Many cities worldwide have large amounts of industrial vacant land (IVL) due to development and transformation, posing a growing problem. However, the large-scale identification of IVL is hindered by obstacles such as high cost, high variability, and closed-source data. Moreover, it is difficult to distinguish industrial vacant land from operational industrial land based solely upon image features. To address these issues, we propose a method for the large-scale automatic identification of IVL. The framework uses deep learning to train remote-sensing images of potential industrial vacant land to generate a semantic segmentation model and further use population density and surface temperature data to filter model predictions. The feasibility of the proposed methodology was validated through a case study in Tangshan City, Hebei Province, China. The study indicates two major conclusions: (1) The proposed IVL identification framework can efficiently generate industrial vacant land mapping. (2) HRNet exhibits the highest accuracy and strongest robustness after training compared with other semantic segmentation backbone networks, ensuring high-quality performance and stability, as evidenced by a model accuracy of 97.84%. Based on the above advantages, the identification framework provides a reference method for various countries and regions to identify industrial vacant land on a large scale, which is of great significance for advancing the research and transformation of industrial vacant land worldwide.

Spatially uneven urbanization shapes various urban-lake spatial patterns; however, the effect of pattern evolution on lake breeze front (LBF) penetration via thermal and aerodynamic mechanisms in inland multi-lake megacities remains unclear. Therefore, sensitivity experiments were conducted to examine LBF changes over the past 40 years in Wuhan, China—where lakes are located on the periphery of built-up areas or integrated with urban fabrics—using the Weather Research and Forecasting (WRF) model under high-temperature and weak-wind conditions. Moreover, we quantified the contributions of thermal (lake-land surface temperature differences (LSTD), urban heat island intensity (UHII)), and aerodynamic factors (lake-land surface roughness differences (LSRD)) to LBF penetration. The results showed that for lakes entirely within urban fabrics, the thermal and roughness characteristics at lake-land interfaces dominated LBF penetration. Specifically, urban expansion towards lakeshores without connections promoted LBF penetration due to the stronger positive benefits of the LSTD. However, urban expansion bordering lakeshores inhibited LBF penetration, as the inhibitory effects of LSRD outweighed those of LSTD. When lakes remained on the periphery of built-up areas, higher UHII and the UHII-weighted center moving towards suburban lakes accelerated the LBF movement into built-up areas. Based on these findings, we propose adaptive strategies for urban growth boundaries to facilitate the natural infiltration of LBFs into urban environments.