A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery

2019 ◽  
Vol 41 (6) ◽  
pp. 2239-2262 ◽  
Author(s):  
Moein Zalpour ◽  
Gholamreza Akbarizadeh ◽  
Navid Alaei-Sheini
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
False Alarm ◽  
False Alarm Rate ◽  
Satellite Imagery ◽  
New Approach ◽  
Deep Learning Features ◽  
Oil Tank ◽  
High Resolution Satellite Imagery

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

Self-Supervised Deep Learning for Vehicle Detection in High-Resolution Satellite Imagery

2021 ◽  
Author(s):  
Zeyad Awwad ◽  
Faisal Alnasser ◽  
Tariq Alshahrani ◽  
Matthew Moraguez ◽  
Ahmad Alabdulkareem ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vehicle Detection ◽  
High Resolution Satellite Imagery

scholarly journals BUILDING SEGMENTATION IN AGRICULTURAL LAND USING HIGH RESOLUTION SATELLITE IMAGERY BASED ON DEEP LEARNING APPROACH

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 587-594
Author(s):  
L.-Y. Liu ◽  
C.-K. Wang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Arable Land ◽  
Field Investigation ◽  
Land Area ◽  
Training Images ◽  
Building Area ◽  
High Resolution Satellite Imagery

Abstract. Understanding building area in agricultural land is important since arable land area in Taiwan is limited. One of the practical ways is manual digitization on high resolution satellite imagery, which can avoid field investigation and achieve satisfying results. However, such practice is tedious and labor intensive. Past researches have shown that deep learning methods are useful to segment buildings in different cities using satellite imagery. In this study, ENVINet5 model was trained and used to segment buildings from high resolution Pleiades pansharpened imagery. The training images (with the size of 2500 pixels × 2500 pixels) were randomly selected from 9 counties/cities to increase diversity since each county/city has different building patterns. The performance of ENVINet5 model reached 0.977, 0.814, 0.847, and 0.829 respectively on accuracy, precision, recall, and F1 score. Since evaluation by pixels can be difficult to show geometry of buildings, we evaluated the model by counting the number of inference building segments, which was post-processed from inference result of ENVINet5 trained model. Further analysis by counting the inference building segments is discussed in this study.

scholarly journals A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Resolution Satellite Imagery

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meytar Sorek Hamer ◽  
Ata Akbari Asanjan ◽  
Michael Von Pohle ◽  
Adwait Sahasrabhojanee ◽  
Emily Deardorff ◽  
...  
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
High Resolution ◽  
Satellite Imagery ◽  
Urban Environments ◽  
Learning Approach ◽  
Quality Estimation ◽  
High Resolution Satellite Imagery ◽  
Very High

scholarly journals Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

2020 ◽  
Author(s):  
Isla Duporge ◽  
Olga Isupova ◽  
Steven Reece ◽  
David W. Macdonald ◽  
Tiejun Wang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Human Detection ◽  
Detection Accuracy ◽  
African Elephants ◽  
Heterogeneous Landscapes ◽  
Detection Capabilities ◽  
High Resolution Satellite Imagery ◽  
Very High

AbstractSatellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.

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