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By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development. • Eight BiSeNet model to classify basalt land uses/covers and landscape assessment were developed. • Performance of BiSeNet using input size of 128x128 with Nadam optimizer is highest. • Three different indices including DCAD, TECI, and SHDI were chosen to assess landscape quality. • Outcomes of BiSeNet models are used effectively to monitor different landscape indices. • Landscape quality in 41 geosites of Dak Nong UGGp was monitored over 30 years. [ABSTRACT FROM AUTHOR]

Urbanisation is a key aspect of land use change (LUC), and accurately modelling of urban LUC is crucial for sustainable development. Cellular automata (CA) are widely used in LUC research. However, previous studies have overlooked the significant temporal dependence and spatial heterogeneity associated with LUC. To address these gaps, this study proposes a novel model called KCLP-CA, which integrates k-means, a convolutional neural network (CNN), a long and short-term memory neural network (LSTM), and the popular patch-generation land use model (PLUS). Initially, k-means and CNN are utilised to address spatial heterogeneity, while LSTM tackles temporal dependence. The LSTM and land expansion analysis strategy (LEAS) models of PLUS are employed to obtain land use conversion probability maps. Finally, a simulation of land use dynamic change was conducted using a linear weighted fusion conversion probability map that accounts for random factors. To validate the KCLP-CA model, land use data collected from Hangzhou between 1995 and 2000 were employed. The results showed that the KCLP-CA model outperformed traditional methods, including artificial neural networks and random forest model, with the figure of merit (FoM) index increasing from 2.12% to 4.19%. Random forest analysis of drivers impacting LUC revealed that distance to water and road network density exerted the greatest influence on urban land development in Hangzhou. Incorporation of various policy planning factors affecting urban development yielded simulation results aligning more closely with reality, resulting in a FoM index increase of 1.64–1.76%. In summary, the model developed in this study combines the strengths of two sub models to deliver an accurate and effective simulation of future land use. [Display omitted] • Effects of spatiotemporal dependence on land use change were studied. • A novel KCLP-CA model was proposed in this study. • This model effectively combines the advantages of the KCL-CA and PLUS models. • Improved simulation outcomes were observed when the KCLP-CA model was employed. [ABSTRACT FROM AUTHOR]

Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products. • Use of hyperspectral imaging to predict multi-quality of processing tomatoes. • Use of hyperspectral imaging to classify maturity of processing tomatoes. • Comparison of recurrent neural networks with traditional machine learning methods. • Identification of important wavelengths in contributing to model performance. [ABSTRACT FROM AUTHOR]

Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation. [Display omitted] • KS approach provides an algorithmic framework that is not prone to overfitting. • KS approach has been applied in an extremely divergent case of P. edulis forests. • KS can be used for estimating unknown CSSs by datasets with missing data. • The study compiled the climatic classification table with CSSs for P. edulis forests. • Quantifying unobservable variables is a key for chaotic estimation in the future. [ABSTRACT FROM AUTHOR]