At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

Suitability evaluation of tea cultivation is very important for improving the yield and quality of tea, which can avoid blind expansion and achieve sustainable development; however, to date, relevant research at town and village scales is lacking. This study selected Xinming Township in Huangshan City, Anhui Province, as the study area, which is the main production area of Taiping Houkui Tea—one of the ten most famous teas in China. We proposed a machine learning-based tea cultivation suitability evaluation model by comparing logistic regression (LR), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), random forest (RF), Gaussian Naïve Bayes (GNB), and multilayer perceptron (MLP) to calculate the weight accuracy of the evaluation factors. We then selected 12 factors, including climate, soil, terrain, and ecological economy factors, using the RF with the highest accuracy to calculate the evaluation factor weights and obtained the suitability evaluation results. The results show that the highly suitable area, moderately suitable area, generally suitable area, and unsuitable area land categories for tea cultivation were 14.13%, 27.25%, 32.46%, and 26.16%, respectively. Combined with field research, the highly suitable areas were mainly distributed in northwest Xinming Town, which is in line with the distribution of tea cultivation at the Xinming township level. The results provide a scientific reference to support land allocation decisions for tea cultivation and sustainable green agricultural development at the town and village scales.

To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies.

Understanding the relationship between drought and the water use efficiency (WUE) in terrestrial ecosystems can help reduce drought risk. It remains unclear what the correlation between the cropland water use efficiency (CWUE) and drought during drought events. We aim to identify the spatiotemporal relationship between drought and the CWUE and to ensure the service capacity of cultivated land ecosystems. In this study, the cubist algorithm was used to establish a monthly integrated surface drought index (mISDI) dataset for the Huang–Huai–Hai Plain (HHHP), and the run theory was used to identify drought events. We assessed the spatio-temporal variations of drought in the HHHP during 2000–2020 and its influence on the CWUE. The research results were as follows: from the overall perspective of the HHHP, the mISDI showed a downward trend. Drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant. The CWUE response to drought had a three-month lag period and a significant positive correlation, and it was shown that the cultivated land ecosystems in this area had strong drought resistance ability. This study provides a new framework for understanding the response of the CWUE to drought and formulating reasonable vegetation management strategies for the HHHP.