Depicting the spatiotemporal dynamics of vegetation cover in the Yellow River Basin (YRB) and delineating the influences of climate change and human activities on the dynamics have been of significant importance for understanding the surface earth systems in general and also for formulating ecological protection plans of the YRB in particular. This study uses the GIMMS NDVI dataset from 1982 to 2015 and the MOD13A1 NDVI dataset from 2000 to 2021 to explore the spatial and temporal characteristics of vegetation cover in the YRB for the period from 1982 to 2021 with an attempt to reveal the influencing factors. The spatial distribution and temporal variation characteristics of vegetation cover are analyzed by maximum value composite, Theil-Sen median trend analysis, and Mann–Kendall test. Combined with the mean annual temperature and annual precipitation in the same period, influencing factors of vegetation cover in the YRB are discussed by using binary linear regression analysis and residual analysis. Results show that: (1) the multi-year average NDVI values increase from the northwest to the southeast and that the annual mean values of the vegetation covers fluctuate relatively greatly along an increasing trend with a growth rate of 0.019/(10a). Understandably, the monthly mean NDVI values show a single-peak distribution pattern, with August being the peak time (0.4936). (2) 77.35% of the studied areas are characterized by exhibiting an increasing trend of vegetation cover during the study period (i.e., 1982–2021). (3) Vegetation cover of the YRB is affected by the combined effects of climate change and human activities, with human activities being more significant in the observed amelioration of vegetation cover.

Digital watermarking is important for the copyright protection of electronic data, but embedding watermarks into vector maps could easily lead to changes in map precision. Zero-watermarking, a method that does not embed watermarks into maps, could avoid altering vector maps but often lack of robustness. This study proposes a dual zero-watermarking scheme that improves watermark robustness for two-dimensional (2D) vector maps. The proposed scheme first extracts the feature vertices and non-feature vertices of the vector map with the Douglas-Peucker algorithm and subsequently constructs the Delaunay Triangulation Mesh (DTM) to form a topological feature sequence of feature vertices as well as the Singular Value Decomposition (SVD) matrix to form intrinsic feature sequence of non-feature vertices. Next, zero-watermarks are obtained by executing exclusive disjunction (XOR) with the encrypted watermark image under the Arnold scramble algorithm. The experimental results show that the scheme that synthesizes both the feature and non-feature information improves the watermark capacity. Making use of complementary information between feature and non-feature vertices considerably improves the overall robustness of the watermarking scheme. The proposed dual zero-watermarking scheme combines the advantages of individual watermarking schemes and is robust against such attacks as geometric attacks, vertex attacks and object attacks.

Large-scale building extraction is an essential work in the field of remote sensing image analysis. The high-resolution image extraction methods based on deep learning have achieved state-of-the-art performance. However, most of the previous work has focused on region accuracy rather than boundary quality. Aiming at the low accuracy problems and incomplete boundary of the building extraction method, we propose an architecture that optimizes the boundaries of buildings, BAPANet. Notably, the architecture consists of an encoder-decoder network and residual refinement modules responsible for prediction and refinement, respectively. The objective function optimizes the network in the form of three levels (pixel, feature map, and patch) by fusing three loss functions: binary cross-entropy (BCE), intersection over-union (IoU) and structural similarity (SSIM). The five public datasets' experimental results show that the extraction method in this paper has high region accuracy, and the boundary of buildings is clear and complete.

Digital watermarking is an effective method of vector map copyright protection. However, the topology and geometric features are potentially influenced by the disturbances of vertices caused by watermark embedding, thus reducing the availability of watermarked vector maps. In this research, we propose a post-correction method for two-dimensional vector map watermarking, by which the topology and geometric features of the input data can be preserved, while using only conventional watermarking techniques. In the proposed method, the Maximum Perturbation Regions (MPR) of vertices and direction angle constraints methods of adjacent lines are adopted to undergo multi-azimuth checks of watermarked vertices, which may cause changes of the geometric features and topology. After that, the coordinate adjustment method which is based on the homonymous vertices topology association and the MPR are combined for topology and geometric feature correction. For watermark embedding, two classic frequency domain watermarking techniques, Discrete Fourier Transformation (DFT) and Discrete Wavelet Transformation (DWT), phase based and low-frequency coefficient based respectively, are also adopted. The scheme was conducted on a building vector map and a road vector map. The experimental results show that the proposed method can ensure the topology consistency and geometric feature similarity of vector maps before and after embedding watermarks. Moreover, this method has little interference with the maps, which improves the usability of the watermarked vector map.