CdS exhibits excellent photochemical properties and high quantum efficiency in the visible light region, however, its catalytic stability is significantly compromised by photocorrosion. Constructing CdS/Mg?CdIn₂S₄ heterojunctions can effectively suppress photocorrosion and enhance the material's overall stability. In this study, CdS nanowires (CdS NWs), CdS nanoparticles (CdS NPs), and Mg?CdIn₂S₄ nanosheets (NSs) were prepared using the ion exchange method, and heterojunctions of 5% CdS NWs/Mg?CdIn?S? (5% by mass fraction of CdS NWs) and 5% CdS NPs/Mg?CdIn?S? (5% by mass fraction of CdS NPs) were constructed. The photocatalysts were characterized using X?ray diffraction (XRD), UV?vis diffuse reflectance spectroscopy (DRS), Fourier?transform infrared (FT?IR) spectroscopy, N₂ adsorption?desorption isothermal analysis, transient photocurrent measurements, and electrochemical impedance spectroscopy. The results confirmed the successful construction of both CdS NWs/Mg?CdIn₂S₄ and CdS NPs/Mg?CdIn₂S₄ heterojunctions. The catalytic performance was evaluated in a photoreaction system, both heterojunctions possess significant capabilities for the photocatalytic reduction of CO₂. Notably, the CdS NWs/Mg?CdIn₂S₄ heterojunction exhibited superior photocatalytic performance, achieving CO and H₂ production rates of 716.7 μmol/（g·h) and 664.3 μmol/（g·h), respectively. These values represent a 46.2?fold and 56.8?fold enhancement compared to pristine Mg?CdIn?S?.Consequently, this work provides a solid foundation for further research and practical applications in the field of photocatalytic CO₂ reduction, underscoring significant academic and practical significance.

The combination of remote sensing image information extraction and artificial intelligence algorithms is an important technical tool for land use status survey, monitoring and management in land resources and environmental departments.Aiming at the problems of insufficient spatial information localization and inaccurate multi-scale target feature segmentation generated by U-net in remote sensing image extraction, a CA-Res2-Unet model incorporating an attention module into the head of Res2Net to replace the coding part of U-net is proposed, which aims to enhance the spatial localization and multi-scale feature information segmentation capability of U-net.Experiments were carried out on mainstream networks and improved models through the WHDLD public data set and the self-made data set of Shenfu New District. The results show that compared with the basic model, OA, M_IoU and F1 indexes of the experiment on the WHDLD public data set and the self-made data set of Shenfu New District increased by 0.92%, 2.00%, 1.58% and 1.18%, 2.87%, 1.91%, respectively. The visual effect and quantitative indexes of the proposed method are superior to other mainstream semantic segmentation networks, which can provide scientific basis for the investigation of the status quo of regional land use and the decision-making of relevant departments.