet50 were compared with GoogLeNet Inception. The differences in verification between LUCAS and Flickr images were discussed regarding the image’s quantity, the spatial distribution, the representativeness, and so on. The uncertainties of verification arising from differences in the spatial resolution of the different datasets were explored by CCI LC and GCL-FCS. The application of the method has great potential to support and improve the efficiency of land cover verification.

Carbon satellites, as the most direct means of observing carbon dioxide globally, offer credible and scientifically robust methods for estimating carbon emissions. To enhance the accuracy and timeliness of urban-scale carbon emission estimates, this study proposes an innovative model that integrates top-down carbon satellite data with high-resolution spatial proxies, including points of interest, road networks, and population distribution. The K-means clustering method was employed to study the relationship between carbon emissions and XCO₂ anomalies. Based on this, the local adaptive carbon emission estimation model was constructed. Further, by integrating the spatial distribution and weights of proxy data, carbon emissions were reallocated to generate a high-resolution urban carbon emission map at a 1 km × 1 km resolution. Taking Urumqi, the XCO₂ background concentration ranged from approximately 408 ppm to 415 ppm in 2020, and the corresponding ${∆\mathrm{XCO}}_2$ ranged from −1.58 ppm to 1.13 ppm. The total carbon emission estimated by the local adaptive model amounted to approximately 58.26718 million tons in 2020, close to the EDGAR dataset, with most monthly relative error within ±10%. The Pearson correlation coefficient between the ODIAC dataset and spatially redistributed carbon emission was 0.192, and their comparison showed that high carbon emission areas in the spatially redistributed carbon emission aligned closely with urban industrial parks and commercial centers, offering a more detailed representation of urban carbon emission spatial characteristics. This method contributed to exploring the potential of carbon satellites for quantitatively measuring anthropogenic emissions and offers improved insights into monitoring urban-scale carbon dioxide emissions.

Integrating heterogeneous and multilingual geoscience texts into coherent knowledge graphs is challenged by semantic inconsistencies from terminology variations, diverse expressions, and data heterogeneity, hindering the construction of reliable mineral exploration knowledge systems. We propose a semantic-aware fusion framework that enables consistent and sustainable integration of mineral exploration knowledge. Built on a standardized geological knowledge schema defining core entities and their interrelations, the framework incorporates an incremental update paradigm via a schema-guided fusion mechanism that detects and resolves semantic conflicts while preserving provenance for traceable evolution. Evaluated on textual sources, the framework achieves an overall triple extraction F1-score of 0.82. Notably, for the critical task of entity extraction, it attains an F1-score of 0.88, outperforming BERT-BiLSTM and BERT-BiLSTM-CRF baselines by up to 11 points. Precision for key metallogenic elements exceeds 0.90. It identifies 1432 conflicts during fusion and generates a refined knowledge graph of 18,204 high-quality de-duplicated triples, retaining 87.3% of inputs. The resulting graph supports downstream applications, including case analysis, visualization, question answering, and mineral prospectivity prediction. Unlike conventional aggregation approaches, this work treats knowledge fusion as a semantically guided dynamic process, enhancing consistency, transparency, and adaptability. It provides a practical pathway toward intelligent and sustainable geoscience knowledge infrastructures.