Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.

Accurately detecting landslides over a large area with complex background objects is a challenging task. Research in the area suffers from three drawbacks in general. First, the models are mostly modified from typical networks, and are not designed specifically for landslide detection. Second, the images used to construct and evaluate models of landslide detection are limited to one spatial resolution, which struggles to meet the requirements of such relevant applications as emergency response. Third, assessments are primarily carried out by using the training data on different parts of the same study area. This makes it difficult to objectively evaluate the transferability of the model, because ground objects in the same area are distributed with similar spectral characteristics. To respond to the challenges above, this study proposes DeenNet, specifically designed for landslide detection. Different from the widely used encoder–decoder networks, DeenNet maintains multi-scale landslide features by decoding the input feature maps to a large scale before encoding a module. The decoding operation is conducted by deconvolution of the input feature maps, while encoding is conducted by convolution. Our model is trained on two earthquake-triggered landslide datasets, constructed using images with different spatial resolutions from different sensor platforms. Two other landslide datasets of different study areas with different spatial resolutions were used to evaluate the trained model. The experimental results demonstrated an at least 6.17% F1-measure improvement by DeenNet compared with three widely used typical encoder–decoder-based networks. The decoder–encoder network structure of DeenNet proves to be effective in maintaining landslide features, regardless of the size of the landslides in different evaluation images. It further validated the capacity of DeenNet in maintaining landslide features, which provides a strong applicability in the context of applications.

Ecological zoning and green–development assessment at the village–town scale in China are significant tasks for sustainable planning in China. In this study, we build an index system to calculate the eco–environmental vulnerability score and divide the results into extreme, heavy, moderate, light, and slight levels based on evidence from 43,046 villages and towns in China from 2000 to 2015; then, we build a sustainable–development score calculation criterion to perform sustainability assessments. The results show that nine indexes (digital elevation model (DEM), slope, net primary productivity (NPP), total rainfall per year, per capita cultivated land, farmland proportion, grassland proportion, forestland proportion, and construction–land proportion) are the main factors in the variation in eco–environmental vulnerability under the conditions of urbanization. The eco–environment is found to have worsened from 2000 to 2015, and the deterioration areas are mainly concentrated in Tibet, the eastern area of Xinjiang and the Xing’an Mountains region. Economic growth and ecological protection can achieve common development when eco–environmental vulnerability is at light and slight levels, while when eco–environmental vulnerability is fragile, the inhibitory effect of economic growth is obvious in rural areas. The results can provide useful information for village–town planning.

The volume of remote sensing images continues to grow as image sources become more diversified and with increasing spatial and spectral resolution. The handling of such large-volume datasets, which exceed available CPU memory, in a timely and efficient manner is becoming a challenge for single machines. The distributed cluster provides an effective solution with strong calculation power. There has been an increasing number of big data technologies that have been adopted to deal with large images using mature parallel technology. However, since most commercial big data platforms are not specifically developed for the remote sensing field, two main issues exist in processing large images with big data platforms using a distributed cluster. On the one hand, the quantities and categories of official algorithms used to process remote sensing images in big data platforms are limited compared to large amounts of sequential algorithms. On the other hand, the sequential algorithms employed directly to process large images in parallel over a distributed cluster may lead to incomplete objects in the tile edges and the generation of large communication volumes at the shuffle stage. It is, therefore, necessary to explore the distributed strategy and adapt the sequential algorithms over the distributed cluster. In this research, we employed two seed-based image segmentation algorithms to construct a distributed strategy based on the Spark platform. The proposed strategy focuses on modifying the incomplete objects by processing border areas and reducing the communication volume to a reasonable size by limiting the auxiliary bands and the buffer size to a small range during the shuffle stage. We calculated the F-measure and execution time to evaluate the accuracy and execution efficiency. The statistical data reveal that both segmentation algorithms maintained high accuracy, as achieved in the reference image segmented in the sequential way. Moreover, generally the strategy took less execution time compared to significantly larger auxiliary bands and buffer sizes. The proposed strategy can modify incomplete objects, with execution time being twice as fast as the strategies that do not employ communication volume reduction in the distributed cluster.

Monitoring and assessing urban flood disasters is the key to reducing the damage of this hazard. The urban surveillance video, with the advantages of flexibility and low cost, has been used as an effective real-time data source to monitor urban flooding. The paper presents an inundated area extraction method based on raindrop photometric model. The proposed method operates on the video and divides the task into two steps: (1) extracting water surface, followed by (2) refining inundated areas. At the first step in the process, the water covered areas are extracted from the variation of video in time series. This procedure was an improved version of the raindrop photometric model. Constrained information, especially road ranges, was obtained from video background image which has eliminated interference factors. Then inundated areas can be refined with the constraint information. Experiments performed on different locations show that the proposed method can provide more reliable results than the traditional method based on spectral features.

Superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories—even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implemented faster relative to the repeat calculation strategy, especially for limited calculation resources. Therefore, the proposed strategy is more suitable for superpixel segmentation algorithms. In addition, this research accumulates experience for expanding the abundant sequential algorithms to the distributed environment and provides more solutions for large-scale image processing demands.