scholarly journals Ground and satellite monitoring of atmospheric pollution processes in urban areas

2021 ◽  
Author(s):  
R.A. Amikishieva ◽  
V.F. Raputa ◽  
A.A. Lezhenin
Keyword(s):  
High Resolution ◽  
Atmospheric Pollution ◽  
Urban Areas ◽  
Impurity Concentration ◽  
Satellite Images ◽  
Satellite Observations ◽  
Satellite Monitoring ◽  
Cement Plant ◽  
High Resolution Satellite Images ◽  
Statistical Relationships

The results of the analysis of atmospheric pollution processes in the vicinity of the Chernorechensky cement plant and the Iskitim city were presented. Snow cover samples and high-resolution satellite images were used as research materials. The reconstruction of the fields of impurity concentration was carried out on the basis of low-parameter models. Statistical relationships were identified between ground-based and satellite observations.

scholarly journals Urban areas analysis using satellite image segmentation and deep neural network

2019 ◽  
Vol 135 ◽  
pp. 01064
Author(s):  
Vladimir Khryaschev ◽  
Leonid Ivanovsky
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Urban Areas ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Images ◽  
Building Detection ◽  
High Resolution Satellite Images

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

scholarly journals Large Scale Palm Tree Detection In High Resolution Satellite Images Using U-Net

2019 ◽  
Vol 11 (3) ◽  
pp. 312 ◽  
Author(s):  
Maximilian Freudenberg ◽  
Nils Nölke ◽  
Alejandro Agostini ◽  
Kira Urban ◽  
Florentin Wörgötter ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Large Scale ◽  
Satellite Images ◽  
Image Resolution ◽  
Metropolitan Region ◽  
Palm Tree ◽  
Monitoring Methods ◽  
Maximum Throughput ◽  
High Resolution Satellite Images

Oil and coconut palm trees are important crops in many tropical countries, which are either planted as plantations or scattered in the landscape. Monitoring in terms of counting provides useful information for various stakeholders. Most of the existing monitoring methods are based on spectral profiles or simple neural networks and either fall short in terms of accuracy or speed. We use a neural network of the U-Net type in order to detect oil and coconut palms on very high resolution satellite images. The method is applied to two different study areas: (1) large monoculture oil palm plantations in Jambi, Indonesia, and (2) coconut palms in the Bengaluru Metropolitan Region in India. The results show that the proposed method reaches a performance comparable to state of the art approaches, while being about one order of magnitude faster. We reach a maximum throughput of 235 ha/s with a spatial image resolution of 40 cm. The proposed method proves to be reliable even under difficult conditions, such as shadows or urban areas, and can easily be transferred from one region to another. The method detected palms with accuracies between 89% and 92%.

scholarly journals GROUND AND SATELLITE MONITORING OF POLLUTION PROCESSES ISKITIM-LINEVSK INDUSTRIAL ZONE

2021 ◽  
Vol 4 (1) ◽  
pp. 60-65
Author(s):  
Ruslana A. Amikishieva ◽  
Vladimir F. Raputa ◽  
Irina A. Solov ‘eva
Keyword(s):  
Atmospheric Pollution ◽  
Comprehensive Analysis ◽  
Satellite Observations ◽  
Satellite Monitoring ◽  
Research Material ◽  
Melted Snow ◽  
General Dynamics ◽  
High Resolution Images ◽  
Statistical Relationships ◽  
Parameter Reconstruction

The results of a numerical analysis of atmospheric pollution in the vicinity of the industrial site of the Chernorechensky cement plant (CCP) and the territory of Iskitim are presented. The research material was the results of sampling melted snow for 2019-20. The snow index (NDSI), calculated from high-resolution images from the Landsat and Sentinel satellites, was used as satellite data. Statistical relationships between ground-based and satellite observations are given. The general dynamics of changes in the impurity concentration in the snow and NDSI values are revealed. The concentration is calculated on the basis of low-parameter reconstruction models using ground-based measurements. For calculations and visualization, the means of the geographic information system, which was developed earlier, were used. These studies represent the basis for the development of a methodology for a comprehensive analysis of the process of atmospheric pollution using ground-based and satellite observations.

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