The service network is capable of addressing large-scale service composition. However, existing service network works still have several limitations. Prior knowledge, such as expert-defined service chains, is not incorporated into the service network. QoS constraints are less considered in the service network, and thus the generated service chain does not always satisfy the optimal QoS constraints. Additionally, some basic services also require outputs to be used directly as inputs, which the service network cannot provide. To address these limitations, this paper proposes a geospatial service web (GSW) model named SR-QoS-GSW that incorporates service semantic relationships and QoS information. The SR-QoS-GSW model consists of atomic services and composite services that consider QoS, processing services, data services, and relationships among them. A SR-QoS-GSW prototype was developed using 570 atomic services and 27 composite services and evaluated using two case studies—a river network extraction and an urban housing selection. Then, the information entropy and time complexity between SR-QoS-GSW and the existing service network were compared. The results show that geospatial service chains can be created more efficiently by incorporating existing service chains as composite services. Integrating QoS information into the GSW would allow service composition algorithms to generate service chains that satisfy optimal QoS constraints. The outputs of services used as new inputs with additional self-matching relationships also give the service network greater flexibility. Finally, the analysis of the information entropy and time complexity verified the increased diversity and decreased the search space of the SR-QoS-GSW.

The timely and accurate acquisition of spatial distribution information for crops holds significant scientific significance for crop yield estimation, management, and timely adjustments to crop planting structures. This study revolves around Henan and Shaanxi provinces, employing a spatiotemporal image data fusion approach. Utilizing the characteristic representation of the Normalized difference vegetation index (NDVI) temporal data from Sentinel-2 satellite imagery, a multi-scale segmentation of patches is conducted based on spatiotemporal fusion images. Decision tree classification rules are constructed through the analysis of crop phenological differences, facilitating the extraction of the crop spatial patterns (CSPs) in the two provinces. The classification accuracy is assessed, yielding overall accuracies of 91.11% and 90.12%, with Kappa coefficients of 0.897 and 0.887 for Henan and Shaanxi provinces, respectively. The results indicate the following: (1) the proposed method enhances crop identification capabilities; (2) an accuracy evaluation against the data from the Third National Land Resource Survey and provincial statistical yearbook data for 2022 demonstrates extraction accuracy exceeding 90%; and (3) an analysis of the crop spatial patterns in 2022 reveals that wheat and corn are the predominant crops in Henan and Shaanxi provinces, covering 74.42% and 62.32% of the total crop area, respectively. The research outcomes can serve as a scientific basis for adjusting the crop planting structures in these two provinces.

Terraces constitute a critical form of land surface modification, serving both as essential agricultural resources and effective soil and water conservation measures. However, their morphological diversity, pronounced scale variation, and ambiguous geometric boundaries pose substantial challenges for automated extraction from high-resolution remote sensing imagery. Existing CNN- and Transformer-based methods still face difficulties in simultaneously preserving fine spatial details and modeling long-range contextual information. To address these limitations, this study proposes TerraceNet, a hybrid CNN–Transformer architecture with an encoder–dual-decoder design. The framework employs a ConvNeXt encoder to extract multi-scale features, which are subsequently aggregated by a dynamic fusion feature pyramid network (DF-FPN) and fed into two parallel decoders: a boundary decoder dedicated to fine-grained edge localization and a multi-scale Transformer decoder that incorporates boundary priors for global context modeling and final segmentation. Experimental results on a 2 m resolution GF-1 satellite imagery dataset from the Wuding River Basin in the Loess Plateau demonstrate that TerraceNet an IoU of 77.61% and an F1-Score of 87.39%. These results validate the effectiveness of the proposed architecture for extracting morphologically diverse terraces in complex terrain.