Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset of tailings ponds available for relevant research. This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing images. Taking Tongling city as the study area, the proposed model can detect tailings pond locations with high accuracy and efficiency from a large HSR remote sensing image (precision = 99.6%, recall = 89.9%, mean average precision = 89.7%). An optimal random forest model and morphological processing were utilized to further extract accurate tailings pond margins from the target areas. The final map of the entire study area was obtained with high accuracy. Compared with the random forest algorithm, the total extraction time was reduced by nearly 99%. This study can be beneficial to mine monitoring and ecological environmental governance.

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Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Remote Sensing ◽

10.3390/rs14010103 ◽

2021 ◽

Vol 14 (1) ◽

pp. 103

Author(s):

Dongchuan Yan ◽

Hao Zhang ◽

Guoqing Li ◽

Xiangqiang Li ◽

Hua Lei ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Transfer Learning ◽

Near Infrared ◽

Image Data ◽

Remote Sensing Images ◽

Tailings Pond ◽

True Color ◽

Tailings Ponds

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

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An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13112052 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2052

Author(s):

Dongchuan Yan ◽

Guoqing Li ◽

Xiangqiang Li ◽

Hao Zhang ◽

Hua Lei ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

High Precision ◽

Large Scale ◽

Dam Failure ◽

Remote Sensing Images ◽

Feature Maps ◽

Tailings Pond ◽

Tailings Ponds

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

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Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set Evolutions

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2014.2330341 ◽

2015 ◽

Vol 53 (2) ◽

pp. 883-899 ◽

Cited By ~ 11

Author(s):

Zhongbin Li ◽

Wenzhong Shi ◽

Qunming Wang ◽

Zelang Miao

Keyword(s):

Remote Sensing ◽

Level Set ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Images

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A robust mosaicking procedure for high spatial resolution remote sensing images

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2015.09.009 ◽

2015 ◽

Vol 109 ◽

pp. 108-125 ◽

Cited By ~ 36

Author(s):

Xinghua Li ◽

Nian Hui ◽

Huanfeng Shen ◽

Yunjie Fu ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Images

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Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

Remote Sensing ◽

10.3390/rs10111737 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1737 ◽

Cited By ~ 19

Author(s):

Jinchao Song ◽

Tao Lin ◽

Xinhu Li ◽

Alexander V. Prishchepov

Keyword(s):

Remote Sensing ◽

Land Use ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Images ◽

Remote Sensing Imagery ◽

Points Of Interest ◽

Very High Spatial Resolution ◽

Functional Zones ◽

Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

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