Expediting Building Footprint Segmentation from High-resolution Remote Sensing Images via progressive lenient supervision

The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness. Many existing building segmentation methods were developed upon the encoder-decoder architecture of U-Net, in which the encoder is finetuned from the newly developed backbone networks that are pre-trained on ImageNet. However, the heavy computational burden of the existing decoder designs hampers the successful transfer of these modern encoder networks to remote sensing tasks. Even the widely-adopted deep supervision strategy fails to mitigate these challenges due to its invalid loss in hybrid regions where foreground and background pixels are intermixed. In this paper, we conduct a comprehensive evaluation of existing decoder network designs for building footprint segmentation and propose an efficient framework denoted as BFSeg to enhance learning efficiency and effectiveness. Specifically, a densely-connected coarse-to-fine feature fusion decoder network that facilitates easy and fast feature fusion across scales is proposed. Moreover, considering the invalidity of hybrid regions in the down-sampled ground truth during the deep supervision process, we present a lenient deep supervision and distillation strategy that enables the network to learn proper knowledge from deep supervision. Building upon these advancements, we have developed a new family of building segmentation networks, which consistently surpass prior works with outstanding performance and efficiency across a wide range of newly developed encoder networks. The code will be released on https://github.com/HaonanGuo/BFSeg-Efficient-Building-Footprint-Segmentation-Framework.Comment: 13 pages,8 figures. Submitted to IEEE Transactions on Neural Networks and Learning System

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches.Comment: 12 pages,7 figures, submitted to IEEE Transactions on Image Processin

Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction

Buildings are the basic carrier of social production and human life; roads are the links that interconnect social networks. Building and road information has important application value in the frontier fields of regional coordinated development, disaster prevention, auto-driving, etc. Mapping buildings and roads from very high-resolution (VHR) remote sensing images have become a hot research topic. However, the existing methods often ignore the strong spatial correlation between roads and buildings and extract them in isolation. To fully utilize the complementary advantages between buildings and roads, we propose a building-road collaborative extraction method based on multi-task and cross-scale feature interaction to improve the accuracy of both tasks in a complementary way. A multi-task interaction module is proposed to interact information across tasks and preserve the unique information of each task, which tackle the seesaw phenomenon in multitask learning. By considering the variation in appearance and structure between buildings and roads, a cross-scale interaction module is designed to automatically learn the optimal reception field for different tasks. Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks. Experiments on a wide range of urban and rural scenarios show that the proposed algorithm can achieve building-road extraction with outstanding performance and efficiency.Comment: 34 pages,9 figures, submitted to ISPRS Journal of Photogrammetry and Remote Sensin

Impacts of Land Use and Salinization on Soil Inorganic and Organic Carbon in the Middle-lower Yellow River Delta

ACKNOWLEDGEMENTS This study was financially supported by the National Natural Science Foundation of China (Nos. 41877028 and 41205104). This work also contributes to the activities of N-Circle projects, a UK-China Virtual Joint Centre on Nitrogen, funded by the Newton Fund via Biotechnology and Biological Sciences Research Council (BBSRC) (No. BB/N013484/1Peer reviewedPostprin

The impact of filaments on dwarf galaxy properties in the Auriga simulations

With a hydrodynamical simulation using a simple galaxy formation model without taking into account feedback, our previous work has shown that dense and massive filaments at high redshift can provide potential wells to trap and compress gas, and hence affect galaxy formation in their resident low-mass haloes. In this paper, we make use of the Auriga simulations, a suite of high-resolution zoom-in hydrodynamical simulations of Milky Way-like galaxies, to study whether the conclusion still holds in the simulations with a sophisticated galaxy formation model. In agreement with the results of our previous work, we find that, compared to their counterparts with similar halo masses in the field, dwarf galaxies residing in filaments tend to have higher baryonic and stellar fractions. At the fixed parent halo mass, the filament dwarfs tend to have slightly higher star formation rates than those of field ones. But overall we do not find a clear difference in galaxy g - r colours between the filament and field populations. We also show that at high redshifts, the gas components in dwarf galaxies tend to have their spins aligned with the filaments in which they reside. Our results support a picture in which massive filaments at high redshift assist gas accretion and enhance star formation in their resident dwarf-sized dark matter haloes.Peer reviewe

Preparation and Performance Optimization of Two-Component Waterborne Polyurethane Locomotive Coating

This paper reports the effects of different formulas on the performance of waterborne polyurethane (WPU), including two-component WPU and curing agent, wetting dispersant, defoaming agent, and wetting agent. The optimization of rheological additives selection, through the optimization of coating physical properties and chemical properties, can make the film show uniform color and appearance without pinholes, bubbles, or wrinkles, and have a long probation period. Through the analysis of performance after a 1000-h quick ultraviolet (QUV) aging test, the light reduction rate is 23.19%, and the color difference is 1.9. As can be seen from the scanning electron microscope (SEM) image and the three-dimensional stereomicroscope, the film shows relatively uniform dispersion, good compactness, and smooth surface. The two-component WPU topcoat is found to have high gloss 87.1 (60°) and high weather resistance, which provides a positive indication for the modulation and production of waterborne locomotive paint