Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks.

Data from the U.S. Defense Meteorological Satellite Program’s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable—Large-scale Impervious Surface Index (LISI)—is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area.

Road extraction is an important task in remote sensing image information extraction. Recently, deep learning semantic segmentation has become an important method of road extraction. Due to the impact of the loss of multiscale spatial features, the results of road extraction still contain incomplete or fractured results. In this paper, we proposed a deep learning model, which is called the dense-global-residual network, that reduces the loss of spatial information and enhances context awareness. In the dense-global-residual network, the residual network is used to extract the features at different levels. To obtain more abundant multiscale features, a dense and global spatial pyramid pooling module based on Atrous Spatial Pyramid Pooling is built to perceive and aggregate the contextual information. The proposed method obtains better results on the GF-2 road dataset and public Massachusetts road dataset of aerial imagery. In order to prove the effectiveness of our method, we compared with four methods, such as DeepLabV3+, U-net, D-LinkNet and coord-dense-global model, the accuracy of our method is considerably better. Moreover, the dense-global-residual network can also effectively extract roads, especially trees and building shadows occlude the road. In addition, our method can successfully extract roads in regions of different development levels in universality experiments. This indicates that the proposed method can effectively maintain the completeness and continuity of roads and improve the accuracy of road segmentation from high-resolution remote sensing images.

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.