Hybrid Feature Embedding For Automatic Building Outline Extraction

Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with this problem. We also designed a triple-branch decoder structure to handle different features generated by encoder. Experiment results show that our model outperforms other baseline model on two datasets, achieving 91.1% mIoU on Vaihingen and 83.8% on Bing huts

Multiconstraint Transformer-Based Automatic Building Extraction From High-Resolution Remote Sensing Images

Building extraction from very high-resolution remote sensing images is a fundamental task and is widely used in applications, such as change detection, disaster assessment, and real-time update of geographic information databases. However, due to the complexity of the geographical environment and the diversity of target features, accurate automatic building extraction remains very challenging. With the fast development of deep learning techniques, convolutional neural networks (CNN) have been widely used in remote sensing research and have achieved considerable results. But for large urban area-based building detection tasks, the CNN-based method usually gets into local optima and generates many false positive detections around building boundaries. To avoid the local optima and be aware of nonlocal information, this article proposes a hybrid feature extraction model based on the combination of the CNN and Transformer to realize the automatic building detection from very high-resolution remote sensing images. Meanwhile, a multiconstraint weighting mechanism is proposed to enhance the ability of the model to recognize the regular geometric boundaries of buildings. Comprehensive experiments are conducted on the three different datasets. The proposed MC-TRANSU achieves the best F1-score and intersection over union, compared with the state-of-the-art methods, such as SegNet, TransUnet, and Swin-Unet, and the detection accuracy improved around 5$\%$. Quantitative and qualitative results verify the superiority and effectiveness of our model

Concurrent Precipitation Extremes Modulate the Response of Rice Transplanting Date to Preseason Temperature Extremes in China

Abstract Understanding how crop phenology responds to climate change is critical for enabling agricultural adaptation measures. Pre‐season temperature alone leads to well‐understood changes in crop phenology. Nevertheless, the modulation effect of concurrent precipitation extremes on the response to temperature extremes has been largely under‐addressed. Here, we investigate the response of rice transplanting dates to pre‐season temperature extremes and reveal the modulation effects of concurrent precipitation extremes by using station‐observed rice transplanting dates from 1981 to 2018 across mainland China. We also evaluate the performance of a remotely sensed phenology product, ChinaCropPhen1km, in reproducing the above temperature responses and modulation effects. Our results showed that transplanting dates tended to advance in response to an extremely hot pre‐season, while concurrent extreme drought offset the advance by up to 2.6 days. Transplanting dates tended to be delayed in response to an extremely cold pre‐season, while concurrent extreme precipitation exacerbated the delay by up to 1 day. Responses to temperature extremes and modulation effects were divergent across different regions. Under extremely hot conditions, transplanting dates advanced further in hotter and wetter regions, while under extremely cold pre‐seasons, transplanting dates delayed less in colder and drier regions. Transplanting dates from the ChinaCropPhen1km product underestimated the responses to temperature extremes by up to 4.7 days and detected the opposite modulation effect compared to those obtained from observations. Our results highlight that the need to improve our understanding and modeling of modulation effects of precipitation extremes on temperature–phenology relationship, which benefits the field of agriculture risk analysis and climate change adaptation