Afforestation and reforestation, both of which refer to forestation strategies, are widely promoted as key tools to mitigate anthropogenic warming. However, the carbon sequestration potential of these efforts remains uncertain in satellite-based assessments, particularly when accounting for dynamic climate conditions, vegetation-climate feedback, fire-dominated disturbance, and the trade-offs associated with surface albedo changes. Leveraging a coupled Earth system model, we estimated that global forestation mitigates 31.3 to 69.2 Pg C _eq (carbon equivalent) during 2021–2100 under a sustainable shared socioeconomic pathway. Regionally, the highest carbon mitigation potential of forestation concentrates in tropical areas, while mid-high-latitude regions demonstrate higher heterogeneity, highlighting the need for region-specific strategies and further refinement of nature-based mitigation plans. Our findings underscore the importance of considering disturbances and minimizing adverse albedo changes when estimating the carbon mitigation potential of forestation initiatives. We also advocate for the development of consistent, high-resolution maps of suitable areas for targeted forestation, avoiding environmentally sensitive lands and potential conflicts with other human activities.

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.

Flood mapping plays a crucial role in effective disaster management, risk assessment, and mitigation planning, given the widespread and destructive nature of floods. However, current synthetic aperture radar (SAR)-based methods face challenges related to extensive labeled training data, compromised classification accuracy, and limited applicability across different satellite systems and resolutions. In response to these challenges, our research introduces a pioneering unsupervised SAR-based flood mapping algorithm, inspired by artificial general intelligence principles. Notably, the innovative method demonstrates flexibility, performing effectively across various SAR satellites with differing resolutions and sensors, eliminating the need for satellite-specific algorithms. Our algorithm enhances processing speed and scalability by eliminating labor-intensive labeling of training data and manual intervention. To validate its performance, we conducted tests in three distinct regions using meter-level imagery from HISEA-1, Gaofen-3, and Sentinel-1 satellites. Consistently outperformed prevalent unsupervised techniques like Kmeans and Otsu, and even a Supervised-convolutional neural network segmentation algorithm by AI-Earth, with F1-scores exceeding 0.91. This outstanding performance showcases its accuracy, irrespective of the satellite systems or regions utilized. Furthermore, the seamless integration of our algorithm with high-performance cloud computing platforms such as Google Earth Engine enhances its adaptability and scalability, enabling continuous monitoring of global floods. This is crucial in understanding flood trends, assessing their impacts, and formulating effective disaster mitigation strategies.

The application of 3D Network on Chip (NoC) provides an effective way for tackling the performance bottleneck for high-performance Systems on Chips (SoCs). How to design an efficient 3D Network on Chip which is satisfied with the communication requirement of 3D system and simultaneously enables significant performance enhancements has encouraged a lot of attention. In this paper, we focus on the automatic design for custom based NoC architecture by use of a novel approach. The synthesis idea is proposed to develop a minimum cost topology and an optimized floorplan to decrease the power consumption, under the hardware and software constraints. Different algorithms are used to solve the sub-problems. In the core to switch connectivity stage, we firstly use Tarjan Algorithm to find the strong connectivity part in the core communication graph, and then use the Min-cut Algorithm to partition the core communication graph into sub-graphs. To establish the switch to switch connection, we apply Genetic Algorithm (GA) to do the path computation and flow control. Finally, we use Genetic Algorithm to solve the switch position problem. Optimized positions of switches in the floorplan for minimizing the power consumption are obtained while meeting the non-overlapping constraints. The experimental results show that our proposed synthesis approach is efficient and much power saving in the application of NoC design work.