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Extracting roads from high spatial resolution imagery (HSRI) has been a hot research topic in recent years. Particularly, the fully convolutional network (FCN)-based methods have shown promising performance in accurately extracting roads from HSRI. However, most existing FCN-based approaches suffer from such deficiencies of convolution in spatial detail loss, inadequate fusion of multi-scale features, and lack of consideration for long-range dependencies, making road extraction from HSRI remain a challenging task. To address the above challenges, based on LinkNet architecture, this paper provided a novel neural network named RFE-LinkNet, which employs a U-shaped framework and integrates several receptive field enhancement modules and dual attention modules. In the RFE-LinkNet, in order to enhance the spatial information perception and capture long-range dependencies, the multiple receptive field enhancement module is devised to expand the receptive field while preserving the spatial details of feature maps. And dual attention module is provided to capture accurate features for road extraction by refining multi-scale features from the different-level feature maps in the view of their relative importance. Experiments on Massachusetts road dataset and DeepGlobe road dataset are conducted to evaluate the performance of RFE-LinkNet, respectively. Experimental results show that the proposed method achieves superior performance compared to previous road extraction, establishing its state-of-the-art effectiveness. The code of RFE-LinkNet is available at https://github.com/zhengxc97/RFE_LINKNET.

We establish both the uniqueness and the existence of the solutions to a hidden-memory variable-order fractional stochastic partial differential equation, which models, e.g., the stochastic motion of a Brownian particle within a viscous liquid medium varied with fractal dimensions. We also investigate the inverse problem concerning the observations of the solutions, which eliminates the analytic assumptions on the variable orders in the literature of this topic and theoretically guarantees the reliability of the determination and experimental inference.

Due to its high efficiency and low cost, automatic extraction of building footprints from remotely sensed imagery has long been an important means to obtain building footprint information, which can be easily implemented using existing fully convolutional network (FCN)-based methods. However, such methods suffer from imperfections and thus accurately extracting building footprints from remotely sensed imagery remains a challenging task. For example, cascaded convolutions generally cannot preserve the spatial details well, leading to blurred boundaries and omission of small buildings. Insufficient multiscale features fusion without considering semantic gaps between different level features could yield misclassification. In addition, the fixed receptive field always produces discontinuous holey extracted large buildings. To this end, we propose a novel multiscale and multipath network with boundary enhancement (MMB-Net) that accurately extracts building footprints from remotely sensed imagery. More specially, a parallel multipath feature extraction module is first designed to capture high spatial information-preserved multiscale features with less semantic distances. In addition, the receptive field is enlarged and broadened by a multiscale features enhancement module. Then, an attention-based multiscale features fusion module is built to appropriately aggregate multiscale features. Lastly, a spatial enhancement module is presented to refine the extracted building boundaries by capturing boundary information from low-level features. The proposed MMB-Net has been tested on two benchmark data sets together with other SOTA approaches. The results show that MMB-Net can achieve promising building footprints extraction performances and it outperforms the SOTA methods. The implementation of MMB-Net is available at.

Building extraction from high spatial resolution imagery (HSRI) plays an important role in the remotely sensed imagery application fields. However, automatically extracting buildings from HSRI is still a challenging task due to such factors as large size variations of buildings, background complexity, variations in appearance, etc. Especially, it is difficult to extract both crowded small buildings and large buildings with accurate boundaries. To address these challenges, this paper presents an end-to-end encoder–decoder model to automatically extract buildings from HSRI. The designed network, called AEUNet++, is based on UNet++, attention mechanism and multi-task learning. Specifically, the AEUNet++ introduces the UNet++ as the backbone to extract multiscale features. Then, the attention block is used to effectively fuse different-layer feature maps instead of direct concatenation in the output of traditional UNet++, which can assign adaptive weights to different-layer feature maps as their relative importance to enhance the sensitivity of the mode and suppress the background influence of irrelevant features. To further improve the boundary accuracy of the extracted buildings, the boundary geometric information of buildings is integrated into the proposed model by a multi-task loss using a proposed distance class map during training of the network, which simultaneously learns the extraction of buildings and boundaries and only outputs extracted buildings while testing. Two different data sets are utilized for evaluating the performance of AEUNet++. The experimental results indicate that AEUNet++ produces greater accuracy than U-Net and the original UNet++ architectures and, hence, provides an effective method for building extraction from HSRI.

A time-fractional substantial diffusion equation of variable order is investigated, in which the variable-order fractional substantial derivative accommodates the memory effects and the structure change of the surroundings of the physical processes with respect to time. The existence and uniqueness of the solutions to the proposed model are proved, based on which the weighted high-order regularity of the solutions, in which the weight function characterizes the singularity of the solutions, are analyzed.